# Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to
Industrial Applications

Cong T. Nguyen, Yuris Mulya Saputra, Nguyen Van Huynh, Tan N. Nguyen, Dinh
Thai Hoang, Diep N Nguyen, Van-Quan Pham, Miroslav Voznak, Symeon Chatzinotas,
and Dinh-Hieu Tran
The research leading to the published results was supported by the European
Union within the REFRESH project - Research Excellence For Region
Sustainability and High-tech Industries ID No.
CZ$.10.03.01/00/22\\_003/0000048$ of the European Just Transition Fund and by
the Ministry of Education, Youth and Sports of the Czech Republic (MEYS CZ)
through the project SGS ID No. SP 7/2023 conducted by VSB - Technical
University of Ostrava.Cong T. Nguyen is with the Institute of Fundamental and
Applied Sciences, Duy Tan University, Ho Chi Minh City 700000, Vietnam, and
also with the Faculty of Information Technology, Duy Tan University, Da Nang
550000, Vietnam (e-mail:nguyenthanhcong21@dtu.edu.vn)Yuris Mulya Saputra is
with the Department of Electrical Engineering and Informatics, Vocational
Collge, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia (e-mail:
ym.saputra@ugm.ac.id)Nguyen Van Huynh is with the Department of Electrical
Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ,
United Kingdom (e-mail: huynh.nguyen@liverpool.ac.uk)Tan N. Nguyen
(corresponding author) is with the Communication and Signal Processing
Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc
Thang University, Ho Chi Minh City, Vietnam.
(email:nguyennhattan@tdtu.edu.vn)Dinh Thai Hoang and Diep N. Nguyen are with
the School of Electrical and Data Engineering, University of Technology
Sydney, Australia (e-mail<EMAIL_ADDRESS>diep.nguyen@uts.edu.au)Van-
Quan Pham is with Nokia Bell Labs, Murray Hill, NJ 07974, USA (email:
quan.pham_van@nokia-bell-labs.com)Miroslav Voznak is with the VSB - Technical
University of Ostrava, 70800 Ostrava, Czech Republic (e-mail:
miroslav.voznak@vsb.cz)Symeon Chatzinotas with the Interdisciplinary Centre
for Security, Reliability and Trust (SnT), University of Luxembourg, 4365
Esch-sur-Alzette, Luxembourg (e-mail: symeon.chatzinotas@uni.lu)Dinh-Hieu Tran
is currently with Nokia, France<EMAIL_ADDRESS>
###### Abstract

Terrestrial networks form the fundamental infrastructure of modern
communication systems, serving more than 4 billion users globally. However,
terrestrial networks are facing a wide range of challenges, from coverage and
reliability to interference and congestion. As the demands of the 6G era are
expected to be much higher, it is crucial to address these challenges to
ensure a robust and efficient communication infrastructure for the future. To
address these problems, Non-terrestrial Network (NTN) has emerged to be a
promising solution. NTNs are communication networks that leverage airborne
(e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to
facilitate ultra-reliable communications and connectivity with high data rates
and low latency over expansive regions. This article aims to provide a
comprehensive survey on the utilization of network slicing, Artificial
Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to
address diverse challenges of NTNs from the perspectives of both academia and
industry. Particularly, we first provide an in-depth tutorial on NTN and the
key enabling technologies including network slicing, AI/ML, and ORAN. Then, we
provide a comprehensive survey on how network slicing and AI/ML have been
leveraged to overcome the challenges that NTNs are facing. Moreover, we
present how ORAN can be utilized for NTNs. Finally, we highlight important
challenges, open issues, and future research directions of NTN in the 6G era.

###### Index Terms:

NTN, network slicing, AI/ML, ORAN, and 6G.

##  I Introduction

Terrestrial networks, encompassing land-based infrastructures such as fiber
optics, coaxial cables, and wireless transmission, are the backbone of modern
communication systems, enabling the seamless exchange of information in our
interconnected world. However, with the ever-increase of demands, e.g., nearly
5 billion Internet users worldwide [1], terrestrial networks are facing a wide
range of challenges, from coverage and reliability to interference and
congestion. Particularly, as reported by Ericsson, only 10% of the global
population has access to mobile broadband services [2]. The main reason for
this is due to the difficulties in establishing infrastructure in underserved
regions, such as rural areas, islands, and isolated communities. Moreover, the
reliability and resilience of terrestrial networks are required to be
improved, especially in case of natural disasters, accidents, or attacks [3].
Furthermore, current terrestrial networks are not suitable for new use cases
and services that require high bandwidth, low latency, or global coverage [4].

Non-terrestrial Network (NTN), encompassing satellites, Unmanned Aerial
Vehicles (UAVs), and High-Altitude Platform Stations (HAPS) networks, is a
promising solution to address the challenges that terrestrial networks are
facing. For instance, through the utilization of satellites and HAPS, NTN can
effectively overcome coverage gaps in terrestrial networks, facilitating
broadband connectivity across expansive distances. NTN can also play a vital
role in enhancing the resilience of terrestrial infrastructure during crises
by utilizing UAVs to provide emergency access [5]. Furthermore, by integrating
with terrestrial networks, NTN can unlock new possibilities for applications
requiring high bandwidth, low latency, and global coverage, including
autonomous driving, smart cities, Internet-of-Things (IoT), Augmented Reality,
Virtual Reality, cloud gaming, and video conferencing, in the 6G era [6, 7].

TABLE I: Comparison with existing surveys Ref. | Focus | Differences
---|---|---
[8] | Integration of New Radio in NTN | Not focus on network slicing, AI/ML, and ORAN
[4] | Role of NTN in 5G systems | Not focus on network slicing, AI/ML, and ORAN
[6] | Integration of NTN with different types of networks | Not focus on network slicing, AI/ML, and ORAN
[7] | Utilization of cellular, Wide-Area, and NTN for IoT | Not focus on NTN
[9] | Application of AI/ML in NTN | Not focus on network slicing and ORAN
[10] | Architecture, performance evaluation, and standardization of NTNs | Not focus on network slicing, AI/ML, and ORAN
[11] | Architecture, performance evaluation, and standardization of NTNs | Not focus on network slicing, AI/ML, and ORAN
Figure 1: Organization of this paper.

Despite its potential, the development of NTN is also facing serious
challenges. Particularly, NTN often faces significant propagation delay and
path loss due to high altitudes. This can significantly hinder the
effectiveness of NTN in time-sensitive applications. Moreover, the challenging
nature of channel estimation is exacerbated by the time-variant
characteristics of NTN. The high moving speed of satellites also causes severe
Doppler effects and serious challenges in mobility management. Additionally,
the complex integration of NTN and existing terrestrial network infrastructure
necessitates careful design and considerations to optimize.

To address these challenges, various solutions have been developed in recent
years, leveraging technologies such as network slicing, Artificial
Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN).
Particularly, with the powerful ability to create multiple virtual networks
from a shared physical network architecture, network slicing enables a single
NTN to serve multiple types of applications and users with different demands,
e.g., low-latency communication for UAV and high bandwidth for satellite
Internet services. Moreover, AI/ML can help NTNs to overcome critical
challenges of NTNs. Specifically, AI/ML techniques are very effective in
handling non-linear effects and general impairments of channels in NTNs. Those
techniques can also enable the autonomous operation of numerous wireless
applications, thereby reducing the need for constant human intervention.
Additionally, by utilizing the current diverse vendors ecosystem, ORAN can
help to significantly improve NTNs’ resilency, scalability, and flexibility.

This paper aims to provide an in-depth and comprehensive survey on the
utilization of those technologies to address the diverse challenges of NTNs
from the perspectives of both academia and industry. Particularly, we first
provide an in-depth tutorial on NTN and the enabling technologies including
network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on
how network slicing and AI/ML have been leveraged to overcome the challenges
that NTNs are facing. Moreover, we present how ORAN has been utilized for NTNs
from an industry standpoint. Finally, we discuss the current challenges and
open issues and introduce potential research directions for NTN in the 6G era.

As summarized in Table I, there are a few surveys on the development of NTN in
the literature, such as [6, 7, 4, 9, 10, 8, 11]. Particularly, [8] focuses on
the integration of New Radio in NTN, whereas [4] elaborates on the role of NTN
in 5G systems. Moreover, [6] surveys the integration of NTN with different
types of networks such as IoT, Mobile Edge Computing (MEC), and mmWave. Taking
another approach, [7] discusses the utilization of cellular, Wide-Area, and
NTN for IoT applications. Additionally, [9] investigates the application of
AI/ML in NTN, whereas [10] and [11] discuss the architecture, performance
evaluation, and standardization aspects of NTNs and terrestrial networks
integration. To the best of our knowledge, there exist no comprehensive
studies on how the abovementioned technologies are employed to address the
challenges in NTN, especially from an industry perspective. Given the rapid
increase in user demands and the emergence of new services, there is an urgent
need for new solutions to address the limitations of NTNs. As a result, this
paper is expected to fill the gap in the literature and contribute to the
future development of 6G networks.

As illustrated in Fig. 1, the rest of this paper is organized as follows.
Section II provides a tutorial on NTN and its core enabling technologies
including network slicing, ORAN, and AI/ML. Then, the applications of network
slicing and AI/ML in NTN are discussed in detail in Section III and IV. Next,
the industry applications of ORAN are presented in Section V. Open issues,
challenges, and future research directions are presented in Section VI, and
conclusions are given in Section VII.

##  II Overview of NTN and Enabling Technologies

In this section, we first provide an overview of NTN, including its
architectures, advantages, and use cases. Then, the challenges in NTNs are
discussed in thoroughly. Finally, we provide a brief background on advanced
technologies and infrastructures that can be adopted to efficiently address
these challenges, including network slicing, AI/ML, and ORAN.

###  II-A Overview of NTN

Figure 2: Overview of NTN [12].

NTNs are communication networks that partially or fully operate through the
airborne or spaceborne vehicle(s) [13, 14, 15, 12]. With connections from the
air or space, NTNs can empower ultra-reliable communications when the
terrestrial infrastructures are not available, such as in remote and
unreachable areas or during natural disasters [16]. In addition, NTNs can
easily provide multicast connectivity with high data rates and low latency
over a large region, enabling massive Machine Type Communication (mMTC) and
enhanced Mobile Broadband (eMBB) communications. Moreover, multiple NTNs can
be connected together and/or connected to existing terrestrial networks to
provide continuous and ubiquitous wireless coverage, and thus bringing us
closer to the era of anything, anytime, and anywhere communications [14].

The features, requirements, and protocols for NTNs have been standardized by
3GPP since 2017 with a study item on deployment scenarios and channel models
in Release-15 [16]. After that, in Release-16, the required features to enable
New Radio (NR) support for NTN are determined by a study in Radio Access
Network (RAN) working group. This is then developed further in Release-17 with
a set of potential features to enable 5G NR to operate over NTN at frequencies
up to 7.125 GHz [16]. Use cases of IoT over NTN with link budget and
parameters are also discussed in this Release-17. Release-18 is under
development and will focus on 5G systems with satellite backhaul architecture
as well as addressing the mobility and continuity problems when connecting
different NTNs and also connecting NTNs to terrestrial networks [16].

NTN has been attracting great attention from both industry and academia
recently due to its potential to complement and enhance existing terrestrial
networks in terms of coverage, capacity, and mobility. It is expected to play
an essential role in the development of 5G-advanced and 6G networks. For that,
big tech companies like SpaceX, OneWeb, Amazon Kuiper, and SoftBank are
investing billions of dollars in this field to provide global connectivity and
expand their business models [17, 18]. In general, NTNs can be categorized
into airborne and spaceborne platforms as illustrated in Fig. 2.

####  II-A1 Airborne Platforms

This category includes UAVs and HAPSs. While UAVs operate at low altitudes,
(e.g., a few hundred meters), HAPSs, such as airplanes, balloons, and
airships, can reach the stratosphere region with altitudes ranging from 20
kilometers to 200 kilometers [12]. One example of airborne NTN is Project Loon
[19, 20] from Google which deploys balloons at high altitudes from 18
kilometers to 25 kilometers to provide connectivity to remote and rural areas.
Facebook is also involved in airborne NTN by developing solar-powered drones
that operate at an altitude of up to 27 kilometers and provide Internet
services to an 80-kilometer-radius area below its flying path through Project
Aquila [21].

Airborne NTN can be deployed quickly at a lower cost and has a much smaller
propagation delay compared to spaceborne NTN. However, airborne platforms face
critical challenges of stabilization on air and refueling. In particular,
operating at low altitudes makes them vulnerable to environmental conditions
such as strong winds or storms that can change the flying path of balloons and
drones or even destroy them. It is also difficult and inefficient to refuel
airborne vehicles while maintaining connections for users. These challenges
could be the reason for the termination of projects Aquila and Loon in 2018
and 2021, respectively. While airborne platforms may not be effectively used
to provide Internet services, they can be used in emergency scenarios such as
during natural disasters and rescue missions in rural areas where cellular
coverage is not available.

####  II-A2 Spaceborne Platforms

Recently, spaceborne NTN has been emerging as a promising technology for
future communication networks (e.g., 5G-Advanced and 6G) where satellites are
placed in space to provide communications for users on Earth. These satellites
fly around the Earth in specific orbits and can be categorized into: (i)
Geostationary Earth Orbit and (ii) Non-Geostationary Earth Orbit [12, 22].

##### Geostationary Earth Orbit (GEO)

Satellites operating in GEO (i.e., GEO satellites) fly around the Earth above
the equator from west to east following the Earth’s rotation. GEO satellites
travel at the same rate as the Earth and take 23 hours 56 minutes and 4
seconds to complete one orbital period. As a result, these satellites are
“stationary”, i.e., appear motionless, at a fixed position in the sky to
observers on Earth [23]. To exactly match the Earth’s rotation, GEO satellites
must travel at the speed of about 3 kilometers per second at an altitude of
35,786 kilometers. Due to the high altitude, GEO satellites can cover a large
portion of the Earth’s surface. Theoretically, three GEO satellites can
provide near-global coverage. The beam footprint of GEO satellites can range
from 200 kilometers to 3,500 kilometers [14]. There are currently hundreds of
GEO satellites in the orbit [24], and most of them are used to provide
services such as weather monitoring, TV broadcasting as well as remote sensing
and positioning. GEO satellites can also be used for communication services.
However, due to the high altitude, the communication latency is significantly
high (around 600 milliseconds according to Starlink [17]). To provide low
latency connections, satellites operating at low orbits have been gaining
great attention recently.

##### Non-Geostationary Earth Orbit (NGEO)

Different from GEO satellites, NGEO satellites operate at lower altitudes with
orbital periods of less than 24 hours. In addition, their positions can be
always changed with respect to observers on Earth. There are two types of NGEO
satellites according to their altitudes: (I) Medium Earth Orbit (MEO)
satellites and (ii) Low Earth Orbit (LEO) satellites.

  * •

MEO satellites usually operate at 2,000 kilometers to 25,000 kilometers above
the Earth’s surface [12]. Flying at these altitudes allows MEO satellites to
create beams with diameters from 100 kilometers to 500 kilometers.

  * •

LEO satellites can be deployed at altitudes from 200 kilometers to 2,000
kilometers [12] which is lower than other orbits but still very far from the
Earth’s surface. LEO satellites can create beam footprints with diameters from
5 kilometers to 200 kilometers.

MEO and LEO satellites can be used for sensing, positioning, and communication
systems due to their low propagation delay compared to GEO satellites. For
example, the US Global Positioning System (GPS) uses at least 24 MEO
satellites for its global positioning services. As of June 26, 2022, the GPS
constellation consists of 31 operational satellites [25]. Although MEO
satellites can be used for communication services, the communication latency
is still high and may not be feasible for today and future communication
applications. Recently, LEO satellites have been emerging as promising
platforms to provide Internet services to users on Earth anywhere and anytime.
For example, Starlink, the world’s first and largest constellation, uses
thousands of LEO satellites to deliver broadband Internet for services such as
streaming, online gaming, and video calls to users around the world [17].
Starlink’s satellites fly at an altitude of about 550 kilometers with much
lower propagation delay compared to those of GEO and MEO satellites. As a
result, Starlink can achieve a roundtrip delay of around 25 milliseconds for
its services [17]. The current biggest competitor of Starlink is Amazon’s
Project Kuiper which aims to launch over 3,236 LEO satellites to provide low
latency and high-speed Internet services to users on a global scale. In
addition, this satellite system is also integrated into resilient
communication infrastructure powered by a global network of ground stations
and Amazon Web Services for better services [18].

###  II-B Challenges of NTN

Although providing various promising applications and use cases, NTN faces
several technical challenges that need to be fully addressed to ensure its
success in future communication networks.

  * •

Propagation delay and path loss: NTN platforms face a particular challenge of
propagation and path loss due to their high altitudes. For example, GEO
satellites experience a round-trip latency of around 600 milliseconds [17]
which is considered significantly high. This may not be feasible for
communications that require low or ultra-low latency such as online gaming,
video streaming, and VR/AR. Compared to terrestrial communications, the
propagation path losses of NTN platforms are much higher due to long distances
to UEs. In addition, NTN platforms at high altitudes can cover large areas to
serve a massive number of users with different propagation delays and path
losses in different regions [14]. Consequently, ensuring good communications
for all users is very challenging. Moreover, managing initial accesses and
synchronizations for these diverse users also poses another challenge to NTNs.

  * •

Channel estimation: Channel estimation is an essential task in every
communication system. For NTNs, it is even more challenging due to the
inherent time-variant property of NTN platforms. In particular, at high
altitudes, NTN platforms fly from horizon to horizon very fast (e.g., around
5-10 minutes for LEO satellites [12]). As a result, users on Earth remain in
the coverage of a particular NTN platform for a very short period of time. In
addition, the long propagation delay may make estimated channel state
information outdated quickly [26]. Hence, traditional estimation methods in
terrestrial networks may not be feasible for NTNs, and advanced approaches are
required to ensure the success of NTNs.

  * •

Doppler effect: Due to their high movement speeds, NTN platforms introduce
significant Doppler effects on communication links between them and users on
Earth. In particular, the Doppler effect is the shift in the frequency of
signals during the relative motion between transceivers. The Doppler effect
also happens in terrestrial networks, e.g., users on high-speed trains or
cars. However, in NTNs, this effect is more serious as NTN platforms fly at
very high speeds. For example, a user communicating with an LEO satellite
operating at 600 kilometers above the ground may experience a Doppler shift of
up to 48 kHz given the carrier frequency of 2 GHz [27]. This Doppler shift is
significantly larger compared to those of users in terrestrial networks.

  * •

Mobility management: Another challenge in NTNs is mobility management due to
the high speeds of NTN platforms. For example, satellites operating at NGEO
have short orbital periods (around 2-10 hours [12]). Consequently, users on
the ground can only observe a particular NGEO satellite over a very short
period, typically several minutes [12]. In this case, the ground users will
need to perform handovers frequently, especially when these satellites use
multiple beams to cover an area on Earth.

  * •

Resource management: Compared to terrestrial terminals, NTN platforms need to
transmit signals with much higher power to deal with the high path loss and
ensure users on Earth can successfully decode the transmitted signals. This
introduces a new challenge for NTN platforms as they are not equipped with
stable power sources like terrestrial terminals. Moreover, the frequency bands
assigned to NTN communications are limited and already crowded. In particular,
the S-band and Ka-band are the target bands for NTNs [12]. However, 4G LTE
devices are using the S-band, and millimeter wave-enabled devices in 5G are
using the Ka-band. As a result, users in NTNs may experience co-channel
interference from these terrestrial devices. This demands novel spectrum-
sharing solutions to intelligently and efficiently utilize the limited
frequency bands.

###  II-C Technologies and Infrastructures for NTN

Solutions to address the aforementioned challenges of NTNs have been actively
developing in the past few years. Among them, network slicing, ORAN, and AI/ML
are promising technologies that are expected to play vital roles in NTNs. In
this section, we will provide the fundamentals and advantages of these
technologies for NTNs.

####  II-C1 Network Slicing

Figure 3: General architecture of network slicing-powered NTN [28, 29].

Network slicing is a new technology that allows us to create multiple unique
logical and virtualized networks, called slices, on top of a physical
infrastructure [30, 31, 29]. With network slicing, service providers can
quickly serve diverse services according to their requirements such as
resources, quality of service (QoS) demands, and network functionalities,
simultaneously. To do that, network slicing deploys various technologies such
as Software-Defined Networking (SDN), Network Function Virtualization (NFV),
and Network Orchestration. In particular, SDN is an approach to decouple the
control plane from the data plane of network devices and make the control
plane programmable [32]. In this way, SDN can dynamically perform network
configurations through the centralized controller, resulting in better network
performance compared to conventional networking. NFV is a novel technology to
virtualize network services such as firewalls, routers, and load balancers by
using virtualization technologies and commercial off-the-shelf programmable
hardware [33]. In such a way, NFV brings various benefits to network operators
such as decoupling software from hardware, flexible network function
deployment, and dynamic scaling [34]. Finally, network orchestration refers to
the automated rules to dynamically control and automatically program the
network, allowing it to ensure the service level agreements for the services.
With these advanced technologies, network slicing is expected to play an
essential role in 5G and beyond due to its advantages as follows [31, 35]:

  * •

Enabling Scalability and Flexibility: In practice, network services may
require different amounts of resources and network functions at different
times. To better utilize the system’s resources, network slicing allows
network operators to dynamically allocate resources from a particular slice to
other slices that demand higher resource requirements without affecting
services in the source slice. Network slicing also can quickly create a slice
for new services when requested, e.g., critical applications, and adjust the
allocated resources and network functions based on users’ demands.

  * •

Ensuring Security and Privacy: Although using the same physical
infrastructure, slices in network slicing networks are independent. As such,
security attacks in one slice cannot affect other slices in the system. This
is called slice isolation, an important design principle of network slicing.

  * •

Improving QoS: Network slicing can allocate different types of resources and
network functions to diverse services based on their QoS requirements. In
addition, during congestion situations, network slicing can still guarantee
the QoS for users by dynamically allocating resources and prioritizing traffic
between slices in the network.

With the aforementioned advantages, network slicing has been emerging as a
potential enabler that can effectively address various challenges of NTNs. A
general network slicing-powered NTN system is illustrated in Fig. 3. In
particular, like traditional network components, NTN platforms can also be
virtualized both in terms of resources and network functions to support
different types of services simultaneously. With network slicing, service
providers can quickly establish new slices for NTN services, e.g., Earth
observation, broadcast services, and broadband services, to serve different
groups of users [36]. This can efficiently address the problem of high cost
and high delay in building satellite constellations in orbits. In addition,
network slicing also allows NTNs to be effectively and flexibly integrated
into existing terrestrial networks. Nevertheless, network slicing-powered NTN
systems still face several challenges in designs, protocols, and
optimizations. These challenges and existing approaches in the literature will
be discussed in detail in Section III.

####  II-C2 ORAN

ORAN is the disaggregation of the traditional RAN, allowing cellular equipment
provided by different vendors can be interoperated by using open and
standards-based protocols [37, 38]. In the following, we first provide the
fundamentals of RAN and then discuss the architecture and advantages of ORAN
over RAN.

Figure 4: General architecture of mobile networks.

Fig. 4 illustrates a general architecture of mobile networks, including RAN,
core network, and services. In particular, the purpose of RAN is to connect
user devices (e.g., mobile phones and computers) to the core network to access
services provided by network operators. The main component of RAN is base
stations. In general, a base station consists of two main units: (i) radio
unit (RU) and (ii) baseband unit (BBU). RU receives signals from users and
sends them to BBU for processing before transmitting them to the core network.
Traditionally, a single vendor provides all the units, software, and
connections between them of the base station. As a result, it is difficult if
not impossible to change any component of RAN, making it costly and less
flexible in deployment and operation. To address all these drawbacks of
conventional RAN, ORAN was proposed recently with a more flexible design.

Figure 5: RAN vs ORAN [39].

In Fig. 5, we illustrate the differences in the designs of RAN and ORAN. In
particular, ORAN allows the radio access network to work with hardware from
any vendor while traditional RAN requires a specific vendor for its hardware.
Similarly, in the software part, software from any vendor can be used with a
commercial off-the-shelf server, whereas RAN requires proprietary RAN software
from a specific vendor. With this open design, ORAN offers various advantages
compared to RAN, such as flexibility, open management and orchestration,
enabling multi-vendor solutions, and being able to use less-expensive third-
party hardware and software. With these advantages, ORAN can be an important
technology for NTNs as well as NTN-integrated terrestrial networks. More
details applications of ORAN for NTN and current development both in academia
and industry will be presented in Section V.

####  II-C3 AI/ML

AI/ML has been developing significantly since the deep neural network (DNN)
architecture was re-invented and trained over a large amount of data by
powerful computational computers. It has been successfully applied to various
areas such as computer vision, gaming, and natural language processing. In the
fields of communications and networking, AI/ML has been emerging as a
promising solution to efficiently and significantly improve communication
performance. AI/ML is a potential technology for NTNs to overcome their
critical challenges that conventional approaches cannot handle well. For
instance, deep reinforcement learning, an advanced AI/ML algorithm, can be
used to address resource allocation, routing, handover, and beamforming
problems in NTNs [40, 41, 42] due to its capability in dealing with the
dynamics and uncertainty of the system. Deep learning (DL) with advanced
neural network architectures such as Convolutional Neural Network (CNN) and
Recurrent Neural Network (RNN) is very effective in handling non-linear
effects and general impairments of channels in NTNs. In this section, we will
present the fundamentals of common AI/ML techniques that can be used to
improve the performance of NTNs. The details of existing AI/ML applications
for NTNs will be presented in Section IV.

AI refers to a field of computer science that focuses on building machines
(especially computer systems) to perform tasks that require human intelligence
such as learning, problem-solving, decision-making, and reasoning. ML is a
special subset of AI in which a machine tries to learn a specific task (e.g.,
image classification, voice recognition, or signal classification and resource
allocation in communication networks) and performance metrics such as
classification accuracy and performance loss by using only the data collected
from the task. ML can be generally categorized into three subsets: (i)
supervised learning, (ii) unsupervised learning, and (iii) reinforcement
learning [43].

  * •

Supervised learning: In this type of ML, the learning model is trained with
labeled datasets to classify data or predict outcomes accurately. For example,
an ML model can be trained with a dataset of images of different animals that
are labeled by humans. Over time, the model can learn ways to identify these
animals. Supervised learning is the most common type of ML used today. The
standard supervised learning techniques include Naive Bayes, Liner Regression,
Decision Tree, Support Vector Machine, and Logistic Regression [43].

  * •

Unsupervised learning: Different from supervised learning, unsupervised
learning models are trained on unlabeled data. To do that, unsupervised
learning algorithms scan through training data to identify patterns or trends
without human intervention. For example, unsupervised learning can be used to
learn from online sales data to look for different types of clients making
purchases. The standard techniques in unsupervised learning include K-means
clustering, Self-Organization Map, and Principal Component Analysis [43].

  * •

Reinforcement learning: This type of ML does not require a prior dataset for
training. In particular, reinforcement learning is trained through trial and
error by interacting with an external environment. Given a particular state,
the reinforcement learning agent makes an action based on its current policy.
After that, it observes the next system state and the immediate reward
calculated by a predefined reward function. All these observations will be
learned by the agent to gradually obtain the optimal policy. The standard
techniques in reinforcement learning include Q-learning, deep Q-learning, and
multi-armed bandit [43, 44, 45].

Recently, DL, a subset of ML, has been proposed to further improve the
prediction performance of ML models by using DNNs. Compared to conventional ML
algorithms, DL has several advantages such as no need for system modeling,
supporting parallel and distributed algorithms, and being reusable. A typical
DNN consists of four main components: (i) neurons, (ii) weights, (iii) biases,
and (iv) activation functions. In particular, layers of a DNN are connected to
each other by neurons, also known as nodes. Each neuron has an activation
function such as tanh, sigmoid, and relu [44]. The activation function is used
to calculate the output of each neuron given its weight and bias. During
training, the weights of the DNN are updated by calculating the gradient of
the loss function. There are three main types of DNNs including (i) Artificial
Neural Network (ANN), (ii) Recurrent Neural Network (RNN), and (iii)
Convolutional Neural Network (CNN).

##### Artificial Neural Network

Figure 6: General architecture of ANN.

ANN, also known as feed-forward neural network, is the most common type of
DNNs. Typically, an ANN consists of nonlinear processing layers, including an
input layer, several fully connected hidden layers, and an output layer as
shown in Fig. 6. As a hidden layer takes the outputs of its previous
interconnected layer as its inputs, ANN processes information in one direction
from the input layer through the hidden layer to the output layer. Generally,
ANN can work well with nonlinear functions, and thus it can be considered a
universal function approximator.

##### Recurrent Neural Network

Figure 7: General architecture of RNN.

RNN is a special architecture of DNNs that is widely used for time series data
or data that involves sequences. To do that, RNN deploys a feedback loop and
hidden states to store information of previous inputs to improve the learning
processes of the next data, as illustrated in Fig. 7. In particular, the
output of the RNN cell at time $t-1$ will be stored in the hidden state
$h_{t}$. This stored information will be used for learning the next sequence
at time $t$.

##### Convolutional Neural Network

Figure 8: General architecture of CNN.

CNN is designed mainly for training over image data. The general architecture
of a CNN is illustrated in Fig. 8. Specifically, a CNN consists of convolution
layers that have a set of convolutional filters. Each convolutional filter
extracts specific features from image data. After each convolution operation,
CNN uses a Rectified Linear Unit (ReLU) transformation to maintain positive
values during training. This helps the training process to be faster and more
effective. The pooling layer, also known as downsampling, is used to reduce
the number of training parameters by performing dimensionality reduction. It
has been widely demonstrated that CNN can handle image data much more
effectively than ANN. This is because CNN does not need to convert images to
1-dimensional data before training, which increases the number of training
features as well as removes the correlations of features in images. CNN, in
contrast, can learn these features directly from image data by using
convolutional layers.

##  III Network Slicing-aided NTN

Network slicing is a powerful technique in telecommunications, which allows
operators to create multiple virtual networks from a shared physical network
architecture. Utilizing technologies such as SDN and NFV, the operators can
dynamically allocate network resources to each network slice according to its
specific requirements in terms of bandwidth, latency, and reliability. In the
context of NTN, network slicing enables a single network infrastructure to
serves multiple types of applications and users with different demands. For
example, remote control for UAVs often requires low-latency communication,
whereas satellite internet service might require higher bandwidth to serve
millions of users. For both cases, network slicing can divide a single
physical NTN into two virtual slices to meet different demands by allocating
different resources to each slice.

###  III-A Satellite Networks

Satellite networks, due to their benefits such as worldwide coverage area and
fast deployment, can significantly enhance mobile terrestrial networks [28].
Several challenges for satellite networks such as delay, throughput, and
utilization of resources can be addressed effectively by network slicing [46].
However, those advantages come with several problems such as seamless
integration of satellite network into the existing mobile networks [28], load
balancing, resource allocation considering satellite constellation topology
[47], and efficient routing methods [48]. To address these problems, multiple
approaches have been proposed.

####  III-A1 Resource Allocation

Among those problems, resource-allocation-related challenges have been
addressed in [49, 50, 51, 52]. Specifically, in [49], the authors propose
innovative approaches to integrate network slicing into railway communication.
These approaches aim to optimize network resource allocation by ensuring that
latency requirements are met for each class of service in a network. First, a
mathematical approach, namely Queuing Theory (QT) method, uses a nonlinear
constrained optimization to minimize the difference between the latency. In
addition, a Neural Network (NN) approach, namely NN total model, utilizes
delays obtained through simulations as input dataset to produce the control
variables. Since this approach approximates the required latency by simple
fitting, it could lead to inefficient and inaccurate results. To overcome this
drawback, the authors propose an NN-divided model [49] to approximate the
latency of satellite and terrestrial networks using two separate neural
networks. This approach solves an optimization problem similar to that of the
QT-based method, except that it minimizes an expected mean latency. Simulation
results show that these approaches can reduce the latency by roughly 25%-60%.
With a focus on dynamic allocation of radio resources, in [50], the authors
propose a RL-based slicing strategy, namely dynamic radio resource slicing
strategy (Sat-RRSlice), to serve the LEO networks. Particularly, the network
slicing problem is modeled as a Semi-Markov Decision Process (SMDP), including
network states, actions, state dynamics, and rewards. The states of each slice
denote the resource allocation and utilization at a time slot. Moreover, the
actions, taken by a resource manager on each slice at a time slot, indicate
whether a resource unit is allocated to a slice or not. Furthermore, the
reward function is used to calculate the reward, defined by the network’s
total utility, for taking a certain action. Using Q-learning, optimal Q-values
are learned iteratively based on information obtained from a wireless
environment over discrete time slots. Simulation results show that, compared
with the static slicing strategy [50], Sat-RRSlice can increase the resource
utilization rate and sum utility (i.e., the total benefit gained from
communication) by at least 18.5% and 9%, respectively.

Taking another approach, in [51], the authors design a network-slicing
architecture for LEO satellite network with a focus on increasing flexibility
and resource utilization. In this architecture, network slicing is managed
through Network Slice Selection Assistance Information (NSSAI) and Network
Slice Selection Policy (NSSP). The NSSAI, stored in each device, is
specifically configured for each device while the NSSP is the set of rules
that the network management component must follow. Moreover, two types of
network slices are defined: shared slices used by all users for common
functions (e.g., storing user data and authenticating users), and dedicated
slices for providing personalized service. Experimental results show that a
network implemented with this architecture can effectively handle multimedia
communications over IP networks. The authors in [52] design a framework for
SDN/NFV-enabled satellite ground segment systems to enable on-demand network
slicing. This framework, namely OnDReAMS, has a Service Orchestrator (SO)
responsible for managing the life cycle of network slices, e.g.,
instantiation, maintenance, and termination. Besides, an NFV Manager is used
to handle the instantiation, modification, and termination of the VNFs.
Moreover, a novel component, namely Satellite Network Slice Descriptor (SNSD),
describes the characteristics of the slice as requested by the customer,
allowing the SO to set up the slice with flexibility. For resource allocation,
the slicing problem is solved using a Mixed Integer Linear Program (MILP)
model. A time-window-based online algorithm is then developed to handle the
on-demand aspect of the solution, which solves the MILP at the beginning of
each time window with updated slice resources. Simulation results show that
OnDReAMS can reduce the average of QoS violation by approximately 10%-50%
compared with a baseline approach.

####  III-A2 Routing

Besides resource allocation, another important aspect of slice-aware satellite
networks is routing, which is the focus of [48] and [53]. Particularly, in
[48], a routing strategy for real-time applications over satellite networks
with Virtual Functions (VFs) deployed on satellites is proposed. Particularly,
the authors develop a VF constrained simple path algorithm to find the
shortest path by searching for paths while checking their delay and function
requirements iteratively. To overcome the drawbacks of this algorithm (i.e.,
sub-optimality, instability, and non-scalability), a second algorithm is
developed, namely VF-aware shortest path algorithm (VFSP). In this algorithm,
the problem of finding the best path in an SN is divided into two sub-
problems: finding the shortest path from the source to each functional
satellite and from each functional satellite to the destination. By leveraging
the unique property of Dijkstra’s algorithm [54], these sub-problems are
solved efficiently by running the algorithm twice, once forward from the
source and once from the destination, eventually joining these paths to find
the optimal solution. Simulation results show that both algorithms have a run
time of approximately 100 times faster than that of the integer linear
programming (ILP) approach. Moreover, compared to the KSP-based approach, the
VFSP method can improve the acceptance ratio by at least 5%. Another approach
for routing, i.e., link-embedding methods, is presented in [53]. Specifically,
the authors propose a network slicing planning scheme for satellite networks,
considering the mobility of LEO satellites and the handover of virtual
networks. To this end, two link-embedding methods are developed. The first
method is an algorithm that aims to find the shortest path while ignoring the
links with limited capacity, thereby significantly reducing the propagation
delay. The second method locates the largest link in the neighborhood and
updates the path weight to the found path’s minimum link capacity, thereby
improving the link stability. Simulation results show that the proposed
methods can improve the data throughput by up to 21%.

####  III-A3 Satellite Edge Computing

Network slicing application to satellite edge computing (SatEC) architectures
are discussed in [47] and [46]. Particularly, in [47], the authors present an
IoT-supportable SatEC architecture to use satellites for 6G IoT services
efficiently. To that end, the authors propose solutions to address two
problems: balance the trade-offs between latency and power, and managing
network resource allocation. For the first problem, a multi-objective
optimization problem is formulated considering latency, computational power,
and transmission power attenuation. A Satellite Edge Multi-objective Tabu
Search (SE-MOTS) [55] algorithm is used to find a Pareto-optimal point, which
presents the best trade-off among the three factors considering the dynamic
satellite topology and service requirements. For the second aspect, a sliced
SatEC optimization problem, formulated as a normalized weighted sum of three
objective functions, is used to schedule the tasks with different demands. A
golden-section method [56] is used to solve this aspect with low computational
resources. Taking another approach, in [46], the authors design a system
architecture that combines space-based edge computing and network slicing for
space-based network resource management, as illustrated in Fig. 9. In this
architecture, edge computing nodes are included due to their benefits of being
closer to the data sources and their ability to process data locally. These
nodes, namely distributed fog satellite nodes and centralized space-based edge
clouds, are responsible for computing tasks with low and high complexity,
respectively. In the control plane of the network, the authors introduce the
two-layer controller, Fog SDN controller (FSC) and Cloud SDN controller &
Slicing Manager (CSC&SM). Particularly, FSC abstracts fog satellite nodes and
manages their resources while CSC&SM optimizes resource management of network
slices with a global view of the network. Moreover, a 5G Satellite Network
Slice Management(5G SNSM) [46] is proposed to perform flexible nerwork slice
allocation based on QoS requirements. Simulation results show that the
proposed architecture can improve the end-to-end delay and jitter by at least
7% and 50%, respectively.

Figure 9: Space edge cloud satellite network architecture [46].

####  III-A4 Slice Admission Control

Slice admission control is another important aspect of network slicing that
should be considered simultaneously with resource allocation. In [57], a two-
stage approach is developed to jointly optimize resource allocation and slice
admission control in LEO satellite networks. To this end, the authors first
formulate a robust optimization problem to optimize slice admission, taking
into account the uncertainty in the network state and user distribution. Based
on the admission decisions in the first stage, the resource allocation
strategies are optimized in the second stage to guarantee QoS for users. Since
the original optimization problem is non-convex, auxiliary variables are
introduced to transform the problem into a convex form, thereby significantly
reducing the problem’s complexity. Simulations results show that the proposed
approach can improve the user QoS by 7.2% compared to the non-robust approach.

###  III-B Integrated Satellite-Terrestrial Networks

Integrated Satellite-Terrestrial Networks (ISTNs) utilize satellite
communication to extend network coverage and reduce the dependence on
terrestrial infrastructure, thereby offering services to users with higher
efficiency. Network slicing can bring several advantages to ISTNs such as
efficiency in resource sharing, differentiated QoS provisioning [58], and
network flexibility as well as scalability. However, multiple challenges also
arise from this application, including network awareness requirements and the
highly dynamic environments [58], QoS reduction due to terrestrial obstacles
[59], limited resource constraint, and service provider incentives.

####  III-B1 Resource Allocation

Innovative approaches are proposed to overcome those challenges. To address
the resource allocation problem in slice-aware ISTN, optimizing the VNE [60]
algorithm is a promising solution. To that end, the authors in [59] design a
framework, namely Slice-Aware VNE for Satellite-Terrestrial (SAST-VNE) to
improve the implementation of the VNE algorithm in ISTNs. In this framework,
problematic network slices, i.e., those that do not meet Key Performance
Indicators (KPIs) or will undergo satellite handover, are inserted into a
queue for analysis. In case a new slice is created, the embedding is computed
using the DViNE algorithm [61]. For problematic network slices, if they have
high priority, i.e., low tolerated latency, each affected link will be
remapped with the shortest-path algorithm. On the contrary, if they have lower
priority, the authors propose an interative algorithm, namely SAST-VNE, to
balance the network load and minimize the migration cost. Simulation results
show that during handovers, SAST-VNE can reduce the average node migrations
and average link migration by roughly 25% and 6%, respectively. Another
implementation of VNE for ISTNs is presented in [58]. Particularly, the
authors design a testbed to validate the feasibility of integrating non-GEO
satellite constellations and implementing VNE algorithms in highly dynamic
network conditions. This testbed consists of a dynamic satellite-terrestrial
network emulated in Mininet [58], an external Ryu SDN [58] controller and a
VNE algorithm script. First, the Mininet emulator is used to build an
OpenFlow-based substrate network, enabling the Ryu SDN controller to manage
the flow of packets in the network. The Ryu SDN controller also uses a Traffic
Engineering (TE) application to create paths for each VN, set rate limits,
gather network statistics, read the network topology, and process changes to
the network topology in real-time. Furthermore, the VNE algorithm script,
implemented in Matlab using an Integer Linear Programming formulation with a
load-balancing objective function, handles required resources for virtual
networks and updates the network configuration to the Ryu SDN controller.
Simulation results show that the testbed can handle many scenarios and changes
in the network topology such as adding new VNs, dynamic changes in the network
layout, handling of network failures.

Moreover, approaches to optimize resource allocation are presented in [62] and
[63]. Particularly, the authors in [62] propose a dynamic slicing strategy for
ISTNs. The proposed strategy uses mirror nodes, instead of virtual nodes, to
store motion duration and resource information of satellites. Based on that,
service forwarding path and slice resources can be adjusted, as illustrated in
Fig. 10. The mirror nodes also communicate the satellite resources with ground
stations and receive feedback, i.e., which satellites to be chosen for the
slicing process. An optimization model is then formulated to maximize the
long-term average slicing performance of the network with a constraint of
computing, storage and bandwidth resources. Moreover, a resource reserved in
slices and adaptive adjustment between slices algorithm (R2A2) is developed to
solve the optimization problem. Simulation results show that, compared to a
DQN approach, R2A2 can increase resource utilization and slice satisfaction by
roughly 33% and 30%, respectively. With a focus on admission decision for a
slice request, the authors in [63] propose a game-theory-based solution for
resource allocation in ISTNs. To solve the challenges related to limited
resource constraints, slice admission control is formulated involving
different combinations of service providers and users. Specifically, service
providers and users form a multi-sided market to exchange resources. To
participate in the network, both sides consider their prices, i.e., network
resources for the providers and the cost for the users. An auction, designed
with the Multi-Sided Ascending-Price Auction Mechanism [63], is performed by
increasing the price for both sides based on supply and demand until a balance
is reached. Eventually, the final prices are set, and the allocation of
resources to users is determined. Experimental results show that, compared to
a baseline approach, i.e., second-price auction [63], the ascending-price
auction can increase the bandwidth per user, the admission ratio, and the gain
from trade per user by up to 25%, 33%, and 20%, respectively.

Figure 10: Mirror node model [62].

A novel approach for network slice orchestration is presented in [64].
Particularly, the authors present a system model for deployment of network
slicing for aircrafts using satellites. This model aims to enhance resource
management in ISTNs by incorporating the concept of slice collaboration, i.e.,
defining network slices based on their willingness to share user traffic
statistics with the infrastructure provider). To this end, a Mixed Integer Non
Linear Programming (MINLP) model is developed to maximize the resources, i.e.,
cache and backhaul resources) while satisfying slice constraints. Moreover, a
pricing model [64] is proposed to increase the chances of uncooperative
slices, i.e., slices that are unwilling to share user traffic statistics,
being served. Simulation results show that the MINLP model can increase the
selection probability of uncooperative slices by 33% and cooperative slicing
can accommodate 200% more services compared to that of uncooperative slicing.
Taking another approach, the authors in [28] propose a slice-aware NTN
architecture to enable the seamless integration between NTN and terrestrial
networks. To this end, the authors propose an end-to-end slicing model which
treats the NTN as a slice-aware link in the terrestrial network. Based on the
proposed model, a functional architecture fully compliant with the 3GPP
standard is developed to allow the NTN to be integrated with the terrestrial
network. The architecture consists of three main segments: the user segment
with the satellite terminal, the ground segment with satellite gateways and
network control components, and the space segment with one or more satellites.
The Satellite Terminal (ST) serves as the User Equipment (UE) in 5G networks,
communicating with the space segment, which then relays messages to the
satellite gateway. Satellite gateways are the central entities that manage
resource allocation, authentication, and data processing. Simulation results
show that the proposed slice-aware scheme can achieve better trip time, packet
error rate, and jitter compared to those of the cases without network slicing.

####  III-B2 User Assignment

In addition to routing, several frameworks have been proposed to enhance
network performance by optimizing user assignment in [65], [41], and [66].
Particularly, in [65], the authors propose a network slicing scheme for hybrid
satellite-terrestrial networks, aiming to improve reliability and reduce the
video traffic offload. To this end, the authors develop a scheduling strategy
that assign the users to three different types of networks, e.g., only
satellite, only terrestrial, and hybrid satellite-terrestrial, based on
Channel Quality Indicator (CQI) and QoS requirements. Simulation results show
that the proposed scheduling strategy can improve the network throughput by up
to 48% compared to the case where only the satellite network is employed.
Differently, with a focus on space-terrestrial integrated vehicular networks
(STIVN), the authors in [41] propose a scheme for network slicing to support
both delay-tolerant services (DTSs) and delay-sensitive services (DSSs). This
scheme aims to solve the problem of scheduling and resource slicing in STIVN,
which involves allocating spectrum resources to slices, determining bandwidth
allocation, and user assignment for each vehicle. Specifically, the system
cost (i.e., DSS requirement violation, DTS delay, and slice reconfiguration)
is taken into account in two subproblems: a resource slicing subproblem in
large-timescale, and a resource scheduling subproblem in smaller timescale. To
solve those subproblems, a two-layered RL-based scheme is developed. In the
first layer, namely resource slicing layer, spectrum-resources are pre-
allocated to slices using a proximal policy optimization (PPO)-based RL
algorithm [41]. In the second layer, namely resource scheduling layer,
depending on network conditions and service requirements, spectrum resources
are assigned to vehicles using match-based algorithms. Simulation results show
that the proposed approach can reduce the overall system cost up to roughly
72.57%, compared to that of a baseline approach. In another approach, the
authors in [66] propose a network slicing scheme to improve their existing
resource management approach in satellite-LTE networks. In the previous
approach, an adaptive hybrid satellite-LTE downlink scheduler (H-MUDoS) [67]
determines if users can be served through satellite network or ground-based
stations. However, simulation results show that the decrease in QoS of the
satellite network affects the performance of the entire hybrid network. In the
current approach, the network is separated into isolation slices, each with
its own scheduling strategy assigned by the scheduler. Simulation results show
that the decrease in QoS of a slice does not affect other slices.

####  III-B3 Routing

Next, approaches for optimizing routing are presented in [68] and [69].
Particularly, in [68], the authors propose a novel framework to optimize
resource distribution for network slicing management in ISTNs. In this
framework, a hybrid approach combining ML and Ant Colony Optimization, is
implemented to associate a new metric, i.e., a cost metric, to any route in
the network. In this case, when a route is chosen more frequently, its cost is
increased and vice versa. With this mechanism, the network can adapt to
changes in user demands and effectively allocate resources. Experimental
results show that the proposed framework increases the user acceptance ratio
up to roughly 15% under different numbers of users and 20% under different
numbers of servers. Taking another approach, an automatic network slicing
framework for ISTNs is presented in [69]. The proposed framework aims to find
the satellite-gateway assignments and resource allocation to optimize the
overall utility, e.g., throughput and latency, of network slices. To that end,
a Voronoi tessellation-based topology construction mechanism is proposed to
map the satellite constellations to equivalent network topologies. Based on
that, an MILP is formulated. To reduce the complexity of the MILP problem, the
optimization problem is decomposed into a resource allocation and a satellite-
gateway assignment sub-problems. An online iterative algorithm is then
developed to solve the two sub-problems. Simulation results show that the
proposed framework can increase the slice admittance rate by up to 23% and
reduce the control traffic by 76% compared to the standard SDN approach.

###  III-C Space-Air-Ground Integrated Networks

Space-Air-Ground Integrated Networks (SAGINs) aim to achieve full network
coverage and ubiquitous services by integrating terrestrial networks,
satellite networks and aerial networks[70]. While applying network slicing to
SAGINs is an effective solution for efficient usage of network resources, this
technique faces new challenges such as complex slice orchestration in multi-
domain networks, resource optimization considering UAVs position, and
dispatching cost. To address those challenges, multiple innovative schemes
have been proposed.

####  III-C1 Resource Allocation

For instance, as the main concern of network slicing application, resource
allocation is discussed in [71, 72, 73, 74]. Particularly, in [71], the
authors propose a framework for integrating network slicing to a SAGIN, which
establishes three types of RAN slices, i.e., high-throughput, low-delay, and
wide-coverage. In particular, a non-scalar multi-objective optimization
problem (MOOP) is formulated to jointly optimize throughput, service delay and
coverage area. Moreover, a Central and Distributed Multi-agent Deep
Deterministic Policy Gradient (CDMADDPG) algorithm is developed to solve the
problem. This CDMADDPG algorithm uses a centralized unit to determine the
optimal positions for the virtual UAVs (vUAVs) and the most suitable
subchannels as well as power resources among the slices. Then, intra-slice
resource sharing is arranged by virtual base stations, vUAVs, or virtual LEO
satellites, depending on the distributed units. Eventually, near-Pareto
optimal solutions can be found. Simulation results show that, compared to a
baseline approach, the proposed framework can improve throughput and delay by
up to 10% and 50%, respectively. With a focus on dynamically slicing spectrum
resource in SAGINs an online control framework is proposed in [72]. Here, the
proposed framework aims to adapt to varying vehicular environments and achieve
isolated service provisioning, i.e., each type of service is processed in an
independent queue. To that end, the authors propose a workflow of dynamic
slicing consisting of four steps, i.e., request admission, request scheduling,
UAV dispatching, and resource slicing, to ensure that services with different
QoS are adequately served. Based on that, a Lyapunov-based approach is
proposed to maximize the system revenue and minimize a time-averaged penalty
while stabilizing the system. This approach aims to minimize time-averaged
queue backlogs of all services, i.e., the average number of unprocessed
requests in a queue over a period. Simulation results show that the proposed
framework can increase the throughput by approximately 26%.

Taking another approach utilizing AI, the authors in [73] introduce an AI-
enabled network slicing architecture for 6G networks, aiming to enable
intelligent network management and facilitate emerging AI services.
Particularly, the architecture has two main characteristics: AI for slicing,
i.e., using AI to manage multiple network slices with strict QoS requirements,
and slicing for AI, i.e., creating special slices for AI services. It is shown
in [73] that AI can be used to support different phases of the network slicing
process such as preparation (e.g., service demand prediction and slice
admission), planning (e.g., VNF placement and resource reservation), and
operation (e.g., resource orchestration and Radio Access Technology
selection), as illustrated in Fig. 11. Moreover, in the proposed architecture,
network slices are tailored to support three stages of AI services (including
data collection, model training and model inference) with their corresponding
required QoS. Simulation results show that a deep deterministic policy
gradient (DDPG)-based network slicing solution has a system cost (a weighted
sum of resource reservation cost, slice reconfiguration cost, and delay
requirement violation penalty) 15% lower than that of myopic resource
reservation [73].

Figure 11: AI based network slicing solution [73].

Considering the current status of power communication network development, the
authors in [74] propose a SAGIN slicing architecture. Specifically, the
proposed architecture consists of four layers, i.e., service request, slicing
management and scheduling, virtualized resource, and infrastructure layer. The
service request layer receives requests from users to define requirements and
build service slices. The slicing management and scheduling layer provides the
management interface for users, controls the scheduling management, stores the
data required by the platform environment and transmits the corresponding data
to the lower functional components. The virtualized resource layer utilizes
NFV to virtualize the physical resources and allocates sufficient virtual
resources to different slices. The infrastructure layer provides the physical
resources. It is shown in [74] that this architecture can manage network
resource allocation efficiently to meet different demands of the power grid
communication service.

####  III-C2 Slice Configuration and Routing

Together with resource allocation, multiple aspects are considered to enhance
network performance, including slice configuration, Service Level Agreement
(SLA) decomposition, and routing. A multi-domain network slicing framework is
proposed in [75], aims to jointly optimize all the three aspects. It is shown
in [75] that the proposed framework can handle multiple slice configurations
in satellite-terrestrial edge computing networks (STECNs). Particularly, a
Markov process is modeled to track the probability of satisfying the SLA per
configuration. Moreover, an index-based slice configuration policy, based on
restless multi-armed bandits (RMABs) [76] [77], is defined to solve the multi-
domain slicing problem. Based on the slice configuration policy and the
slice/resource availability, the SLA is decomposed. The routing and resource
allocation optimization problem is solved by each domain controller with
consideration of the slice/resource availability. Simulation results show that
the proposed framework can achieve three to six times higher rewards than
those of baseline approaches in terms of user requirements in the SLA and the
energy consumption. It is also shown in [75] that the proposed framework can
achieve optimal configuration under different network conditions, e.g., outage
and delay.

In [78], the authors propose a scheme to integrate network slicing to SAGINs
with native AI. In particular, under the assumption that all network entities
are software-defined and can load multiple network functions and take any
required roles, all network entities are classified into three classes, i.e.,
end entity, routing entity, and management as well as orchestration entity.
End entities are communication sources and destinations. Routing entities are
responsible for access control, traffic routing, and QoS management.
Management and orchestration entities are responsible for entity management,
network slicing management, and resource orchestration. A pre-defined
operating entity [78], aware of network characteristics, adopts the
Distributed Weighted Classification Method (DWCM) to assign roles to all
network entities. Experimental results show that, compared to a baseline
approach [78], the proposed scheme can increase the delivery ratio and the
overall data rate by roughly 4% and 28%, respectively and reduce the end-to-
end delay and routing overhead by approximately 46% and 6%, respectively.

Summary: In this section, we have discussed multiple approaches for network
slicing integration in NTNs, e.g., satellite networks, ISTNs, and SAGINs.
Specifically, network slicing is an effective solution to enhance NTNs with
better delay, throughput, network resource utilization, network flexibility,
and scalability. However, multiple challenges arise when applying network
slicing to NTNs such as resource allocation, routing, load balancing, slice
admission control, and SLA decomposition. Among those challenges, resource
allocation is the most noticeable concern. To address resource allocation, one
approach is to formulate an optimization problem, which can be solved by
AI/ML-based methods, mathematic algorithms, scheduling strategies, or
innovative mechanisms. Additionally, optimizing VNE is effective in enhancing
resource allocation. Another challenge to network slicing is routing, which
can be solved by shortest-path approaches. Although the proposed approaches
can effectively address various challenges of network slicing in NTN, there
are still open issues that stem from the complexity of network slicing
optimization problems. Particularly, these problems often include numerous
constraints due to resource limitations and various platforms, e.g., space,
air, and ground, resulting in high complexity. As a result, most of the
proposed approaches do not consider network slicing holistically or are based
on simplified assumptions. Therefore, additional effort is needed to develop
effective and holistic solutions for network slicing in NTNs, especially the
integration of NTNs and terrestrial networks. The works surveyed in this
section are summarized in Table II.

TABLE II: Summary of network slicing approaches in NTN. Reference | Network Type | Problem | Technique
---|---|---|---
[49] | Satellite Network | Resource allocation | NN-based model
[50] | Resource allocation | RL-based slicing strategy
[51] | Resource allocation | Managing slice through NSSAI and NSSP
[52] | Resource allocation | MILP
[48] | Routing | Dijkstra’s algorithm
[53] | Routing | Develop new shortest path algorithm
[47] | Satellite edge computing | SE-MOTS, golden-section method
[46] | Satellite edge computing | 5G SNSM
[57] | Slice admission control | Simplifying robust optimization problem using auxiliary variables
[59] | ISTN | Resource allocation | SAST-VNE
[58] | Resource allocation | Testbed design
[62] | Resource allocation | R2A2
[63] | Resource allocation | Multi-Sided Ascending-Price Auction Mechanism
[64] | Resource allocation | MINLP
[28] | Resource allocation | End-to-end slicing model
[65] | User assignment | Hybrid scheduling strategy
[41] | User assignment | PPO-based RL algorithm, matched-based algorithms
[66] | User assignment | Smart scheduler
[68] | Routing | ACO
[69] |  | Routing | MILP
[71] | SAGIN | Resource allocation | CDMADDPG
[72] | Resource allocation | Lyapunov-based algorithm
[73] | Resource allocation | Managing slice by AI
[74] | Resource allocation | Scheduling
[75] | Slice configuration and routing | RMABs
[78] | Slice configuration | DWCM

##  IV AI/ML-aided NTN

As people continue to explore and establish more practical applications
throughout the Earth, there exists a growing need for advanced technologies to
support NTN. AI/ML-aided NTN offers a guarantee for significant benefits in
this area, from improving communication efficiency for IoT systems to enabling
autonomous UAV operations in huge and remote areas. Specifically, AI/ML can
increase efficiency and reliability to optimize communication protocols using
NTN. They can also enable the autonomous operation of numerous wireless
applications, thereby reducing the need for constant human intervention.
Finally, AI/ML can facilitate new scientific discoveries and insights by
enabling more efficient and targeted data analysis using NTN. As a result, NTN
can generate vast quantities of data, and AI/ML may help to extract valuable
insights and enable better-informed decisions in a variety of practical
applications, as discussed in the following.

###  IV-A IoT

The utilization of AI and ML in NTN has the potential to revolutionize IoT
systems by improving data collection and enabling extensive communication for
sensors located in remote and broad areas. NTN may increase radio coverage,
provide monitoring, and incorporate sensing services to remote locations by
utilizing satellites, airships, and aircraft as illustrated in Fig. 12. This
can lead to significant improvements in data collection from areas that were
previously inaccessible, allowing for more comprehensive and accurate
insights.

Figure 12: The integrated air-ground and NTN for IoT systems.

In [79], the authors investigate the potential use of DL for NTN-based
industrial IoT (IIoT) services, i.e., spaceborne-based and airborne-based
intelligent IIoT systems. This research describes how diverse DL-based
algorithms have been used and tested for NTN-based IIoT applications based on
the optimization objective, the available power, and processing resources.
Here, DL is considered as the most effective AI algorithm when it comes to
end-to-end optimization. Specifically, for spaceborne applications, an
ensemble DNN-based optimization can be utilized for resource allocation while
a deep-Q network can be used for energy consumption and processing latency in
Satellite-IoT (S-IoT) networks. Additionally, S-IoT edge computing and DL
methods can accelerate data processing and transmission as well as enhance
bandwidth utilization. Since the practical viability of conventional DL is
contingent on the availability of a large amount of sensor data and sufficient
computing power, DRL with lower complexity can be used. For airborne
applications, an IoT network with three layers for online large data
processing based on MEC is proposed with UAVs as edge servers, Lyapunov
optimization, DRL algorithm, and CNN Q-networks. In this case, the CNN
Q-network, which enables action reward prediction, is trained using the UAVs’
views of the surrounding environment, and the DRL can efficiently optimize the
path planning of the UAVs.

To provide more benefits of using ML/AI-aided NTN, the authors in [80]
categorize the NTN into integrated UAV-IoT networks and integrated S-IoT
networks. For integrated UAV-IoT networks, an RL method can be applied to
build the data collection trajectory of a UAV from IoT devices with the aim to
increase the UAV’s flight duration. Using a double deep Q-network strategy, an
effective path to maximize collected IoT data under flight time and obstacle
avoidance limitations can be designed. Then, to increase the IoT network’s
energy efficiency, a DRL strategy is employed to optimize the channel and
power allocation of IoT devices in the uplink communication. Meanwhile, a DRL
approach can be adopted in the integrated S-IoT networks. In this case, an
energy-efficient channel allocation mechanism for LEO S-IoT network can be
implemented. The DRL approach can also be employed to address the complexity
of energy cost and latency minimization influenced by IoT user association and
resource allocation.

Despite that NTNs, especially the ISTNs, can provide wide coverage for IoT
systems, their links still suffer from high dynamics and latency. To address
the problem, several ML/DL approaches are surveyed in [81], aiming at
optimizing the routing strategy update. Specifically, traffic patterns of the
integrated network are learned using CNN, resulting in routing paths that
balance traffic. Then, a deep Q-learning-based method is proposed to reduce
the routing delay in the integrated network by combining both satellite and
terrestrial users’ networking, caching, and computing resources. Additionally,
the ISTN has considerable spectrum-sharing potential, and thus two spectrum-
sharing systems based on SVM and CNN are presented. Here, the intelligent
spectrum sharing reduces interference and increases spectrum efficiency
compared to traditional approaches. As the satellite covers a large area, it
is important for IoT devices to be accurately positioned to ensure that the
service stays up and runs in the integrated network.

###  IV-B Mobile Services

Figure 13: NTN with moving NT-BSs and NT-users.

Since NTN consists of space-borne base stations (BSs), e.g., satellites, and
airborne BSs, e.g., UAVs and aircraft, they may lead to a dynamic and non-
stationary environment. As such, non-terrestrial users (NT-users) on the
ground need to predict the mobility of those non-terrestrial BSs (NT-BSs)
autonomously. Likewise, NT-users may move frequently, e.g., drones, which make
the trajectory estimation from the NT-BSs side challenging (as illustrated in
Fig. 13).

To cope with those issues, the work in [82] and [83] propose new ML/AI-aided
NTN, aiming at supporting the activities of NT-BSs and NT-users. In [82], the
authors introduce an NT-users-driven DRL approach for handover and throughput
optimization in NTN-based multi-user access control. Specifically, a
centralized agent on the backhaul side of NT-BSs trains DQN parameters with a
sigmoid activation function. The trained DQN is then used by each NT-user with
slow mobility, e.g., smartphone user, to make his/her own access decisions.
The proposed technique allows each NT-user to intelligently access a suitable
NT-BS to improve long-term system throughput and reduce the NT-BSs’ handovers.
Compared with other benchmark methods, e.g., RSS-based and Q-learning
algorithms, the proposed framework is superior in terms of the long-term
throughput and the handover numbers by 6 times and 8%, respectively.

The above work is then extended with the existence of NT-users with
medium/high mobility, i.e., drones in low-altitude or high-altitude, by the
authors in [83]. In this case, each NT-BS should autonomously forecast NT-
users’ trajectories and the likelihood of their presence at any site. For
that, instead of using the aforementioned complex DRL method, this work
proposes novel RL systems in which each of many NT-BSs independently
calculates deployment trajectories to maximize the access of NT-users. For the
deployment trajectory, multiple NT-BSs can apply _k-step_ state reduction (SR)
distributive Q-learning to optimize the autonomous trajectory. Via simulation
results, the suggested approaches outperform Q-learning, maximal SINR, and
distributive DRL in terms of the average number of served NT-users up to 47%.

###  IV-C Network Management

One of the NTN communication systems, i.e., the satellite communication
networks, typically has a challenge in terms of system capacity, in which the
resources of most satellites are usually underutilized. To address this
challenge, collaboration among different satellite systems can be one of the
most effective solutions to enhance resource utilization. Currently, each
satellite has an unconnected system architecture and dedicated resource
utilization. For that, ML/AI approaches can be used to cope with the above
limitations by developing intercommunication frameworks among different
satellite systems.

The authors in [84] propose a resource management framework in heterogeneous
satellite networks. In the framework, SDN and virtualization methods in the
data center are used to manage and combine disparate resources as shown in
Fig. 14. To obtain the optimal resource utilization, a DRL approach that uses
Markov decision process (MDP) and integrates DL for inference capability as
well as RL for decision-making potential is then applied. In this work, the
state space contains service and resource states, whereas the action state
includes all actions in which an agent provides resources to the services.
Additionally, the reward may include spectral efficiency, bandwidth,
throughput, and power efficiency. Here, each satellite acts as a smart multi-
agent that can perform distributed data processing and transmit information
between satellites and/or ground stations in the cloud system. This reduces
the workload of the data center and enhances the efficiency of communication
because of shorter transmission paths. The SDN-based integrated networks for
resource allocation are also proposed in [85]. The purpose of this integrated
network is to monitor networks, computation resources as well as caches, and
orchestrate them all simultaneously. They use Markov decision process for the
resource allocation optimization problem, and deep Q-learning approach for the
problem solving. The simulation results reveal that the proposed scheme can
achieve the highest expected utility per resource up to 10 times compared with
other baseline methods.

Then, the work in [86] presents a novel framework of "self-evolving networks
(SENs)," which employs AI through ML algorithms, e.g., federated learning and
online learning, to fully automate and intelligently evolve future integrated
NTN in terms of network management, communication, computation, and mobility
of mobile users. For that, the authors utilize the intelligent vertical
heterogeneous network (I-VHetNet) architecture as a model to envision the idea
of SEN in future integrated networks. The I-VHetNet design not only combines
terrestrial, aerial, and satellite networks, but also includes intelligence,
computation, and caching platforms to allow multi-level edge computing. In
particular, the SEN engine first employs AI to forecast where additional
network capacity and coverage are required based on user movement, behavior,
and applications. Utilizing the prediction result, the SEN engine
intelligently and automatically sends UAV-BSs or adjusts a HAPS beam to
enhance network coverage and increase its capacity to serve customers. It then
can choose terrestrial, aerial, or satellite networks to backhaul UAV-BSs
based on the QoS requirements. The SEN engine can also execute computational
offloading to the best computational level systems, e.g., cloud computing, fog
computing, or collaborative computing of mobile users. Afterward, the SEN
engine can monitor network performance and user satisfaction. To adapt to
dynamic changes and create more accurate automated and intelligent decisions,
the SEN engine uses network environment assessment as feedback. From the
simulation using three data centers and 300/3000 users, the proposed system
can minimize the data offloading and computing delay by 0.05 seconds with
10GB-10TB offloaded data per user.

To further relax the network management and service-oriented resource
allocation of the integrated terrestrial and NTN in B5G/6G networks, network
slicing can be used through leveraging AI-based approaches. In [87], dynamic-
adaptive AI-enabled network slicing management to deal with dynamic wireless
environments is discussed. Specifically, to allow intelligent orchestration of
optimization problems in network slicing management, several AI-based
approaches can be applied. However, it is worth noting that using conventional
DNN, CNN, or DRL methods may suffer from a slow convergence rate, and thus it
can deteriorate the learning performance especially when parameters change
fast dynamically. To this end, transfer learning and meta-learning can provide
a fast response with fewer samples. Both learning methods benefit from
transfer/meta knowledge without requiring data training from scratch to solve
the new learning problem with less training data. The use of RL then can speed
up the convergence in re-training and improve the re-fitting ability. Based on
the case study, the proposed transfer and meta-learning framework can minimize
the loss and cost of slices up to 0 and 0.7 overtime, respectively, in two
typical dynamic schemes, i.e., bursty traffic and devices’ arrival/departure
in slices. A similar network slicing method to guarantee different QoS levels
according to users’ requirements for eMBB in 5G-satellite networks is proposed
in [88]. Particularly, the authors introduce a neural network-based resource
allocation optimization problem to satisfy different QoS requirements. Here,
the work adopts the neural network with a weighted round-robin scheduler (WRR-
NN) since a multi-queue system for packet fetching is utilized, aiming at
meeting the delay requirement for average end-to-end packet delivery delay.
Based on the comprehensive simulations, this technique can precisely follow
system dynamics and satisfy eMBB’s service latency and jitter criteria at
approximately 0.025s and 10-50ms, respectively.

The addition of privacy for resource allocation in integrated networks can
also be implemented. The authors in [89] present a distributed federated
learning (FL)-based intrusion detection system (IDS) in the integrated
terrestrial networks and NTN, aiming at addressing limited satellite network
resources and high privacy requirements. As such, the FL is used to allocate
resources adequately in each domain and block malicious traffic, including
DDoS attacks. Here, the FL only sends the trained model without raw data
sharing to preserve privacy. This solution outperforms DL-based IDS for
malicious traffic identification rate at 98%, packet loss at 0%, and CPU
consumption rate at 70%.

Figure 14: The communication between satellites and ground stations using
software-defined network and virtualization.

###  IV-D Vehicular Networks

In terms of vehicular networks, the integration between terrestrial and NTN
has the potential to bring efficient, reliable, and robust data transmission
for medium/high speed or delay-sensitive/delay-tolerant services. This can
reduce the dynamic network environment including handover and unstable network
connection problems.

For example, the authors in [90] apply multipath transmission control protocol
(MPTCP) congestion control mechanism to perform data transmission through the
integrated networks concurrently for high-speed railway schemes. Nonetheless,
due to frequent handover problems, they utilize reference signal received
power information according to a DRL approach to boost the goodput (i.e., the
amount of successful data received by the recipient within the deadline)
performance. Particularly, the integration among gated recurrent unit (GRU),
CNN, and deep deterministic policy gradient (DDPG) are used to build a
learning model. First, the representation network generates a network state
representation for the actor and critic networks to develop congestion control
actions. Second, the actor network can derive the congestion control actions
according to the observed state. Meanwhile, the critic network can assess
congestion control actions performance to further optimize the policy
gradients and update the actions. Finally, the experience memory stores
congestion control state, action, reward, and next state sequences. Random
sampling from saved experience sequences then trains the representation,
critic, and actor networks, thereby reducing data reliance. Through
simulations using static and high-speed mobile scenarios, the proposed
solution can achieve the highest goodput up to 63% compared with other
baseline algorithms.

Next, a joint resource slicing and scheduling problem to minimize the system
cost in a long-term scenario in the integrated space-terrestrial vehicular
networks is investigated in [91]. Here, the system cost includes the delay-
sensitive service cost, delay-tolerant service cost, and slice configuration
cost. To find the solution, a two-layer RL-based approach is used, as
illustrated in Fig. 15. In the resource slicing layer, a proximal policy
optimization-based RL method pre-allocates spectrum resources. Meanwhile,
matching-based algorithms assign spectrum resources in each slice to each
vehicle based on dynamic network circumstances and service requirements in the
resource scheduling layer. From the trace-driven experiments, the proposed
framework can efficiently minimize the system cost by 98% while meeting
service quality standards, compared with the proportional slicing scenario.

Then, the authors in [92] foresee 6G convergent terrestrial and non-
terrestrial networks of virtual emotion and pandemic prevention from two
perspectives that are Red AI for accuracy and Green AI for efficiency.
Specifically, the Red AI-enabled 6G virtual emotion approach leveraging DL
algorithms can be used to detect specific emotions of humans passing through a
specified area using vehicles with high accuracy and low delay. Furthermore,
the Red AI-enabled 6G epidemic prevention approach is utilized to provide
epidemic services including fast medical item delivery and epidemic prevention
map construction using autonomous vehicles with smart devices on the ground.
Meanwhile, Green AI-enabled 6G virtual emotion focuses on computation cost
reduction in detecting emotion with pre-defined accuracy. For Green AI-enabled
6G epidemic prevention using DL algorithms, it provides the same services as
the Red AI however with the minimum data and reduced number of training
processes and communications, thereby maximizing the efficiency. All the above
are supported by the NTN via 6G communications.

Figure 15: The resource slicing and scheduling in the integrated space-
terrestrial vehicular networks.

###  IV-E UAV

In the next decade, B5G and 6G wireless networks that include terrestrial
networks and NTN will be heavily reliant on UAVs for a variety of purposes
[93]. For that, wireless communication must be reliable, trustworthy, and
inexpensive to support such huge UAV deployments. Through unlimited
connectivity in 6G, UAVs for commercial use can be used widely at different
altitudes from low to high ones, e.g., delivery, surveillance, traffic
control, and aerial imaging.

The authors in [94] investigate antenna tilt deployment optimization of air-
to-ground (A2G) network between terrestrial base stations and UAVs using a DL
method, aiming at maximizing users’ throughput in the air. In particular, bi-
DNN is employed to approximate the behavior of the A2G network and decide the
optimal network configuration. The optimal solution can be achieved by
considering inter-site distance, number of antenna sectors, UAV altitude, base
stations’ locations, and traffic load. Through the experiment, optimal antenna
tilt angles decrease by up to 30 degrees as inter-site distance increases
between 20km and 80km to ensure adequate coverage throughout the cell.

In [95], UAV-based low altitude platform systems (LAPS) with ML/DL approaches
to create a flying ad hoc network (FANET) is discussed. Specifically,
Q-learning-based RL can be used to develop autonomous and adaptive packet
routing among UAVs. In addition to Q-learning, SVM and logistic regression can
support real-time and dynamic resource allocation to provide robust UAV
services. The combination of Q-learning and FL, i.e., federated Q-learning can
also be utilized to protect the FANET through jamming detection.

To further optimize packet forwarding between distant ground terminals, the
integration between LEO satellites and UAVs to provide seamless relays using
radio frequency (RF) or free-space optical (FSO) links is presented in [96]
(as shown in Fig. 16). Here, the authors propose multi-agent DRL to optimize
the relationship between orbiting LEO satellites and UAVs’ trajectories by
maximizing the overall throughput of communication over long distances while
reducing the system’s energy consumption. The environment includes multi-hop
communication, while the state includes LEO satellites’ position in two
orbital planes, the UAV’s position, the link distance for each RF/FSO link,
the UAV’s energy consumption, and the time slot. The actions contain
associations and accelerations, while the reward function optimizes the
actions that maximize throughput and minimize energy consumption and distance
between UAVs. Through simulations, it is shown that the proposed framework can
obtain throughput two times higher than that of a baseline method with fixed
ground relays. Furthermore, the energy efficiency can be improved by 2.25
times compared with the baseline method.

Figure 16: The integration between LEO SATs and UAVs with RF/FSO links for
packet forwarding.

###  IV-F Maritime Services

Seamless marine connection service is becoming a reality as a result of recent
advances in merging high-capacity and ultra-reliable integrated terrestrial
network and NTN technologies. Here, NTN can boost terrestrial system coverage
and enable access to marine services in offshore and non-line-of-sight (NLoS)
environments. To deal with the rising complexity of controlling these
interconnected systems, ML/AI-based approaches can be applied, aiming at
achieving the service needs and energy efficiency objectives in diverse marine
communication conditions.

For example, the use of ML/AI-based methods to provide sustainable 6G maritime
networks through NTN is investigated in [97]. Particularly, the most potential
maritime scenarios are first described including maritime search and rescue,
intelligent harbor and vessel logistics, on-board entertainment, navigation
and fleet management, and shipborne IoT. In this case, distributed
intelligence, wherein learning and inference are utilized at several system
levels, is required to cope with the dynamic networking. Then, the authors
show how ML may improve energy-efficient topology management and scheduling in
dynamic marine networks over baseline model-based techniques. For the energy-
efficient topology management, a multi-hop wireless network with the aid of
NTN is deployed in the area of 100 km2 to reduce energy usage of the network
while finding the optimal routing paths for heterogeneous traffic delivery to
multiple destinations, e.g., cargo vessels. For that, the DNN-based DL
approach is utilized to estimate which connections are absolutely necessary
for the best possible configuration as the system’s traffic patterns change.
As a result, the number of involving connections in the routing and execution
time can be minimized. Additionally, the temporal and geographical
correlations in traffic may be discovered and multi-objective optimization
issues can be supported by employing other promising methods such as LSTM and
auto-encoder. For the energy-efficient scheduling, the LSTM approach can be
applied to predict channel quality because of its ability to deal with time
series problems. Consequently, the channel reporting accuracy can be improved
and packet delay can be reduced at a fixed 2 seconds for a user density of
more than 2 users/m2.

###  IV-G Other Applications

Aside from all the above applications, ML/AI-aided NTN scenarios have been
investigated for other emerging applications. For example, a cybertwin-enabled
6G for SAGIN using FL is discussed in [98]. Specifically, accounting for non-
homogeneity of SAGIN, mobile users that are served by different RANs can
offload their local data independently to train them at the respective RANs as
shown in Fig. 17. In this case, the cloud server can work as the trained model
aggregator and global model updater. Here, MNIST dataset is used in the
cybertwin space of the SAGIN. Using additional helpers from SAGIN as the edge
nodes, i.e., LEO satellite for the space network, a UAV for the air network,
and a base station for the ground networks, an FL training process can be
conducted. From the simulation results, it is shown that the satellite network
suffers from the slowest training time and convergence rate due to the large
communication delay between the mobile users and the satellite. The
convergence gets faster when UAV-based networks and base station-based
networks are utilized. Here, the accuracy for SAGIN can reach up to 90%. To
further boost the accuracy, the combination of all the edge nodes and mobile
users can be used for the FL training processes.

Figure 17: Federated learning using multi-level network architecture, i.e.,
space-air-ground networks

Due to the unique features of SAGIN, e.g., time-varying connectivity, diverse
resources, and complex 3-level network design, an adaptive data transmission
mechanism needs to be investigated. In [99], a DRL-based intelligent adaptive
transmission framework for SAGIN is proposed, aiming at maximizing the system
throughput while satisfying the packet delay and reliability standards of the
traffic flow. Particularly, the authors first formulate a mixed-integer
stochastic optimization problem. Then, a re-parameterization method based on a
deep deterministic policy gradient (RPDDPG) algorithm is utilized to solve the
problem. From the numerical results, the RPDDPG algorithm is effective in
improving the throughput and outage probability compared with the relaxation-
based DDPG and heuristic algorithms. Likewise, the authors in [100] propose a
DL-based approach to enhance the traffic control performance of the SAGIN.
Particularly, several GEO, MEO, and LEO satellites as well as hundreds of UAVs
are first considered. Then, online training including data collection and the
training process using a CNN method is performed. From the trained model, a
routing strategy to forward packets to a specific destination and select the
optimal traffic path can be executed. From the simulations, the proposed
method can significantly boost the network throughput by 9% over 500 episodes,
compared with the conventional routing strategy.

In [101], a clustering-based users’ scheduling problem using their
characteristics in high-throughput and multibeam precoded GEO satellite
systems is investigated. Particularly, the work considers three clustering
algorithms for unsupervised learning including K-means, hierarchical
clustering, and self-organization. Each of the algorithms is evaluated as a
function of users’ feature vectors which contain location and channel
information. From the numerical results, it is observed that the channel
information of the user improves the clustering and scheduling performance
depending on per-beam clusters and the number of multicast users. The
numerical results show that the K-means and hierarchical clustering can
achieve an average throughput of 0.01Gbps higher than that of the self-
organization method for 24 to 32 clusters per beam.

_Summary:_ In this section, we have discussed the ML/AI-aided NTN for various
emerging applications/services, e.g, IoT systems, mobile services, network
management, vehicular networks, UAVs, maritime services, cybertwin, and
adaptive data transmission. Particularly, ML/AI approaches have emerged as one
of the most potential solutions in dynamic and mobile environments where the
NTN radio access network, e.g., satellites, frequently move. Additionally,
ML/AI-based NTN can support integrated networks where the deployments of
satellites on multiple orbits for space networks, UAVs/drones for aerial
networks, and base stations for ground networks exist in such a highly dynamic
environment. Here, we can observe from the above discussion that DL, RL, FL,
and the combination among them can take part successfully to help NTN
implementation for various services. First, the DL method is popular for end-
to-end optimization, e.g., resource allocation, channel estimation, and
scheduling. In this case, DNN with deep Q-network or the joint DL and RL
approaches, i.e., DRL, can be utilized to reduce the complexity of the data
training processes. Meanwhile, the use of CNN can be used to optimize the
routing strategy of data traffic in the integrated networks. All the
aforementioned ML/AI techniques adopt the centralized ML approach in which all
radio accesses in the integrated network send their data to a cloud server.
Alternatively, the FL approach can be used to provide a decentralized ML
approach, where satellites, UAVs, ground base station, and mobile users can
train their local data individually and then share the train models with the
cloud server. Nonetheless, the utilization of ML/AI for NTN also faces several
drawbacks including the increasing complexity of the system, the use of huge
amounts of data, and longer training duration. Hence, future research to
minimize these problems should be investigated. The summary of all ML/AI-aided
NTN for various applications is presented in Table III.

TABLE III: Summary of ML/AI-aided NTN for Various Emerging Applications Application |  Service |  ML/AI method |  Refs
---|---|---|---
|  |  |
IoT |  Resource allocation, energy consumption, trajectory optimization |  DNN, DRL, CNN |  [79]
|  Power and channel allocation, latency minimization |  DRL |  [80]
|  Routing strategy optimization, spectrum sharing |  CNN, DQN, SVM |  [81]
Mobile services |  Handover and throughput optimization |  DRL |  [82]
|  Trajectory forecast |  SR distributive Q-learning |  [83]
Network management |  Resource utilization and allocation |  DRL |  [84, 85]
|  Network capacity and coverage prediction |  FL, online learning |  [86]
|  Network slicing |  Transfer and meta learning |  [87]
|  Network slicing and resource allocation |  WR-NN |  [88]
|  Resource allocation |  FL |  [89]
Vehicular networks |  Congestion control |  DRL, CNN |  [90]
|  Resource slicing and scheduling |  RL |  [91]
|  Virtual emotion and pandemic prevention |  DL |  [92]
UAVs |  Antenna tilt deployment optimization |  Bi-DNN |  [94]
|  Autonomous and adaptive packet routing, dynamic resource allocation, jamming detection |  RL, SVM and logistic regression, Federated RL |  [95]
|  Packet forwarding |  DRL |  [96]
Maritime services |  Energy-efficient topology management and scheduling |  DNN, LSTM |  [97]
Other applications |  Cybertwin-based image classification |  FL |  [98]
|  Adaptive data transmission |  DRL |  [99]
|  Optimal routing path |  CNN |  [100]
|  Clustering-based scheduling problem |  K-means, hierarchical clustering, and self-organization |  [101]

##  V ORAN-aided NTN

Figure 18: ORAN architecture TABLE IV: ORAN interface notations Interfaces |  Description |  References
---|---|---
E2 |  This interface establishes a connection between the near-RT RIC and multiple O-CU-CPs, O-CU-UPs, and O-DUs. |  [102]
E1 |  Interface linking the SMO with O-RAN managed elements |  [102]
O1 |  The interface facilitating communication between management entities (NMS/EMS/MANO) and O-RAN managed elements enables operational and managerial functions. This encompasses FCAPS management (fault, configuration, accounting, performance, and security), software management, and file management |  [102]
O2 |  Interface between the SMO and the O-Cloud |  [103]
A1 |  The interface linking the non-RT RIC and Near-RT RIC facilitates policy-driven guidance of Near-RT operations |  [103]
F1-u |  F1 User Plane interface |  [104]
F1-c |  F1 Control Plane interface |  [104]
NG-c |  The next generation (NG) control plane interface is defined between a NG-RAN node and a 5GC |  [105]
NG-u |  The NG user plane (NG-U) interface is established between an NG-RAN node and a UPF. It ensures the non-guaranteed delivery of PDU Session/MBS session user plane PDUs between the NG-RAN node and the user plane function (UPF). |  [105]
Xn-u |  The Xn user plane (Xn-U) interface is established between two NG-RAN nodes. It facilitates the non-guaranteed delivery of user plane PDUs between these two NG-RAN nodes |  [106]
Xn-c |  Reference point for the control plane protocol interconnecting NG-RAN nodes |  [106]
X2-c |  Control plane interface for the exchange of application-level configuration data required for seamless interoperability between eNB and en-gNB |  [107]
Figure 19: ORAN-aided NTN architecture for the scenario gNB on Earth.  Figure
20: ORAN-aided non-terrestrial network architecture for the scenario gNB on
the satellite.  Figure 21: UAV trajectory path design based on ORAN-aided UAV
radio resource allocation [108].  Figure 22: Flow Diagram for UAV trajectory
path design based on ORAN-aided UAV radio resource allocation [108].

As open networks move the Radio Access Network (RAN) to the cloud, an
intriguing alternative is venturing into space. The concept of Non-Terrestrial
Networks (NTN) has garnered increasing attention, with numerous experts
asserting that this innovative approach holds the potential to extend 6G
coverage ubiquitously [109]. Nevertheless, there is currently a scarcity of
research in this domain. Consequently, this section delves into the potential
utilization of ORAN (Open RAN) for Non-Terrestrial Networks (NTN). We commence
by examining the advantages of ORAN, followed by the presentation of a
prospective ORAN application architecture tailored for NTN.

###  V-A Benefits of ORAN for 5G and beyond

ORAN plays a crucial role in 5G and beyond since it provides several benefits
as follows:

  * •

Cost reduction and network resiliency: ORAN has the potential to reduce costs
for operators by opening the RAN ecosystem to multiple vendors. Therefore,
operators have more options to select cost-effective solutions suitable to
their specific requirements. ORAN also leverages off-the-shelf hardware and
cloud-based deployment, thereby lowering costs. This diverse vendor ecosystem
not only saves costs but also enhances network resiliency. By using equipment
from different vendors, operators can reduce the risk of being dependent on
one vendor and better protect their networks against service disruption or
potential vulnerabilities.

  * •

Scalability, flexibility, and interoperability between vendors: ORAN allows
operators to easily scale up/down their networks through disaggregation and
hardware and software supply chain decoupling. Hereby, disaggregation refers
to the separation of traditional monolithic RAN components such as baseband
processing, radio functions, and control functions, into independent and
functional entities/modules that can be developed, deployed, and operated
independently. For example, the control unit (CU) is responsible for
centralized functions, i.e., baseband processing, control plane processing,
and network management functions. The distributed unit (DU) is responsible for
radio functions such as physical layer processing (PLP), radio frequency
processing (RFP), and connection management. By disaggregating the RAN
components, ORAN offers flexibility and vendor diversity to the
infrastructure. Therefore, the operator can select the best solutions from
different vendors for each functional component, promoting competition and
innovation in the market. It also leverages interoperability between different
vendors’ devices, and thus, it is easier to integrate and upgrade network
components.

  * •

Innovation, forward-looking, and technology convergence: ORAN encourages
innovation by allowing start-ups and niche vendors to introduce their novel
solutions and technologies into the RAN domain. It fosters a vibrant ecosystem
that helps drive advancements in VR, MEC, AI, digital twins, semantic
communications, haptic communications, and network automation. ORAN
architecture interfaces can facilitate the integration and interoperability of
the above-mentioned technologies.

  * •

Security enhancement: With ORAN disaggregated architecture, security checks
and verifications can be performed more efficiently. Moreover, operators can
analyze and evaluate the security of each component, including CUs and DU.
This level of granularity allows for better identification and mitigation of
potential vulnerabilities. Another reason for the security enhancement is that
ORAN supports network slicing, allowing operators to create virtualized
networks dedicated to specific use cases. Each network slice can have its
security policies and mechanisms, creating separate security zones. This
allows for custom security configurations for different services and
applications, ensuring that appropriate security levels are maintained based
on the specific requirements of each slice.

###  V-B ORAN architecture

Based on the above-mentioned benefits of ORAN for 5G and beyond, we introduce
ORAN architecture as shown in Fig. 18. Fig. 18 represents different components
of ORAN such as service management and orchestration, near-real-time RIC, open
evolved NodeB (O-eNB), O-RAN Central Unit-Control Plane (O-CU-CP), Open
Distributed Unit (O-DU) and Open Radio Unit (O-RU), and O-Cloud (a cloud
computing platform comprising physical infrastructure nodes to host O-RAN
functions) [110]. First, O-Cloud is a cloud-computing platform consisting of a
set of physical infrastructure components satisfying the ORAN requirements to
host related ORAN functions such as O-CU-UP, O-CU-CP, O-DU, and near-RT RIC.
Moreover, O-Cloud supports three software components, i.e., virtual machine
monitor, operating system (OS), and container runtime. Second, O-eNB enables
O-RU and O-DU with an Open Fronthaul Interface (OFI) between them. Third, an
Open radio unit (O-RU ) is used to terminate the OFI and LOW-PHY functions
(i.e., Fast Fourier Transform (FFT), inverse Fast Fourier Transform (iFFT),
and physical random access channel (PRACH) extraction.) of the radio interface
(RI) to the UE. Moreover, O-RU also terminates the OF M-Plane Interface
(OFMPI) to the Open distributed unit (O-DU) and Service Management and
Orchestration (SMO). Fourth, O-DU is used to terminate OFI, F1, and E2
interfaces, together with the radio link control (RLC), medium access control
(MAC), and HIGH-PHY functions of the RI to the UE. O-DU also terminates the
OFMPI to the O-RU and O1 interface to the SMO. Fifth, the Oran Control Unit
User Plane (O-CU-UP) helps to terminate the F1-u, X2-u, S1-u, Xn-u, E1, 01,
and NG-u interfaces. Besides, it also terminates the Service Data Adaption
Protocol (SDAP) and Packet Data Convergence Protocol (PDCP) protocols. Sixth,
the ORAN control unit control plane helps to terminate the X2-c, F1-c, NG-c,
Xn-c, E2, O1 interfaces, PDCP protocol, and radio resource control (RRC)
protocol. Seventh, the near-real-time (RT) RAN intelligent control (RIC)
supports RT control and optimization for E2 functions and actions over the E2
interface with latency between 10 ms to 1 second. Lastly, the SMO helps to
manage the O-Clouds and support the orchestration of platform, and workflow
management. The following functionalities are supported by SMO, i.e., software
management of deployments; deployments and allocated O-Cloud resources;
creating, deleting, and associating O-Cloud resources; software management of
cloud platform; administration of O-Cloud resources.

In the description above, we provide an overview of each ORAN function but it
would be remiss if we did not mention the interface between them. The details
of interfaces in ORAN architecture can be described in Table IV.

###  V-C ORAN-aided NTN architecture for transparent satellite, without gNB
onboard

In Fig. 19, we propose a novel scenario while gnB is assumed to be allocated
on the Earth. Therefore, it is assumed that the O-DsU is located on Earth and
is connected to the NTN gateway (NTNGW) through the next generation (NG)
interface [111]. The NTNGW and the UE then connect to the satellite through NG
over satellite radio interface (SRI) and NR-Uu interface, respectively [111].
Moreover, the O-CU-CP and O-CU-UP have F1-c and F1-u interfaces with 5GC,
respectively.

###  V-D ORAN-aided NTN architecture for regenerative satellite, with gNB
onboard

In Fig. 20, we show a novel architecture for the regenerative satellite [112].
To the best of our knowledge, this is the first proposed architecture that
shows how ORAN can work with NTN systems [112]. Specifically, the gNB is
equipped with a satellite. In this scenario, the NTNGW connects directly to
the O-CU-UP through the NG interface. Besides, the NTNGW also connects with
non-real-time RIC, and the in-band controls such as E2, F1-c, and F1-u. Then,
the NTNGW connects to the satellite through NG over SRI. In this case, the
satellite is assumed to be equipped with NTN O-DU. The UE also connects to the
satellite through NR-Uu as in the transparent satellite scenario.

###  V-E ORAN-aided UAV trajectory design based on radio resource allocation

In this section, we describe a case where a UAV trajectory flight path is
based on the UAV radio resource allocation, which helps operators fine-tune
their radio resource policies in the ORAN architecture [108]. Since the
location along the trajectory is mainly focused on GUEs, the UAV does not
always belong to the main lobe of the ground base station (GBS). Moreover, the
side lobes of the GBS antenna lead to the scattered cell association
phenomenon, especially in the sky. The cell pattern on the ground is ideally a
contiguous area whereas the best cell is usually the cell closest to the UE.
When the UE moves upward, the side lobes of the antenna start to show up and
the best cell may no longer be the closest one. The connectivity of the cell
in this case becomes discrete, particularly at an altitude of 300m or more.

As shown in Fig. 21, the NRT RIC can retrieve essential UAV-related
measurements from the network based on UE and SMO reports, as well as UAV
trajectory information, aerial load information, and climate information. For
example, unmanned traffic management (UTM) is used to build/train relevant
AI/ML models deployed in RAN. This could be uplink/downlink interference from
the UAVs, UAV detections, and prediction of available radio resources such as
bandwidth, frequency, cell, and beam. Based on this information, the NRT RIC
can support the building and execution of AI/ML models from Non-real-time RIC.
Moreover, the NRT RIC can make radio resource allocation for on-demand
coverage of UAVs taking into account the trajectory and radio channel
information.

###  V-F Flow diagram for ORAN-aided UAV trajectory design based on radio
resource allocation

In Fig. 22, we describe a flow diagram for an ORAN-aided UAV trajectory design
based on radio resource allocation using ML models [108]. In step 1, the
collector in the SMO performs data collection from O-CU/O-DU via the O1
interface. Then this data is transmitted to the non-RL RIC in step 2. In step
3, the application server transmits the application data to non-RT RIC. From
steps 4 to 6, the ML flow is performed. In particular, non-RT RIC trains and
deploys the ML models in steps 4 and 5, respectively. In step 6, non-RT RIC
deploys or updates ML models in near-RT-RIC through the O1 interface. In step
7, the application data is transmitted from the application server to non-RT
RIC. In steps 8 and 9, non-RT RIC sends radio resource allocation, updated
policies, intents, and enrichment data to near-RT RIC via the A1 interface.
From steps 11 to 14, the performance evaluation and optimization are
performed. In step 11, the collector collects data from O-CU/O-DU and then
transmits it to the non-RT RIC in step 12. In step 13, the non-RT RIC executes
the performance monitoring and evaluation. Finally, the process of re-training
or updating the model is performed in step 14.

##  VI Challenges, Open Issues, and Future Research Directions

###  VI-A Challenges and Open Issues

####  VI-A1 Complexity of network slicing

The complexities of constrained optimization problems inherent in network
slicing for NTN pose significant challenges. Particularly, these optimization
problems are characterized by numerous practical constraints, ranging from
bandwidth limitations and latency requirements to security considerations.
Moreover, the combination of various platform in different altitudes, e.g.,
sattellites, UAV, and base station, further aggravates the problem complexity.
Consequently, to ensure the effectiveness of network slicing in NTN,
optimization techniques need to be carefully designed to take into account the
unique characteristics of each platform and the dynamic nature of the network
environment.

####  VI-A2 Challenges of AI-enabled NTN

AI techniques in NTN face a wide range of challenges due to the distinctive
features of non-terrestrial communication environments. First, the dynamic
nature of NTN, influenced by atmospheric conditions and connectivity shifts,
hinders the effectiveness of conventional AI techniques with limited domain
adaptation capability. Moreover, constrained bandwidth further complicates the
deployment of AI models, limiting the transmission of large datasets, which in
turns reduce the effectiveness of AI models’ training. Non-terrestrial
platforms also often have limited power and computational capabilities, which
complicates the deployment of AI techniques. Additionally, applications
demanding high prediction accuracy, such as autonomous driving, introduce
challenges in adapting AI models to the inherent propagation delay of NTN. To
address these challenges, techniques such as TL can be employed to improve the
domain adaption ability of AI models, as well as to address the limited power
and computational issues. Moreover, both FL and TL can be utilized to reduce
the data transmission demands.

####  VI-A3 Security

There is a wide range of security issues in NTNs, ranging from data
transmission and signal jamming to unauthorized access. Particularly, due to
the vast distance of transmission, NTNs are susceptible to interception and
eavesdropping. Moreover, the expansive coverage areas of NTN also make them
potential targets for unauthorized access. This also brings forth privacy
concerns as sensitive data are transmitted over long distances. Aditionally,
the reliance on satellite communication introduces challenges related to
secure satellite operation, such as satellite commands and cyberattacks. To
overcome these problems, promising solutions such as AI-empowered intrusion
detection system, blockchain-based authentication, and privacy-preserving
homomorphic encryption should be investigated.

###  VI-B Future Research Directions

####  VI-B1 Blockchain for NTN

Potential applications of blockchain in NTN encompass diverse areas, aiming to
enhance security, interoperability, efficiency, and privacy. For example, by
leveraging public/private key and digital signatures mechanisms, blockchain
can ensure secure and privacy-preserving identity management in NTN, enabling
transparency management and preventing unauthorized access. Moreover, smart
contracts can be leveraged to orchestrate network slicing, enabling the
automatic execution of agreements among network entities, as well as
streamlining the process of allocating and managing resources dynamically.
Blockchain can also play a pivotal role in stimulating collaboration and
resource-sharing through digital tokens and incentive mechanisms.
Particularly, participants can be incentivized to contribute resources, share
data, or engage in collaborative efforts through token-based rewards, thereby
alleviating resource and data demands in NTN applications.

####  VI-B2 FL for NTN

In NTN, FL can enable collaborative model training across various non-
terrestrial platforms. This collaborative learning paradigm brings two
significant advantages. First, data can be used to train the models locally,
and thus reducing the risks of exposing sensitive data over the network.
Moreover, this also reduces the needs to transmit data, thereby alleviating
the transmission demands. Nevertheless, the integration of FL in NTN involves
challenges related to varying connectivity, platform mobility, and
heterogeneous datasets. Specifically, different non-terrestrial platforms may
experience intermittent or fluctuating network connections, affecting the
synchronization and coordination required for FL processes. Moreover, the
mobility of these platforms introduces complexities in maintaining consistent
and reliable communication channels for collaborative model training.
Addressing these challenges, therefore, is crucial for unlocking the full
potential of FL in NTN and requires more efforts from the academia.

####  VI-B3 Generative AI for NTN

Exploring the integration of Generative Artificial Intelligence (GAI) in NTN
is a promising future research direction with huge potential. Generative AI,
known for its ability to create synthetic data and generate novel content, can
be utilized to address several challenges in NTN. For instance, it can be
employed to generate high-quality synthetic data, complementing the training
and testing of traditional AI techniques in the dynamic and diverse NTN
environments. Moreover, GAI can contribute to enhance the security of NTN by
generating realistic attack scenarios, enabling robust evaluation of network
defense mechanisms. Another approach is utilizing GAI to generate network data
for simulating various network conditions, thereby aiding network optimization
and predicting potential bottlenecks.

##  VII Conclusion

In this paper, we have presented a comprehensive survey of NTN for 6G networks
from both academic and industry perspective. Particularly, we have presented
the in-depth tutorial on NTN and the enabling technologies including network
slicing, AI/ML, and ORAN. Then, we have surveyed state-of-the-art network
slicing and AI/ML approaches that are proposed to address various challenges
of NTN in the literature. Moreover, we have presented how ORAN has been
utilized for NTN from the industry standpoint. Finally, we have discussed the
current challenges as well as open issues and introduced promising
technologies as future research directions of NTNs.

## References

  * [1] A. Adinoyi, M. Aljamae, and A. Aljlaoud, “The future of broadband connectivity: Terrestrial networks vs satellite constellations,” _International Journal of Communications, Network and System Sciences_ , vol. 15, no. 5, pp. 53–66, 2022.
  * [2] “An overview of 5G and non-terrestrial networks,” _IPaccess International_ https://www.ipinternational.net/overview-of-5g-and-non-terrestrial-networks/, Jan 2023.
  * [3] L. Feltrin, N. Jaldén, E. Trojer, and G. Wikström, “Potential for deep rural broadband coverage with terrestrial and non-terrestrial radio networks,” _Frontiers in Communications and Networks_ , vol. 2, p. 691625, 2021.
  * [4] F. Rinaldi, H.-L. Maattanen, J. Torsner, S. Pizzi, S. Andreev, A. Iera, Y. Koucheryavy, and G. Araniti, “Non-terrestrial networks in 5g & beyond: A survey,” _IEEE Access_ , vol. 8, pp. 165 178–165 200, 2020.
  * [5] D.-H. Tran, V.-D. Nguyen, S. Chatzinotas, T. X. Vu, and B. Ottersten, “UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization,” _IEEE Transactions on Wireless Communications_ , vol. 21, no. 3, pp. 1621–1637, 2022.
  * [6] M. M. Azari, S. Solanki, S. Chatzinotas, O. Kodheli, H. Sallouha, A. Colpaert, J. F. M. Montoya, S. Pollin, A. Haqiqatnejad, A. Mostaani _et al._ , “Evolution of non-terrestrial networks from 5g to 6g: A survey,” _IEEE Communications Surveys & Tutorials_, 2022.
  * [7] M. Vaezi, A. Azari, S. R. Khosravirad, M. Shirvanimoghaddam, M. M. Azari, D. Chasaki, and P. Popovski, “Cellular, wide-area, and non-terrestrial iot: A survey on 5g advances and the road toward 6g,” _IEEE Communications Surveys & Tutorials_, vol. 24, no. 2, pp. 1117–1174, 2022.
  * [8] G. Araniti, A. Iera, S. Pizzi, and F. Rinaldi, “Toward 6g non-terrestrial networks,” _IEEE Network_ , vol. 36, no. 1, pp. 113–120, 2021.
  * [9] A. Iqbal, M.-L. Tham, Y. J. Wong, G. Wainer, Y. X. Zhu, T. Dagiuklas _et al._ , “Empowering non-terrestrial networks with artificial intelligence: A survey,” _IEEE Access_ , 2023.
  * [10] P. Wang, J. Zhang, X. Zhang, Z. Yan, B. G. Evans, and W. Wang, “Convergence of satellite and terrestrial networks: A comprehensive survey,” _IEEE Access_ , vol. 8, pp. 5550–5588, 2019.
  * [11] S. Zhang, D. Zhu, and Y. Wang, “A survey on space-aerial-terrestrial integrated 5g networks,” _Computer Networks_ , vol. 174, p. 107212, 2020\.
  * [12] S. Mahboob and L. Liu, “A tutorial on ai-enabled non-terrestrial networks in 6G,” _arXiv preprint arXiv:2303.01633_ , 2023.
  * [13] T. N. Nguyen, T. Van Chien, D.-H. Tran, V.-D. Phan, M. Voznak, S. Chatzinotas, Z. Ding, and H. V. Poor, “Security-Reliability Tradeoffs for Satellite-Terrestrial Relay Networks With a Friendly Jammer and Imperfect CSI,” _IEEE Transactions on Aerospace and Electronic Systems_ , vol. 59, no. 5, pp. 7004–7019, 2023.
  * [14] M. M. Azari, S. Solanki, S. Chatzinotas, O. Kodheli, H. Sallouha, A. Colpaert, J. F. M. Montoya, S. Pollin, A. Haqiqatnejad, A. Mostaani _et al._ , “Evolution of non-terrestrial networks from 5G to 6G: A survey,” _IEEE communications surveys & tutorials_, 2022.
  * [15] D.-H. Tran, T. X. Vu, S. Chatzinotas, S. ShahbazPanahi, and B. Ottersten, “Coarse trajectory design for energy minimization in uav-enabled,” _IEEE Transactions on Vehicular Technology_ , vol. 69, no. 9, pp. 9483–9496, 2020.
  * [16] G. Geraci, D. Lopez-Perez, M. Benzaghta, and S. Chatzinotas, “Integrating terrestrial and non-terrestrial networks: 3D opportunities and challenges,” _IEEE Communications Magazine_ , 2022.
  * [17] “How starlink works.” [Online]. Available: https://www.starlink.com/technology
  * [18] “Project kuiper,” Feb 2023. [Online]. Available: https://www.aboutamazon.com/what-we-do/devices-services/project-kuiper
  * [19] F. Lardinois, “Google announces project Loon: Balloon-powered internet for remote areas,” Jun 2013. [Online]. Available: https://techcrunch.com/2013/06/14/google-x-announces-project-loon-balloon-powered-internet-for-rural-remote-and-underserved-areas/
  * [20] Sep 2020. [Online]. Available: https://www.nzherald.co.nz/nz/google-launches-project-loon/GFHWZAK4J2KNWU6IEPVC35TBIY/?c_id=137&objectid=10890750
  * [21] [Online]. Available: https://money.cnn.com/2015/07/30/technology/facebook-drone-aquila/index.html
  * [22] D.-H. Tran, S. Chatzinotas, and B. Ottersten, “Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks,” _IEEE Open Journal of Vehicular Technology_ , vol. 3, pp. 40–54, 2022.
  * [23] E. S. Agency, “Types of orbits.” [Online]. Available: https://www.esa.int/Enabling_Support/Space_Transportation/Types_of_orbits
  * [24] “UCS satellite database.” [Online]. Available: https://www.ucsusa.org/resources/satellite-database
  * [25] “GPS: The global positioning system.” [Online]. Available: https://www.gps.gov/systems/gps/space/
  * [26] T. 38.821, “Solutions for NR to support non-terrestrial networks (NTN),” 3GPP, Tech. Rep. V16.0.0, January 2020.
  * [27] X. Lin, B. Hofström, Y.-P. E. Wang, G. Masini, H.-L. Maattanen, H. Rydén, J. Sedin, M. Stattin, O. Liberg, S. Euler _et al._ , “5G new radio evolution meets satellite communications: Opportunities, challenges, and solutions,” _5G and Beyond: Fundamentals and Standards_ , pp. 517–531, 2021.
  * [28] Y. Drif, E. Lavinal, E. Chaput, P. Berthou, B. T. Jou, O. Grémillet, and F. Arnal, “Slice aware non terrestrial networks,” in _2021 IEEE 46th Conference on Local Computer Networks (LCN)_. IEEE, 2021, pp. 24–31.
  * [29] N. Van Huynh, D. T. Hoang, D. N. Nguyen, and E. Dutkiewicz, “Optimal and fast real-time resource slicing with deep dueling neural networks,” _IEEE Journal on Selected Areas in Communications_ , vol. 37, no. 6, pp. 1455–1470, 2019\.
  * [30] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network slicing in 5G: Survey and challenges,” _IEEE Communications Magazine_ , vol. 55, no. 5, pp. 94–100, 2017.
  * [31] I. Afolabi, T. Taleb, K. Samdanis, A. Ksentini, and H. Flinck, “Network slicing and softwarization: A survey on principles, enabling technologies, and solutions,” _IEEE Communications Surveys & Tutorials_, vol. 20, no. 3, pp. 2429–2453, 2018.
  * [32] W. Xia, Y. Wen, C. H. Foh, D. Niyato, and H. Xie, “A survey on software-defined networking,” _IEEE Communications Surveys & Tutorials_, vol. 17, no. 1, pp. 27–51, 2014.
  * [33] B. Han, V. Gopalakrishnan, L. Ji, and S. Lee, “Network function virtualization: Challenges and opportunities for innovations,” _IEEE Communications Magazine_ , vol. 53, no. 2, pp. 90–97, 2015.
  * [34] R. Mijumbi, J. Serrat, J.-L. Gorricho, N. Bouten, F. De Turck, and R. Boutaba, “Network function virtualization: State-of-the-art and research challenges,” _IEEE Communications Surveys & Tutorials_, vol. 18, no. 1, pp. 236–262, 2015.
  * [35] S. Wijethilaka and M. Liyanage, “Survey on network slicing for internet of things realization in 5G networks,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 2, pp. 957–994, 2021.