# Survey on Near-Space Information Networks: Channel Modeling, Networking, and
Transmission Perspectives

Xianbin Cao, , Peng Yang, , Xiaoning Su, X. Cao and P. Yang, and X. Su are
with the School of Electronic Information Engineering, Beihang University,
Beijing 100191, China. X. Cao and P. Yang are also with the Department of
Mathematics and Theories, Peng Cheng Laboratory, Shenzhen, Guangdong 518055,
China.

###### Abstract

Near-space information networks (NSIN) composed of high-altitude platforms
(HAPs), high- and low-altitude unmanned aerial vehicles (UAVs) are a new
regime for providing quickly, robustly, and cost-efficiently sensing and
communication services. Precipitated by innovations and breakthroughs in
manufacturing, materials, communications, electronics, and control
technologies, NSIN have emerged as an essential component of the emerging
sixth-generation of mobile communication systems. This article aims at
providing and discussing the latest advances in NSIN in the research areas of
channel modeling, networking, and transmission from a forward-looking,
comparative, and technological evolutionary perspective. In this article, we
highlight the characteristics of NSIN and present the promising use-cases of
NSIN. The impact of airborne platforms’ unstable movements on the phase delays
of onboard antenna arrays with diverse structures is mathematically analyzed.
The recent advancements in HAP channel modeling are elaborated on, along with
the significant differences between HAP and UAV channel modeling. A
comprehensive review of the networking technologies of NSIN in network
deployment, handoff management, and network management aspects is provided.
Besides, the promising technologies and communication protocols of the
physical layer, medium access control (MAC) layer, network layer, and
transport layer of NSIN for achieving efficient transmission over NSIN are
overviewed. Finally, we outline some open issues and promising directions of
NSIN deserved for future study and discuss the corresponding challenges.

###### Index Terms:

High-altitude platforms, unmanned aerial vehicles, channel modeling, network
deployment, handoff management, network management, efficient transmission

##  I Introduction

Recent advances in manufacturing, materials, communications, electronics, and
control technologies have witnessed an unprecedented increase in the
application of various types of non-terrestrial platforms in the military and
civil fields. In the wireless communication research field, non-terrestrial
networks (NTN) have been included as a part of the Third Generation
Partnership Project (3GPP) Rel-17 specifications, and discussions of NTN are
scheduled in the 3GPP Rel-18 work plan to support the fifth-generation of
mobile communication systems (5G) advanced use cases [1]. Additionally, the
integration networks of NTN and terrestrial networks (TN) (or space-air-ground
networks), the architecture of which is shown in Fig. 1, have been globally
considered a promising proposal for the emerging sixth-generation of mobile
communication systems (6G) [2].

Figure 1: An architecture of integrated NTN and TN.

As a crucial component of NTN, near-space information networks (NSIN) are a
new regime of situational awareness, processing, and serving networks composed
of high-altitude platforms (HAPs, including aircraft, airships, and balloons),
high-altitude unmanned aerial vehicles (HAUAVs), and low-altitude unmanned
aerial vehicles (LAUAVs). Compared with traditional space networks, NSIN have
some unique advantages in terms of deployment flexibility and agility,
deployment and operation cost, responsiveness, transmission latency, and
responsiveness. HAPs in NSIN, deployed at an altitude ranging from 17 km to 25
km, have the ability to extend the communication coverage quickly and provide
wide-area communication services. The unmanned aerial vehicles (UAVs) in NSIN,
deployed at a relatively low altitude ranging from several meters to thousands
of meters, can achieve service enhancements in critical areas. Further, NSIN
have the ability to overwhelm TN in terms of quick response to flash crowd
traffic, providing coverage in unserved or underserved areas cost-effectively,
and being resilient to natural and man-made disasters. Summarily, NSIN have
some outstanding advantages in achieving wide-area and three-dimensional (3-D)
coverage as well as local on-demand service enhancement.

As a result, NSIN have become an essential segment of space-air-ground
networks and have wide application prospects in fields such as regional
surveillance, emergency communication, intelligent transportation systems
(ITS), and computational offloading. Although NSIN have many attractive
advantages and application prospects, there are many key issues, such as
channel modeling, networking, and transmission, waiting to be solved before
NSIN can seamlessly integrate with space networks and TN and really contribute
to the emerging 6G technological standards.

###  I-A Surveys and Reviews on NSIN in Recent Ten Years

During the past ten years, the research on NSIN has attracted extensive
attention from both academia and industry. In this subsection, we overview the
surveys and reviews on NSIN in the recent ten years, and the reader is
recommended to refer to these articles and the references therein for more
detailed information on NSIN111It is noteworthy that a number of excellent
surveys and reviews on space-air-ground integrated networks have been
published. Nevertheless, we do not aim to discuss the state-of-the-art in the
research field of space-air-ground integrated networks. Actually, HAP networks
and UAV networks are not separately considered in the above works. Besides,
few of them differentiate UAVs from HAPs and discuss the optimization and
management method design with aerial platform heterogeneity consideration. We
therefore do not review and compare with them in this article..

A review of using HAPs for wireless communications in rural communities was
conducted in [3]. The authors reviewed HAP resource and interference
management techniques, where the potential of utilizing radio environment maps
and artificial intelligence (AI) for HAP interference and resource management
was discussed. Some coverage extension methods were also summarized in this
review. The authors in [4] summarized the recent progress of HAPs concerning
aspects of shape optimization, thermal analysis, and design studies.
Particularly, they discussed various thermal investigations and numerical
modeling approaches to study HAP thermal performance, studied aerodynamic
shape optimization and solar array layout optimization methods, and analyzed
many HAP design optimization methods with and without thermal consideration.
In [5], a review of the evolution of HAP projects and HAP technologies in the
aeronautical engineering field rather than the telecommunications area was
conducted. The work in [6] surveyed HAP-enabled communication systems from the
perspectives of channel modeling, performance analysis of HAP systems, and
energy and interference management methods. The work in [7] presented a
comprehensive literature review of channel modeling, radio resource
management, interference management, waveform design, handoff management, and
network management in HAP networks of the future. Besides, the HAP
communication system and two HAP onboard subsystems, i.e., energy management
and communication payload subsystems, were elaborated on in this work. The
authors in [8] gave a comprehensive review of enabling the connectivity
applications of HAP and UAV platforms utilizing current and future
communication technologies, including direct air-to-ground communications, HAP
communications, air-to-air communications, and multi-link heterogeneous
communications. The network architectures for airplanes and UAVs from the
literature were analyzed. Besides, an up-to-date list of open-source simulator
& emulator platforms for airplanes and UAVs was provided. In the paper [9],
the authors provided a comprehensive review and discussion of AI-driven NSIN
research, considering both communication and computation perspectives. The
authors in [10] discussed the existing spectrum sharing techniques of UAV and
HAP networks in detail from the perspectives of spectrum utilization rules,
spectrum sharing modes, spectrum sensing, and interference management
technologies. The work in [11] comprehensively reviewed low-altitude platform
(LAP)-based communication networks, HAP-based communication networks, and
integrated airborne communication networks. This work discussed the design of
LAP-based communication networks from movement control, networking, and
transmission perspectives. It also elaborated on the channel modeling of HAP-
based communication networks and shed light on how to implement integrated
airborne communication networks. Different from the above works, this survey
will explicitly distinguish HAPs and UAVs, highlight the design considerations
of NSIN, and comprehensively review the research on channel modeling,
networking, and transmission of NSIN. The comparison of this article with the
existing surveys & reviews on NSIN in recent ten years is presented in Table
I.

TABLE I: An overview of surveys & reviews on NSIN in recent ten years
---

###  I-B Contributions and Organization

The goal of this article is to present an overview of the research on NSIN in
the literature in recent ten years. Although NSIN achieve a series of
technological innovations and major breakthroughs in the research fields of
seamless communications, wide-area sensing, ubiquitous computing, and flexible
control, this article mainly surveys and discusses the latest advancements of
NSIN in channel modeling, networking, and transmission aspects. The main
contributions are as follows:

  * •

Characterizing the NSIN and introducing the NSIN as a promising, responsive,
and cost-effective solution for wide-area coverage services and local-area
service enhancement. We highlight the differences between HAP and UAV from the
diverse perspectives of mobility model, energy sources and endurance, payload
and power capability, and footprint.

  * •

Introducing the design consideration of NSIN and discussing the impact of
heterogeneity of airborne platforms in the protocol design and resource
allocation of NSIN. The cooperation among heterogeneous airborne platforms is
also advocated.

  * •

Elaborating on the size (or space), weight, mobility, and power (SWMAP)
constraints of airborne platforms in NSIN. Introducing the concept of the
situation (information) of NSIN and discussing the significance of the network
and security situation in the design of NSIN.

  * •

Discussing the impact of the unstable movements of airborne platforms on the
phase delays of diverse types of onboard antenna arrays. We present the
components of a typical channel model of a HAP-ground channel and discuss the
corresponding large-scale fading and small-scale fading in detail. At the same
time, we provide a comprehensive review of HAP channel modeling in the recent
literature and reveal the significant differences between HAP and UAV channel
models.

  * •

Overviewing and discussing the networking approaches of NSIN in the
literature. This includes an extensive discussion of how to deploy NSIN to
satisfy the quality-of-service (QoS) and quality-of-experience (QoE)
requirements of service recipients. A plethora of handoff strategies taking
into account serving user-side handoff metrics, airborne platform-side handoff
metrics, security, and surveillance information are presented. Network
management techniques from the technical perspectives of link fault recovery,
airborne platform coordination, and network architecture redefinition are also
discussed.

  * •

Discussing how to achieve efficient transmission in NSIN from an extensive
perspective of designing transmission approaches and protocols at the physical
(PHY) layer, the medium access control (MAC) layer, the network layer, as well
as the transport layer of NSIN.

  * •

Introducing the integration of reconfigurable intelligent surfaces (RIS) or
intelligent reflecting surfaces (IRS) with airborne platforms in NSIN and
presenting the benefits of such integration. The feasibility of applying three
types of MAC protocols, including the fixed allocation protocol, the random
contention protocol, and the reservation protocol, to NSIN in accordance with
the characteristics of NSIN is discussed. We highlight the distinction between
UAV routing protocols and routing protocols of NSIN, and present the evolution
of NSIN routing protocols from a HAP-assisted design to a unified design.
Presenting the challenges of applying a conventional transport control
protocol (TCP) to NSIN and providing an overview of the transport layer
protocols customized for NSIN. The main ideas, pros, and cons of these
protocols are also summarized.

Finally, we conclude by summarizing the open issues and promising directions
for future study. Specifically, in Section II, we characterize the NSIN in
detail and present the promising use cases of NSIN that aim to enable the
reader to deepen their knowledge and understanding of NSIN. In Section III, we
elaborate on the components of the channel models of NSIN while highlighting
the impact of unstable platform movement and meteorological factors on the
channel modeling of NSIN. Section IV describes key issues in the networking of
NSIN, including network deployment, handoff management, and network
management. The objectives, solutions, and key technologies of networking in
NSIN are summarized and discussed in detail. In Section V, we comprehensively
present the key technologies at the PHY layer, MAC layer, network layer, and
transport layer of NSIN for achieving effective transmission. The main ideas
and performance of these technologies are discussed and compared. In Section
VI, some open issues that need to be tackled and future directions that
deserve attention are presented. Finally, we conclude this article in Section
VII. For ease of reading, some significant acronyms in this article are shown
in Table II.

TABLE II: LIST OF ABBREVIATIONS Acronyms | Description | Acronyms | Description
---|---|---|---
3GGP | Third Generation Partnership Project | A-GR | ADS-B system aided Geographic Routing
5G |  | The Fifth-Generation of mobile
---
communication systems
6G |  | The Sixth-Generation of mobile
---
communication systems
ACO | Ant Colony Optimization | ADC | Analog-to-Digital Converter
ADS-B | Automatic Dependent Surveillance-Broadcast | AI | Artificial Intelligence
ALOHA | Additive Links On-line Hawaii Area | AoA/AoD | Angle of Arrival/ Angle of Departure
ARIS | Aerial-RIS | ATM | Air Traffic Management
B5G | Beyond 5G | CDMA | Code Division Multiple Access
CDN | Content Delivery Networks | CIR | Channel Impulse Response
CIR | Carrier-to-Interference Ratio | CNPC | Control and Nonpayload Communication
CSMA/CA |  | Carrier Sense Multiple Access
---
with Collision Avoidance
CTS | Clear to Send
DAC | Digital-to-Analog Converter | DC | Direct Current
DLR | Deutsches Zentrum für Luft- und Raumfahrt | DoA | Direction of Arrival
DQN | Deep Q-Network | DRL | Deep Reinforcement Learning
DTN | Delay Tolerant Networks | E2E | End-to-End
EM | Electromagnetic | FDMA | Frequency Division Multiple Access
FSO | Free Space Optical | FSPL | Free Space Path Loss
FWA | Fixed Wireless Access | GM | Gauss-Markov
GPS | Global Positioning System | HAPs | High-Altitude Platforms
URLLC |  | Ultra-Reliable and
---
Low-Latency Communications
HGHR |  | Hybrid Time-Space Graph Supporting
---
Hierarchical Routing
IFT | Instantaneous Flight Time | IMT | International Mobile Telecommunications
IoT | Internet of Things | IRS | Intelligent Reflecting Surface
ISI | Inter-Symbol Interference | ISR | Intelligence, Surveillance, and Reconnaissance
ITS | Intelligent Transportation Systems | ITU | International Telecommunication Union
LAP | Low-Altitude Platform | LAUAVs | Low-Altitude Unmanned Aerial Vehicles
LMS | Least Mean Square | LSTM | Long Short-Term Memory Network
LTE | Long Term Evolution | MANET | Mobile Ad hoc Network
MIMO | Multiple-Input Multiple-Output | ML | Machine Learning
MOS | Mean Opinion Score | MPB | Mission Plan Based
NFV | Network Function Virtualization | NLoS | Non-Line-of-Sight
NOMA | Non-Orthogonal Multiple Access | NSIN | Near-Space Information Networks
NTN | Non-Terrestrial Networks | OFDM | Orthogonal Frequency Division Multiplexing
PAC | Proxy Authentication Center | PDR | Packet Delivery Ratio
PNT | Positioning, Navigation, and Timing | PR | Pheromone Repel
PSNR | Peak Signal-to-Noise Rate | QoE | Quality-of-Experience
QoS | Quality-of-Service | RF | Radio-Frequency
RIS | Reconfigurable Intelligent Surface | RL | Reinforcement Learning
RM | Random Movements | RMS | Root Mean Square
RNN | Recurrent Neural Network | RSS | Received Signal Strength
RTS | Request to Send | RWP | Random Way Point
SAGIN | Space-Air-Ground Integrated Networks | SC-FDMA | Single-Carrier FDMA
SDMA | Spatial Division Multiple Access | SDN | Software-Defined Networking
SER | Symbol Error Rate | SFC | Service Function Chaining
SHARP | Stationary High Altitude Relay Platform | SNR | Signal-to-Noise Ratio
SRCM | Semi-Random Circular Movement | ST | Smooth-Turn
SWMAP | Sizes (or Space), Weights, Mobility, and Power | TDL | Tapped Delay Line
TDM | Time Division Multiplexing | TDMA | Time Division Multiple Access
TN | Terrestrial Networks | UAVs | Unmanned Aerial Vehicles
UPA | Uniform Planar Array | HAUAVs | High-Altitude Unmanned Aerial Vehicles
UtG | UAV-to-Ground | VANET | Vehicle Ad hoc Network
VNF | Virtualized Network Function | WCDMA | Wideband CDMA
WRC | World Radiocommunication Conference | WSN | Wireless Sensor Network
XR | Extended-Range |  |

##  II Characterizing the NSIN and USE CASES

The purpose of this section is to provide a detailed characterization of NSIN,
focusing on its structure, specificity, and design considerations.
Additionally, promising use cases for NSIN will be presented.

###  II-A Segments of NSIN

As shown in Fig. 1, NSIN composed of HAPs, HAUAVs, and LAUAVs with diverse
types of onboard devices are a novel and promising network regime. From the
perspective of platform deployment altitude, it can be categorized into two-
layer vertical aerial networks: HAP networks and UAV networks.

HAP networks that consist of one or more connected HAP platform(s) are
deployed in the near space of 17 km to 25 km. Connected HAP platforms can form
a mesh network topology, which will greatly enhance the service capability of
HAP networks. Further, it can be concluded that the role of HAP in NSIN is
fundamental and irreplaceable. The design and manufacture of HAPs have
attracted extensive attention from industry, and many recent HAP projects or
products have been developed in recent ten years, as summarized in Table III.

TABLE III: Some representative HAP projects/products that are still under construction or maintenance | Project/product
---
Type |  | Company/
---
organization
| Important descriptions
---
| Zephyr S
---
High-Altitude
Pseudo-Satellite [12]
|  | Airbus and
---
NTT DOCOMO
|  $\bullet$ It demonstrated the feasibility of providing communication
---
services from the stratosphere to smartphones.
$\bullet$ Data transmissions across various speeds up to a distance
of 140 km were successfully demonstrated.
$\bullet$ It loaded with systems for diverse communication
and sensing services such as TV, radio,
mobile telephony, VoIP, and remote sensing.
$\bullet$ It supports a payload of 100 kg and a flight
duration of up to one year.
$\bullet$ It covers an area of up to 1000 km in diameter.
| AVEALTO Wireless
---
Infrastructure
Platform [13]
|  | Avealto Ltd.
---
|  $\bullet$ Remain on-station in the stratosphere at an altitude of
---
20 km for missions of several months’ duration.
$\bullet$ Each HAP will provide services to a circular terrestrial
footprint of 240 km in diameter.
| Thunderhead Balloon
---
System [14]
|  | Raven Aerostar
---
|  $\bullet$ It delivers long term evolution (LTE) from 70,000 ft
---
and demonstrates a solution for delivering internet to
underserved, denied or disaster-affected populations.
$\bullet$ It redefines stratospheric missions and provides
cost-effective persistence.
| Yuanmeng [15, 16]
---
|  | Beihang University
---
& Beijing Aerospace
Technology Company
|  $\bullet$ A volume of approximately 18,000 cubic metres.
---
$\bullet$ Payload (mission equipment): 300 kg
$\bullet$ Photovoltaic cells on the hull for power during the day
and battery or fuel cell for power at night
$\bullet$ Fly for up to six months at a stretch, with a large array
of solar panels covering the aircraft’s top side.
$\bullet$ Loaded with systems for wideband communication, relay,
high-definition observation, and spatial imaging.
| Stratobus [17]
---
|  | Thales Alenia Space
---
|  $\bullet$ A solution for local missions out to a terrestrial horizon of
---
up to 500 km
$\bullet$ Providing security (the fight against terrorism,
drug trafficking), environmental monitoring
(forest fires, soil erosion, pollution), telecommunications
(Internet, 4G/5G) and navigation (GPS local reinforcement).
$\bullet$ It can carry up to 450 kg of payload, with a power
rating of eight kW for a five-year mission
| HAWK30 [18]
---
|  | HAPSMobile
---
|  $\bullet$ It aims at connect mobiles, UAVs,
---
and the Internet of Things (IoT) devices around the world.
$\bullet$ It has a wingspan of 78 meters and provides a 100 km
coverage radius for several months.
| PHASA-35 [19]
---
|  | BAE Systems and
---
Prismatic
|  $\bullet$ It aims at providing some many services, e.g., 5G.
---
$\bullet$ It has a payload capacity of 15 kg with
a power capacity of 300-1000 W.
$\bullet$ It can cover a radius of up to 200 km.
TABLE IV: _(_ Continued.) Some representative HAP projects/products that are still under construction or maintenance | Project/product
---
Type |  | Company/
---
organization
| Important descriptions
---
| Elevate [20]
---
|  | Zero 2 Infinity
---
|  $\bullet$ It aims at providing transportation services.
---
$\bullet$ It has launched a payload of almost 500 kg to an altitude of 32 km.
| X-station [21]
---
|  | StratXX
---
|  $\bullet$ It loaded with systems for diverse communication
---
and sensing services such as TV, radio,
mobile telephony, VoIP, and remote sensing.
$\bullet$ It supports a payload of 100 kg and a flight
duration of up to one year.
$\bullet$ It can cover up to a 1000 km area in diameter.

UAV networks consisting of a number of HAUAVs and LAUAVs are deployed in
tropospheric space ranging from a few meters to ten km.

With the integration of the above two-layer networks, NSIN is able to achieve
wide-area and 3-D coverage along with local on-demand service enhancement and
has promising application prospects in many critical applications, including
ITS, emergency communications, and security protection of energy, territory,
homeland, sovereignty, and so on.

###  II-B Differences between HAP and UAV

Nevertheless, there are many significant and distinct differences between a
HAP and a UAV in terms of mobility model, energy source and endurance, payload
and power capability, and footprint. These differences are summarized in Table
V and elaborated on in the following subsections.

TABLE V: Some significant differences between HAP and UAV | Airborne
---
platform
Type | Mobility |  | Energy source
---
& endurance
| Payload & power
---
capability
Footprint
| Aircraft |  | Trajectory re-plan
---
enable
|  $\bullet$ Fuel and
---
solar cells
$\bullet$ Several months
or one year
|  $\bullet$ Many tens of kg
---
$\bullet$ A power rating of
300-1000 W [19]
Cover a radius of up to 200 km [19]
HAP | Balloon |  | Stratospheric
---
winds dependent
|  $\bullet$ Fuel and
---
solar cells
$\bullet$ Several months
or one year
|  $\bullet$ Up to 100 kg
---
$\bullet$ A power rating of
100 W
Can be 60 km coverage radius
| Airship |  | Quasi-stationary or
---
trajectory re-plan
enable
|  $\bullet$ Mainly solar cells
---
$\bullet$ Several years
|  $\bullet$ Up to 450 kg [17]
---
$\bullet$ A power rating of
eight kW
Cover up to 500 km in diameter [17]
UAV |  | Fixed-
---
wing
| Fast movement
---
with limited
steering ability
|  $\bullet$ Mainly fuel cells
---
$\bullet$ Tens of hours
|  $\bullet$ Tens of kilograms
---
$\bullet$ Power of tens
watts [22]
| Up to many tens of km in radius
---
(Depending on coverage region)
| Rotary-
---
wing
| Flexible and
---
fast movement
|  $\bullet$ Battery-powered
---
$\bullet$ Usually within
an hour
|  $\bullet$ Usually a few kg
---
$\bullet$ Provides a few
watts of power
| Up to several km in radius
---
(Depending on coverage region)

####  II-B1 Mobility model

The maximum flight speed of a UAV is much greater than that of a HAP. In
accordance with vertical applications, the flight speed of a UAV can be in the
range of 0-460 km/h [23, 24], and the flight speed of a HAP can be 110 km/h
[18]. A mobility model is a fundamental mathematical expression supporting
network connectivity analysis, routing protocol design, and network
performance evaluation. It mathematically characterizes how the locations,
velocities, and acceleration of network nodes change over time. Next, we
discuss the mobility models designed for UAVs and HAPs, respectively.

There are seven types of UAV mobility models, namely, random way point (RWP)
[25], random movements (RM) [26], smooth-turn (ST) [27], Gauss–Markov (GM)
[28], pheromone repel (PR) [26], Paparazzi [29], semi-random circular movement
(SRCM) [30], and mission plan-based (MPB) [31]. From this table, we observe
that the selection of the mobility model is vertical application-related. For
instance, RWP and RM mobility models can well capture the random activities of
aerial platforms. As a result, many researchers in the research community
leverage them to model the mobility of UAVs. However, in practical
applications, UAVs will choose neither the RWP model nor the RM model due to
restrictions on maneuverability, aerodynamics, and energy consumption. As for
the ST, GM, and PR mobility models, they are suitable for patrolling
applications as they characterize the memory-equipped movement of UAVs. The
flight path generated by the SRCM model for a UAV is a circular trajectory
with a fixed circling center and a diverse range of turning radii. Hence, the
SRCM mobility model is well-suited for UAVs performing some search and rescue
tasks. The MPB mobility model is well-suited for UAVs engaged in cargo
delivery and transportation tasks where the flight paths are pre-planned.
Although uncertainties, such as unknown dangerous airspace and departure
delays, may occasionally cause flight deviations from the intended paths, UAVs
have the ability to promptly re-plan their flight paths based on the pre-
determined routes [32].

A HAP is deployed in the stratosphere, and the relatively weak stratosphere
airflow will cause unstable HAP movements such as horizontal drift, vertical
motion, rotation, and attitude angle disturbance. Therefore, a HAP is regarded
as stationary if it does not escape from a small airspace. ITU suggests that
the diameter of the airspace is 500 m [33]. However, accurate attitude control
of a HAP with huge volumes is hard to achieve, and a HAP may escape from the
airspace with a large probability. To address this issue, the HeliNet Project
designated a more practical stationary model for HAP [34]. The stationary
model is a cylindrical model with a diameter of four km and a height of three
km. Then, a HAP is considered to be stationary if it does not flee from this
cylindrical airspace 99.9% of the time. There is another similar quasi-
stationary model for HAP that allows a HAP to change its altitude over time,
bounded by an upper limit (e.g., 26 km) and a lower limit (e.g., 20 km), with
the cylinder’s radius ranging from one km to five km. The quasi-stationary
model is an important and commonly used model for HAP, despite the fact that a
HAP can fly at a speed of 110 km/h. Besides, representing the spatial location
of a HAP as a cylindrical volume rather than a deterministic point would be
more appropriate.

####  II-B2 Energy sources and endurance

Up to now, HAPs have been able to utilize three types of energy sources,
including fuel energy, beam energy, and solar energy. Due to the limited
capacity of fuel tanks, HAPs powered by conventional energy sources have a
short flight duration. Additionally, the processes of takeoff and landing for
a HAP are time-consuming and energy-intensive. Then, the cruise duration of
conventional energy-driven HAPs will be significantly shortened, and their
application scenario will be rather limited. HAPs can be charged by energy
beams from external energy sources. The concept of power transmission using
radio waves has a long history, going back almost one hundred and forty years
to Heinrich Hertz. In the 20th century, a stationary high-altitude relay
platform (SHARP) project demonstrated such a process of transmitting microwave
power from a large ground antenna system to a HAP [35]. Except for microwave
beam power, laser beams can also be explored as energy sources for HAPs.
Nonetheless, microwave or laser beam power will bring the risk of high-power
irradiation; thus, the practical application of beam-powered HAPs is severely
restricted. HAPs are deployed in the stratosphere to take advantage of the
abundant solar energy available. As a result, modern HAPs are designed with a
focus on efficiently harnessing solar energy, taking into account operational
costs and safety requirements. HAPs are bulky, and then a plethora of solar
panels can be attached to HAP surfaces to convert solar energy into direct
current (DC) power. Solar-powered HAPs may also utilize fuel energy to power
HAP functions and maintain the platform’s attitude during dark nights.

HAUAVs can utilize two types of energy sources, including fuel energy and
solar energy. However, as HAUAVs fly in the atmosphere, the deployment of
solar-powered HAUAVs is closely related to weather conditions. On the
contrary, fuel-powered HAUAVs are relatively robust to weather conditions and
can fly for many tens of hours. As a result, fuel-powered HAUAVs have a wide
range of applications in the military field, such as used for ground attack,
ground surveillance, and early warning. LAUAVs can also utilize two types of
energy sources, i.e., fuel energy and electrical batteries. Owing to the
advantages in manufacturing and maintenance costs, battery-driven LAUAVs are
much more popular than fuel-powered LAUAVs. However, the total battery
capacity of a LAUAV is limited owing to the constraint on its payload
capability. Currently, depending on applications, the limited energy can
support a battery-driven LAUAV to fly for only a few minutes, or at most a few
tens of minutes.

Based on the above analysis, it can be concluded that effective energy
management is of utmost importance for NSIN as it directly impacts its overall
lifespan. The energy consumption of NSIN consists of consumption for flight
control and performing missions (e.g., communications). The energy consumed by
flight control includes that for propulsion and flight attitude control. The
types and characteristics (e.g., size and weight) of platforms in NSIN also
affect flight control consumption. Communication activities will generate
communication energy consumption that includes circuit consumption (e.g.,
energy consumed by mixers, frequency synthesizers, D/A converters, and so on)
and transmission consumption. The types of communication payloads and adopted
communication techniques will also significantly affect the amount of
communication energy. Many mechanisms related to flight control and
transmissions can be adopted to improve the energy efficiency of NSIN. For
example, as the engine power of a UAV will decrease and then increase as the
flight speed increases [36], the flight speed of a UAV should be optimized to
reduce engine power. Reasonable flight plans should be made to reduce engine
power, and maneuvers like small-radius turns and rapid increases in the flight
altitude and speed should be reduced. Additionally, the issue of optimizing
the deployment altitudes of UAVs should be studied. A low deployment altitude
will lead to reduced engine power. However, the non-line-of-sight (NLoS)
probability of communications may increase when UAVs are deployed at low
altitudes. As a result, greater transmission power will be required to satisfy
the QoS requirements of terrestrial serving terminals. Certainly, UAVs can
reduce energy consumption by offloading computational tasks to HAPs [37, 38,
39, 40].

Summarily, the energy constraint of NSIN is determined by that of HAPs, HAUAVs
and LAUAVs. Owing to the stringent constraints on aspects of platform size,
weight, and payload capability, LAUAVs have the most stringent energy
consumption constraints.

####  II-B3 Payload and power capability

Payload and power capability are significant differences between a HAP and a
UAV. The payload and power capability of a HAP are much greater than that of a
UAV. For example, the recent near-space airship “Stratobus” can carry up to
450 kg of payload, which envisions unprecedented potential for integrating
many diverse types of mission systems (e.g., computing, communication,
sensing, positing, navigating, timing (PNT), and intelligence, surveillance,
reconnaissance (ISR) systems) and simultaneously providing multiple kinds of
services. In this way, the efficiency of completing missions by resorting to a
HAP can be greatly improved. Further, owing to the advantages of high payload
capability, a HAP can conduct many crucial tasks that cannot be completed by a
UAV, e.g., the collection of sufficient measurement data inside a typhoon. To
complete this task, an airborne platform needs to carry and drop a large
number of communication and sensing nodes into the interior of the typhoon.
Meanwhile, the airborne platform should enable the collection of data from
these measuring nodes. For a UAV, it has limited payload capacity and thus
cannot carry out many critical missions.

Besides, a HAP can generate a greater power rating than a UAV, e.g., the
“Stratobus” has a 1000 ${\rm m}^{2}$ solar generator and high-energy density
batteries, which are able to generate a power rating of eight kW. The great
power generation capability indicates that a HAP can provide communication
services for a lot of users with high bitrate requirements. Meanwhile, higher
power indicates that a HAP has a larger footprint, and the degradation of
signal quality caused by meteorological attenuation will be alleviated by
increasing transmit power to some degree.

####  II-B4 Footprint

The large footprint is one of the most significant features of a HAP, which
popularizes both research and utilization of it. Theoretically, thanks to a
high deployment altitude, a HAP has a great probability of enabling line-of-
sight (LoS) propagation of around 565 km in diameter. Due to topological
obstacles and radio propagation characteristics, a realistic coverage area for
a HAP is around 240 km in diameter over most land areas and around 500 km over
oceans or plains (where the topography is flat). Owing to their wide
footprint, HAPs have been envisioned to connect remote areas where traditional
Internet connectivity is unserved or underserved. Theoretically, only
thousands of HAPs are required to establish global interconnectivity. Besides,
the wide footprint enables a HAP to be considered a satellite alternative.

Different from a HAP, a UAV has a relatively small footprint. Yet, like a HAP,
the size of the UAV footprint is closely related to the deployment
environment. For instance, when deployed in a rural area, a UAV will have a
large footprint because of the absence of severe shadow fading or signal
blockage caused by high-rise buildings. A UAV has a small footprint when
deployed in a dense urban area. As a result, extensive attention has been paid
to the topic of extending the UAV footprint by optimizing the UAV deployment
altitude.

###  II-C Design Consideration of NSIN

####  II-C1 Heterogeneity

As presented above, NSIN consist of many different aerial platforms that are
quite different in terms of deployment altitude, footprints, computing power,
and so on. Therefore, heterogeneity is the most important feature of NSIN and
must be taken into account in the NSIN design process, especially in the
protocol design of NSIN.

HAPs and UAVs collaboratively construct a network with a multi-tier structure.
In this multi-tier network, different footprints of a HAP and a UAV may lead
to footprint overlap and inter-tier interference. To this end, advanced
beamforming schemes and spectrum sharing mechanisms [41] should be designed to
alleviate the interference so that the performance of NSIN will not be
degraded.

As presented above, HAPs and UAVs have distinct abilities, resulting in the
fact that HAP communication systems and UAV communication systems may adopt
diverse communication paradigms. For instance, they may work at different
frequency bands, explore different modulation and demodulation techniques, and
adopt diverse signal exchange methods. To this end, the design of NSIN should
achieve some technical breakthroughs in terms of the integrated protocol
design, the integrated network framework design, the optimal deployment of
gateways, and so on.

UAVs are widely used in various scenarios due to their flexible and agile
deployment capabilities. In comparison, HAPs have larger sizes and heavier
masses, making them less flexible than UAVs. Consequently, the mobility
control of NSIN cannot be modeled and optimized using the same approaches as
UAVs. HAPs and UAVs also exhibit different communication persistence and
lifespans, which should be taken into account when designing protocols and
optimizing network resources for NSIN.

####  II-C2 Cooperation

The cooperation between HAPs and UAVs can significantly enhance the
performance of NSIN. In many applications, increasing the number of
collaborative airborne platforms can greatly improve the service ability of
NSIN.

On one hand, the footprint of a HAP is much larger than that of a UAV, so the
collaboration of HAPs and UAVs will greatly extend the coverage of UAV
networks [42]. On the other hand, in some areas, such as mountainous and
metropolitan regions, the coverage of HAP networks can be improved by
deploying UAV relays. By acting as relays, UAVs can enhance the quality of
HAP-UAV-user links in a flexible and proactive manner. Airborne platforms in
NSIN can act as aerial network nodes with diverse functionalities, including
sensing nodes, computing nodes, communication nodes, and so on. Thus, through
the cooperation among these airborne platforms, NSIN can complete some
critical tasks (e.g., search, rescue, surveillance, and earth observation)
faster and more accurately. In non-cooperative HAP and UAV networks, each
airborne platform may be required to accomplish several tasks, the process of
which will not only lead to great task latency but also occupy a significant
amount of radio resources (e.g., frequency and power). The robustness of NSIN
can also be enhanced by airborne platform cooperation; as a result, the
heterogeneous NSIN will keep organized even in the case of platform or link
failure.

####  II-C3 Disruption and delay prone network

The link disruption is likely to occur in NSIN. Link disruption will be caused
by many factors, including airborne platform-related factors (e.g., platform
mobility, failure/replacement, interference, and antenna pattern) and
propagation environment-related factors (e.g., blockage and various types of
propagation fading). Delays in NSIN information transmission may be caused by
long-distance transmission, poor link status, reduced network throughput,
packet queueing, and network congestion, etc.

Therefore, some mechanisms should be designed to tackle the link interruption
issue and reduce transmission delay in NSIN, which include beam alignment,
mobility control, interference management, transmission protocol design, queue
management, and congestion control. The reader can refer to more details of
the mechanism design for NSIN in Section X.

####  II-C4 Airborne platform constraints

When designing NSIN, one should simultaneously consider the constraints of
HAPs and UAVs in terms of their sizes (or space), weights, mobility, and
power. SWMAP constraints significantly affect the endurance, sensing,
computation, and communication capabilities of NSIN.

  * •

Size or space: Airborne platforms with larger sizes or more space have a
greater payload capacity and more mounting points. Thus, a larger airborne
platform can carry more powerful sensors and peripherals, such as Internet of
Things (IoT) devices, transceivers, hubs, cameras, three-axis accelerometers
and gyroscopes, magnetometers, barometers, the global positioning system
(GPS), and electro-optical pods. In this way, these platforms have the ability
to complete preset tasks more accurately, quickly, and effectively. However,
the larger the size of the platform, the heavier the platform.
Correspondingly, the cost of manufacturing and operating the platform is
higher, and its popularity is limited.

  * •

Weight: The weight of both the payload and the airborne platform itself will
significantly affect its endurance. The weight of the payload is a crucial
performance indicator for an airborne platform. For a HAP, it is closely
related to the locations of the pods mounted on the HAP, and a HAP can carry
more payloads by deploying decentralized pods. The lighter platform also
enables it to mount more payloads and extends the platform’s endurance. In
fact, the design and manufacturing of lightweight airborne platforms is a
significant trend. In recent years, advanced lightweight materials such as
carbon fiber have been widely utilized in NSIN. For example, the authors in
this article has utilized carbon fiber materials to manufacture the propeller
blades of an airship, which greatly enhance the propeller structure, reduce
the weight of the propeller, and lower energy consumption. The Aquila drone
engineered by Meta has a tremendous wingspan (141 feet) compared to a Boeing
737’s 113 feet. Furthermore, being built with carbon fiber, Aquila weighs only
around 900 pounds, which is approximately half the weight of a smart car.

  * •

Mobility: Mobility is one of the most important characteristics of an airborne
platform, which contributes a lot to its popularity. Compared to stationary
platforms, mobile airborne platforms have many merits, e.g., they can improve
the fairness of network service, proactively improve channel quality, enhance
network performance, and accomplish 360∘ panoramic sensing and monitoring of
specific areas or targets. The degree of performance improvement is also
related to the flexibility of the platform. Airborne platforms have some
constraints on their moving directions and speeds, and the types of airborne
platforms affect their maneuverability. For instance, compared to a LAUAV, a
HAP has greater maneuverability limitations owing to its larger size and
inertia. To this end, NSIN designers need to separately enforce mobility
constraints for airborne platforms in NSIN.

  * •

Power consumption: Power consumption constraints are key consideration during
the process of designing NSIN and play a crucial role in prolonging its
lifetime. Different types of airborne platforms have diverse requirements for
power consumption constraints. In particular, small and battery-driven
platforms have stricter power constraints. Although solar-powered airborne
platforms are not much more energy-sensitive than battery-driven platforms,
developers have to design energy-aware protocols for them such that they can
operate normally during hard and long nights. To save power consumption for
accomplishing tasks, NSIN developers should also pay attention to the design
of energy-efficient management mechanisms. The propulsion power consumption of
airborne platforms can be much greater than that for accomplishing tasks. The
amount of propulsion power consumption is closely related to the
maneuverability of the platforms. Therefore, designers should take into
account the energy-efficient movement control of airborne platforms when
deploying NSIN to accomplish tasks.

####  II-C5 Situation assistance

The NSIN is deployed in a rather complex 3-D airspace. To effectively support
network construction, management, utilization, and operation, NSIN designers
should be aware of, analyze, and exploit situational information related to
NSIN.

The situation of NSIN mainly includes the network situation and the security
situation. The network situation of NSIN characterizes such NSIN statuses as
resources, capabilities, capacity, and traffic. In terms of resources, the
network situation involves the status of spectrum, power, storage, and
platforms. Network capability status describes the communication, computing,
sensing, PNT, and ISR capabilities of NSIN. Capacity status includes link
capacity, sub-network capacity, and network capacity. Regarding the traffic
situation, the network situation describes the status of network and node
traffic congestion, as well as the types of traffic present in the network,
such as control or payload traffic. It is noteworthy that different types of
traffic have distinct service requirements on NSIN, and the necessity of
merging multiple different types of traffic into a network has been widely
recognized.

In a designated time and space range, elements (e.g., neighboring flight
platforms, terrain, and meteorology) of the airspace environment may threaten
the safe flight of an airborne platform. The security situation refers to the
spatial and temporal distribution of the level of threat to the safe flight of
platforms. It is an important prerequisite for the safe and efficient sharing
of airspace by multiple heterogeneous airborne platforms. For instance,
airborne platforms in NSIN need to fly to a target airspace autonomously and
share the limited and complex airspace when carrying out the mission. Then,
when planning the trajectories of airborne platforms, developers should
consider cooperative planning with civil airspace, the avoidance of obstacles,
and the collision with other flight platforms in the neighboring area.

####  II-C6 Aviation regulations

Owing to safety, privacy, and harmonious operation concerns, the aeronautical
regulation activities of NSIN are regulated by national civil aviation
authorities. It indicates that NSIN must operate under civilian laws and be
subject to civilian regulations and licensing to guarantee safe and harmonious
operations. Civil aviation authorities of different countries may formulate
different rules for NSIN. However, relevant rules are formulated mostly
considering the following factors: 1) The categories, sizes, and weights of
airborne platforms (e.g., airships, balloons, and aircraft). For instance,
airborne platforms weighing more than 0.25 kg must be registered in most
countries. 2) The pilot or control methods of airborne platforms, i.e.,
unmanned or manned. For example, current aeronautical regulations restrict
unmanned HAPs from flying in civilian airspace, but the deployment of manned
HAPs may be a step towards allowing HAPs in such airspace. 3) The flight
altitude and safe distance towards other objects, e.g., LAUAVs are not allowed
to fly above 120 m in China. 4) The flight regions of airborne platforms,
whether they are above an urbanized area, restricted areas, near national
borders, or near airports. For instance, airborne platforms are prohibited
from flying within five km of the national border in China. 5) The urgency of
flight missions, e.g., the flight plan approval process will be simplified in
some countries during the period of executing anti-terrorism and emergency
missions.

####  II-C7 Spectrum regulation of NSIN

At the World Radiocommunication Conference in 1997 (WRC-97), the frequency
bands, i.e., 47.2-47.5 GHz (downlink) and 47.9-48.2 GHz (uplink), further
confirmed at WRC-19, were first allocated to HAPs for worldwide usage. This
proposal was promoted by the US as the federal communications commission (FCC)
approved the application of the Sky Station International Corporation to
utilize this 2 × 300 MHz band for stratospheric application of Internet
service and interactive video communication service relay in 1996. Yet,
concerning the great rain fading and immature technical solutions in the upper
frequency bands, some lower frequency bands were agreed to be occupied by HAPs
at WRC-2000. Thereafter, technological advances and the pressing requirement
of providing global broadband applications resulted in a review of the current
spectrum regulatory provisions. At WRC-19, delegates agreed to allocate a
portion of millimeter-wave (mmWave) frequency bands to HAPs for worldwide
usage. An ITU-R “work in progress” report under WRC-23 agenda item 1.4 will
explain the sharing and compatibility studies of HAPs as International Mobile
Telecommunications (IMT) base stations for mobile services in some frequency
bands below 2.7 GHz.

As for UAV networks, the spectrum is allocated in accordance with usage. The
control and non-payload communication (CNPC) link is established to ensure the
safe operation and efficient control of UAVs. As shown in Table VI, some
frequencies for UAV CNPC links are allocated with some usage restrictions.
Yet, partial frequencies fall in the bands allocated to fixed satellite
services. Then, the sharing compatibility issue between UAV CNPC links and
satellite-ground links should be tackled. Payload links refer to the links
used for other radio equipment than those for CNPC links. The payload devices
(e.g., camera, repeater, and flying base station) are closely related to
missions, which can transmit measurement and perception data from a UAV to the
ground. Table VI also depicts the frequencies allocated to UAV payload links.

TABLE VI: Spectrum regulation of UAV networks
---

###  II-D Promising Use Cases of NSIN

As presented above, airborne platforms in NSIN can fulfill various roles. By
functioning as aerial macro-base stations, they have the ability to offer
cost-effective communication services over a wide coverage area. Specifically,
when the coverage radius of a HAP is 50 km and the maintenance cost is $500
per hour, the communication cost will be $1.325 million per gigabit per year.
It indicates that the coverage cost per unit area of a HAP is only 6.7‰ times
that of a ground macro-base station. They can act as mobile relays to connect
isolated information islands, collect data, and deliver data back to TN or NTN
gateways. For instance, airborne relays can be deployed to facilitate
communication between offshore vessels. Particularly, vessels within 200 km
offshore can be interconnected by deploying two HAP relays when the coverage
radius of a HAP is 50 km. Besides, by deploying 130 HAPs, any two vessels
within 200 km offshore of China can be connected. Airborne platforms can serve
as aerial edge computing nodes [43, 44, 45], effectively reducing network
latency by shifting computing capabilities from the cloud to the network edge.
Enabling the ability of edge computing, one can regard NSIN as content
delivery networks (CDN), which enable low-latency and high-interaction content
delivery services. NSIN can provide network access services for rural or
remote areas with inadequate communication infrastructures [46]. They can also
act as caching nodes [47, 48, 49]. By caching content in NSIN, the network
resource allocation and service abilities of data centers can be proactively
optimized according to users’ QoS requirements. The aforementioned essential
functions of airborne platforms provide NSIN with promising application
prospects for various critical use cases.

Emergency communication: Emergency communication is one of the most important
goals of deploying NSIN. NSIN is resistant to natural disasters and
geographical limitations. When TN are disrupted due to earthquakes, floods,
mudslides, and other disasters, NSIN can be quickly deployed to recover
communications and establish information transmission channels for emergency
rescue. Additionally, NSIN can act as complement radio access networks to
sustain services of TN when terrestrial flash crowd traffic occurs in densely
populated areas [50].

QoE enhancement: One can deploy NSIN to assist TN in enhancing users’ service
of experience. Take video transmission as an example, I frames and P frames
are the basic data of video traffic. The codec compression ratios of I and P
frames are great, and thus we require great network bandwidth to deliver I and
P frames. The enhancement frames (i.e., B frames) are used for improving the
quality of received videos. The codec compression ratio of B frames is small,
and the required network bandwidth for the transmission of B frames is small.
One can then choose to deliver I and P frames via TN and transmit B frames
through NSIN. After combining I, P and B frames on the terminal end, users’
service experience can be greatly enhanced. In addition, for applications
involving video streaming and image rendering, real-time processes of such
operations as video coding and decoding, orchestration, geometrical
transformation, project transformation, and perspective transformation, are
crucial. By shifting the rendering ability from the cloud to the NSIN network
edge, the process latency can be greatly reduced, and then the potential of
enabling emerging time-critical applications such as extended-range (XR) or
metaverse via NSIN-assisted TN may be unlocked.

Cargo-UAV applications: It is envisioned that numerous cargo-UAVs will fly in
the airspace of densely populated cities. To ensure the safe operation of
these cargo-UAVs, reliable and low-latency communication links among them and
controllers (enforced by law) must be established, and a large amount of data
about them should be collected and analyzed continuously and timely. HAPs in
NSIN can be deployed to satisfy the above requirements perfectly. Owing to the
high deployment altitude, HAPs can establish communication links of good
quality with cargo-UAVs. Thus, controllers can control cargo-UAVs in a
reliable and low-latency manner via HAPs. The on-board processors of HAPs also
have strong computing and storage capabilities, which can handle the large
amount of data generated by cargo-UAVs timely.

ITS applications: NSIN can provide improved coverage and surveillance services
for intelligent transportation systems (ITS). UAVs in NSIN can achieve the
goal of local service enhancement, and HAPs in NSIN can provide wide coverage
services. Take railway transportation as an example, NSIN will become a new
regime for continuously monitoring the surroundings of railways and detecting
abnormal events around railway tracks. The service requests from high-speed
cars and trains in ITS are increasing with the proliferation of ITS services.
However, it is hard for stand-alone TN to provide low-latency and continuous
coverage services for these transportation vehicles. To satisfy their
requirements, these vehicles can establish connections with suitable network
platforms in NSIN. For instance, high-speed cars and trains do not need to
frequently hand over between access points by accessing HAPs. This reduces the
signaling overhead and handover latency significantly.

Communications among Non-Terrestrial Platforms: As is known, 71% of the
earth’s surface is covered by the ocean. Humans have only explored and charted
5% of the ocean, and there are rich resources waiting to be explored in the
ocean. As a result, more and more fixed or mobile offshore platforms (e.g.,
gas and oil rigs, scientific expeditions, and surveillance platforms) are
being established to exploit the abundant resources on the seabed and explore
the unknown environments. Nowadays, these platforms exchange data with their
terrestrial controllers through renting satellite links, which inevitably
results in long transmission latency. NSIN, which is much closer to these
platforms, can be deployed as relay networks to establish the connection
between them and their controllers. In this way, the end-to-end (E2E)
transmission latency among these platforms and controllers can be
significantly reduced.

Meteorological monitoring: Meteorological monitoring (e.g., typhoon
monitoring) is of great importance in both civilian and military research
fields. Most existing meteorological monitoring systems are space- or ground-
based. However, space-based systems face limitations in ensuring continuous
monitoring due to satellite orbit constraints. Ground-based systems have
limited detection ranges and poor flexibility. NSIN-based monitoring systems
can overcome these shortcomings by offering continuous monitoring capabilities
throughout the entire life cycle of climate phenomena like typhoons, snow, and
rainfall. Therefore, the development of NSIN-based meteorological monitoring
systems is expected to be a significant trend in the near future.

Proliferation of IoT services: Existing terrestrial infrastructures are
limited and increasingly unable to support the highly demanding IoT services.
For example, more and more IoT devices are expected to be deployed in such
areas as forests, mountains, and oceans where there is no or rare TN coverage.
In this context, NSIN is envisioned as a key infrastructure to complement
existing terrestrial infrastructures to collect data or offload
computationally intensive tasks from IoT devices and charge them in an
efficient and cost-effective way [51, 52, 53, 37].

###  II-E Summary and Discussion

NSIN are significant heterogeneous and multi-layered networks consisting of
the HAP, HAUAV, and LAUAV network segments. There are many notable differences
between HAP and UAV, which prevent most protocols designed for UAV networks
from being applied directly to NSIN. Researchers and designers should conceive
protocols and network management architectures for NSIN that take full account
of the heterogeneity among airborne platforms as well as their cooperation.
Besides, there are many promising use cases for NSIN where HAP networks play
an irreplaceable and fundamental role. However, the impact of the unstable
movement of HAP on the performance of HAP networks is seldom studied. The
monitoring data from our field experiments showed that an airship rotated
around one revolution per minute, and its pitch angle could vary by up to
about +/-30∘. The unstable movement of the platform can result in position
shifting and deformation of the HAP’s footprint. As a result, users close to
the boundary of the footprint will frequently trigger handoffs, and their QoS
requirements are hard to be satisfied. A HAP moves with six degrees of
freedom, i.e., X-direction, Y-direction, Z-direction, yaw, roll, and pitch.
The movement of the HAP along the X- and Y-directions will not affect the size
of the footprint, but rather its location. The movement along the Z-direction
will result in the expansion or contraction of the covered area. Further, the
changes in its footprint are closely related to the installed antenna type of
the HAP. For instance, given a high-directional antenna, the area of the
coverage area expands with the uplift of the HAP; however, in the case of a
low-directional antenna (or omnidirectional antenna), the area of the coverage
area shrinks with the uplift of the HAP. Meanwhile, in the case of rotation,
beams will change, and beamforming technologies should be enabled to resist
the impact of platform rotation.

##  III Channel Modeling of NSIN

A good understanding of the channel characteristics of NSIN, key physical
parameters of the channel, and channel modeling issues lays the foundation for
the design of NSIN. The signal propagation environment of NSIN is quite
different from that of TN, satellite networks, and UAV networks. The NSIN
propagation environment goes through the stratosphere and troposphere, where
the temperature, humidity, and atmospheric pressure vary greatly. Besides, the
platforms in NSIN have special SWMAP characteristics. As a result, existing
channel models of terrestrial, satellite, and UAV networks may not be directly
applied to model channels of NSIN.

In this section, we first discuss the phase delays of different types of
antenna arrays onboard NSIN platforms. Next, we discuss the channel
propagation characteristics of NSIN from both large-scale fading and small-
scale fading perspectives. Third, we provide a detailed explanation of the
existing channel models customized for NSIN.

###  III-A Phase Delay

Let ${\bm{r}}_{m,n}=[x_{m},y_{n},z_{mn}]^{\rm T}$ denote the location vector
of the $(m,n)$-th array element with respect to (w.r.t) a reference array
element and
${\bm{d}}=[\cos\varphi\sin\theta,\sin\varphi\cos\theta,\cos\theta]^{\rm T}$
the DoA unit vector with $\theta$ and $\varphi$ representing the elevation
angle and the azimuth angle, respectively, the phase delay of the $(m,n)$-th
array element w.r.t the reference array element can then be given by

${\tau_{m,n}}=-\beta\left\langle{{\bm{d}},{{\bm{r}}_{m,n}}}\right\rangle$ (1)

where $\left\langle\cdot\right\rangle$ represents the inner product operation
and $\beta=2\pi/\lambda$ with $\lambda$ being the carrier wavelength.

The $(m,n)$-th element of the array matrix can be expressed as

$[\bm{a}(\theta,\varphi)]_{m,n}=\exp(j{\tau_{m,n}})$ (2)

Unfortunately, an airborne array antenna will jitter due to the unstable
movement of the airborne platform, which challenges the analysis of the phase
delay. In this subsection, we discuss the impact of jittering on phase delay.
Denote $\bm{R}$ as a rotation matrix capturing the attitude of an airborne
platform, which can be measured by yaw, pitch, and roll angles; we then have

${\bm{R}}={\bm{R}_{yaw}}(\alpha){\bm{R}_{roll}}(\beta){\bm{R}_{pitch}}(\gamma)$
(3)

where $\alpha$, $\beta$, and $\gamma$ represent the yaw angle, roll angle, and
pitch angle of an airborne platform, and

${{\bm{R}}_{yaw}}(\alpha)=\left({\begin{array}[]{*{20}{c}}{+\cos\alpha}&{-\sin\alpha}&0\\\
{+\sin\alpha}&{+\cos\alpha}&0\\\ 0&0&1\end{array}}\right),$ (4)
${{\bm{R}}_{roll}}(\beta)=\left({\begin{array}[]{*{20}{c}}{+\cos\beta}&0&{+\sin\beta}\\\
0&1&0\\\ {-\sin\beta}&0&{+\cos\beta}\end{array}}\right),$ (5)
${{\bm{R}}_{pitch}}(\gamma)=\left({\begin{array}[]{*{20}{c}}1&0&0\\\
0&{+\cos\gamma}&{-\sin\gamma}\\\
0&{+\sin\gamma}&{+\cos\gamma}\end{array}}\right).$ (6)

Then, the impact of attitude changes on the array element can be modeled as

${\bm{r}}_{m,n}^{\rm true}={\bm{R}}{\bm{r}}_{m,n}$ (7)

Based on the above derivation, we can summarize the expressions of phase
delays and steering matrixes of three typical types of antenna arrays,
including linear antenna arrays, rectangular antenna arrays, and circular
antenna arrays in Table VII.

TABLE VII: A list of phase delays and steering matrixes under three typical
antenna arrays

Array structure | Phase delay | Steering vector/matrix
---|---|---
|  | Case I (jitter free):
---
$\tau_{n}=-\xi(n-1)d\sin\theta$
Case II (jitter):
$\tau_{n}=-\xi(n-1)d({R_{22}}\sin\theta+{R_{32}}\cos\theta)$
$[{\bm{a}}(\theta)]_{n}={\frac{1}{{\sqrt{N}}}}{\rm e}^{-j\tau}$
|  | Case I (jitter free):
---
$\begin{array}[]{l}\tau=\underbrace{-\xi(m-\frac{{M-1}}{2})d\cos\varphi\sin\theta}_{{\tau_{m}}}\\\
\begin{array}[]{*{20}{c}}{}\hfil&{\underbrace{-\xi(n-\frac{{N-1}}{2})d\sin\varphi\cos\theta}_{{\tau_{n}}}}\end{array}\end{array}$
Case II (jitter):
$\begin{array}[]{l}\tau=\underbrace{\begin{array}[]{*{20}{c}}\begin{array}[]{l}-\xi(m-\frac{{M-1}}{2})d({R_{11}}\cos\varphi\sin\theta\\\
+{R_{21}}\sin\varphi\sin\theta+{R_{31}}\cos\theta)\end{array}\end{array}}_{{\tau_{m}}}\\\
\begin{array}[]{*{20}{c}}{}\hfil\end{array}\begin{array}[]{*{20}{c}}{}\hfil\end{array}\underbrace{\begin{array}[]{*{20}{c}}\begin{array}[]{l}-\xi(n-\frac{{N-1}}{2})d({R_{12}}\cos\varphi\sin\theta\\\
+{R_{22}}\sin\varphi\sin\theta+{R_{32}}\cos\theta)\end{array}\end{array}}_{{\tau_{n}}}\end{array}$
|
$\begin{array}[]{*{20}{l}}{{\bm{A}}(\theta,\varphi)=}&{{{\bm{a}}_{x}}(\theta,\varphi)\times}\\\
{}&{{\bm{a}}_{y}^{\rm T}(\theta,\varphi)}\end{array}$
---
where,
${[{{\bm{a}}_{x}}(\theta,\varphi)]_{m}}=\frac{1}{{\sqrt{M}}}{e^{-j{\tau_{m}}}}$
${[{{\bm{a}}_{y}}(\theta,\varphi)]_{n}}=\frac{1}{{\sqrt{N}}}{e^{-j{\tau_{n}}}}$
|  | Case I (jitter free):
---
$\begin{array}[]{l}{\tau_{1}}=0\\\ {\tau_{m}}=-\xi
d\sin\theta\cos(\frac{{2\pi(m-2)}}{{M-1}}-\varphi),\\\
\begin{array}[]{*{20}{c}}{}\hfil&{\forall
m\in\\{2,\ldots,M-1\\}}\end{array}\end{array}$
Case II (jitter):
$\begin{array}[]{l}{\tau_{1}}=0\\\ {\tau_{m}}=-\xi
d\cos(\frac{{2\pi(m-2)}}{{M-1}})({R_{11}}\cos\varphi\sin\theta\\\
\begin{array}[]{*{20}{c}}{}\hfil&{+{R_{21}}\sin\varphi\sin\theta+{R_{31}}\cos\theta)}\end{array}\\\
\begin{array}[]{*{20}{c}}{}\hfil&{-\xi
d\sin(\frac{{2\pi(m-2)}}{{M-1}})({R_{12}}\cos\varphi\sin\theta}\end{array}\\\
\begin{array}[]{*{20}{c}}{}\hfil&{+{R_{22}}\sin\varphi\sin\theta+{R_{32}}\cos\theta),}\end{array}\\\
\begin{array}[]{*{20}{c}}{}\hfil&{\forall
m\in\\{2,\ldots,M-1\\}}\end{array}\end{array}$
| $[{\bm{a}}(\theta,\varphi)]_{1}={\frac{1}{{\sqrt{M}}}}$
---
$[{\bm{a}}(\theta,\varphi)]_{m}={\frac{1}{{\sqrt{M}}}}{\rm e}^{-j\tau_{m}}$,
$\forall m\in\\{2,\ldots,M-1\\}$

###  III-B Large-Scale Fading

Large-scale fading happens because electromagnetic (EM) waves between Tx and
Rx are blocked and/or shadowed by large objects (e.g., buildings, hills, and
trees). It therefore includes free space path loss (FSPL) and shadow fading.
The strength of large-scale fading is closely related to the distance between
Tx and Rx and is typically frequency-independent. Besides, rain, atmospheric
gases, and scintillation may lead to large signal fluctuations.

####  III-B1 Free space path loss

As the EM wave spreads from Tx to Rx over an increasing area, the transmitted
signal attenuates with increasing distance. Given a propagation distance $d$
(in meters) and frequency $f_{c}$ (in GHz), FSPL (in dB) is computed by

$FSPL(d,f_{c})=32.45+20\log_{10}(f_{c})+20\log_{10}(d)$ (8)

For a ground user, the distance $d$ determined by HAP deployment altitude
$h_{0}$ and an elevation angle $\alpha$ can be computed by

$d=\sqrt{R_{E}^{2}{{\sin}^{2}}\alpha+h_{0}^{2}+2{h_{0}}{R_{E}}}-{R_{E}}\sin\alpha$
(9)

where $R_{E}$ is Earth radius.

####  III-B2 Shadow fading

Shadowing is the deviation of the received power of an EM signal from its
averaged value. One can model shadow fading (in dB) using a zero-mean normal
distribution with a standard deviation $\sigma_{SF}^{2}$, i.e.,
$N(0,\sigma_{SF}^{2})$, where $\sigma_{SF}$ depends on the elevation angle,
carrier frequency, environment (e.g., urban and dense urban), and the
probability of LoS or non-line-of-sight (NLoS) propagation.

####  III-B3 Atmosphere attenuation

The atmospheric attenuation model characterizes the EM wave absorption of
atmospheric gases. Its strength depends on the carrier frequency, elevation
angle, transceiver deployment altitude above sea level, and absolute humidity.
This type of attenuation should be considered at frequencies above ten GHz.
Nevertheless, one should also calculate this type of attenuation at any
frequency above one GHz when the elevation angle between Tx and Rx is below
ten degrees. ITU-R P.676 (in Annex 1) outlines in detail the computing
procedure of atmospheric attenuation [54].

####  III-B4 Rain attenuation

Rain attenuation is a phenomenon relative to the EM wave frequency and the
rainfall rate. Rain causes attenuation in EM waves through the processes of
absorption and scattering. Rain attenuation is a critical factor to consider
for frequencies above six GHz. As for signal transmission in typhoon
environments, it is essential to calculate the rain attenuation for
frequencies in the several hundred MHz range, as the rainfall rate can reach
up to 72.8 mm/h [55].

ITU-R P.618 [56] described a comprehensive approach for estimating the long-
term statistics of rain attenuation, taking into account the factors of
frequency, rainfall rate, elevation angle, and polarization. In addition to
this, modeling rain attenuation can be achieved through the application of a
synthetic storm technique and the utilization of raindrop size distribution
models like lognormal and optimized models [57]. However, the mentioned rain
attenuation models are independent of time series. As a result, it is not
enough to design NSIN (especially the PHY and MAC layers of NSIN) using these
models. Then, one should propose time series rain attenuation models. ONERA
$n$-state Markov chain and the German Aerospace Research Center (DLR) rain
attenuation model are two types of well-known models of NSIN and satellite
communications. There are three components in an ONERA model. The $1^{\rm st}$
one describes a coarse attenuation time series with values of “rain” and “no
rain”. The $2^{\rm nd}$ one is a microscopic model describing attenuation time
series with high resolution. The $3^{\rm rd}$ one combines the two obtained
time series to generate a complete long-term attenuation time series [58]. The
DLR model comprises a generic component and a specific set of parameters.
These parameters enable the adjustment of the time series generator to
simulate scenarios with different frequencies, elevation angles, and climatic
zones [58].

####  III-B5 Tropospheric scintillation attenuation

Tropospheric scintillation attenuation indicates rapid amplitude and phase
fluctuations in received signals. Tropospheric propagation should be
considered for frequencies above six GHz, and the tropospheric scintillation
effect increases with frequency, being especially significant above ten GHz.
Sudden changes in the refractive index caused by variations in water vapor
content, temperature, and barometric pressure can cause signal fluctuations.
Additionally, the scintillation effect becomes more pronounced at lower
elevation angles due to longer propagation paths. The ITU-R P.618 presents an
accurate prediction method for the scintillation attenuation amplitude [59].
It shows that the tropospheric scintillation attenuation may be dozens of dB
in the case of a low elevation angle. The scintillation attenuation can be
negligible under a high elevation angle case (e.g., $>10^{\circ}$).
Nevertheless, one should also consider the tropospheric scintillation
attenuation in the case of a high elevation angle during rain.

###  III-C Small-Scale Fading

Small-scale fading is caused by the constructive and destructive interference
of multi-path signals between Tx and Rx. It depends on the carrier frequency
and occurs at a spatial scale approximately equaling to the wavelength of the
signal. Small-scale fading characterizes the rapid fluctuation in received
signal strength over short distances and short time periods. Small-scale
fading includes flat fading, frequency selective fading, fast fading, and slow
fading, which are affected by the EM propagation environment.

####  III-C1 Flat fading

In flat fading, the signal amplitude remains constant during each symbol
duration, but the amplitude and phase of the symbol may change over short
distances. To mitigate the impact of flat fading, some techniques, such as
diversity reception and error correction coding, should be utilized.

In flat fading, all frequency components of the received signal fluctuate
simultaneously and in the same proportion. As a result, this type of fading is
also called non-selective fading. One can model the flat fading channel using
a two-state model. This model distinguishes two states: a good state
corresponding to LoS and slightly shadowed channel conditions, and a bad state
corresponding to severe shadowed channel conditions. A semi-Markov model can
capture the state duration. One can utilize a Loo distribution to characterize
the fading in each state. The received signal consists of the direct path
signal and multi-path components. 3GPP TR 38.811 provides system-level
evaluations of flat fading [60].

####  III-C2 Frequency selective fading

Frequency selective fading occurs when the symbol duration is shorter than the
delay spread. It may lead to inter-symbol interference (ISI). An equalization
technique should be employed to mitigate the impact of this type of fading.
This type of technique utilizes a digital filter at Rx to counteract the
channel effect. Frequency selective fading, as the name indicates, affects
diverse spectral components of signal with varying amplitudes. After modifying
some crucial parameters related to NSIN scenarios, the procedure presented in
subsection 7.5 of 3GPP TR 38.901 [61] should be utilized to model the
frequency selection fading channel. The elevation angle between a ground user
and a HAP is a crucial parameter. NSIN scenarios with fewer clusters than
those specified in 3GPP TR 38.901 require the inclusion of angular scaling
factors in cluster generation [61].

####  III-C3 Fast fading

In a fast fading channel, the channel impulse response (CIR) exhibits rapid
variations within the symbol duration. Fast fading distorts the shape of the
baseband pulse, resulting in linear distortion that causes inter-symbol
interference (ISI). Adaptive equalization helps reduce ISI by eliminating the
linear distortion induced by the channel.

####  III-C4 Slow fading

In a slow fading channel, the CIR changes at a much slower rate than the
transmitted baseband signal. One can utilize receiver diversity and error
correction coding techniques to combat the effects of slow fading.

###  III-D Existing Channel Models of NSIN

To fully comprehend the capabilities of NSIN, it is crucial to have a profound
understanding of NSIN channel models. This paper focused on summarizing and
analyzing NSIN radio-frequency (RF) channel models, and the reader can refer
to [11] for HAP free space optical (FSO) channel models. Besides, NSIN channel
modeling includes HAP channel modeling and UAV channel modeling, where UAV
channel modeling has been extensively studied. In this subsection, we focus on
the discussion of HAP channel modeling and analysis of the differences between
HAP channel models and UAV channel models.

####  III-D1 HAP channel models

In the research community, recent HAP channel models can be primarily
categorized into two groups: deterministic models and stochastic models.

Deterministic model: A deterministic channel model requires constant
parameters (e.g., angle of arrival/angle of departure (AoA/AoD)) during
simulations [62] and leverages some methods (e.g., ray-tracing, digital map-
assisted, and two-ray[62]) to estimate the CIR or channel coefficients. The
accuracy of deterministic models relies on extensive and environment-specific
databases, which can characterize the terrain topography, the electrical
properties of buildings, and other obstacles. Besides, model parameter
adjustment according to propagation measurement data is required.

This type of HAP channel modeling method has attracted much attention. For
example, in [63], a ray-tracing-based propagation channel model between a HAP
and a ground mobile station was developed. This model examined the effect of
angle variations on propagation loss by utilizing a ray-tracing tool that
accounted for changes in elevation and azimuth angles. In [63], the authors
employed a ray-tracing technique to compute the power level and path loss
experienced by a mobile user, as mentioned in [64]. By analyzing the outcomes,
the propagation parameters, including path loss and Rice factor, were derived
as functions of elevation and azimuth angles. In addition to evaluating angle
variations, the authors investigated the effects of building height and street
size on the propagation loss of downlink HAP channels in an urban environment.
They utilized a ray-tracing tool to conduct this study and published their
findings in [65, 66, 67]. The research presented in [68] examined the
propagation path loss of the downlink HAP channel using two-dimensional ray-
tracing and the uniform theory of diffraction. The study evaluated the effects
of wave reflection from building walls and the ground, as well as wave
diffraction. Diverse from the above channel modeling methods using ray-tracing
tools to simulate wave reflection and diffraction, the work in [69] studied
the impact of terrestrial clutter on downlink HAP propagation loss and
developed a HAP propagation path loss model including free space path loss and
clutter-induced attenuation using the ray-tracing data.

Statistical model: Without using measurement data, a statistical model
utilizes statistical distributions and empirical parameters to capture the
characteristics of propagation channels in a low-complexity manner and
includes the empirical channel model, the geometry-based stochastic model, and
tapped delay line (TDL)-based stochastic model.

In empirical channel model methods, some empirical channel fading models
(e.g., Rician fading channel) are applied to compute the propagation loss of
downlink HAP channels [70]. For instance, in [71], the downlink HAP
propagation channel loss was calculated from the superposition of free space
(or LoS) path loss and additional shadowing (or NLoS) path loss, where the LoS
probability was determined by empirical parameters. The work in [22] modeled
the downlink HAP propagation loss as the superposition of free space path loss
and multipath and shadow fading, with the multipath and shadow fading being
determined by empirical parameters. Given the highly directional nature of EM
wave propagation in the high-frequency band, the research presented in [72,
73] developed a downlink HAP channel model that included both dominant LoS and
sparse NLoS propagation components. The work in [74] studied the mmWave
downlink HAP channel modeling, where rain attenuation was a dominant
propagation loss. Besides, as scattering was restricted at mmWave frequency
bands, the path loss was considered to be dominated by LoS propagation loss.
Therefore, the downlink HAP propagation channel was modeled as a superposition
of rain attenuation and LoS path loss. Some studies, such as [75, 76, 77, 78,
79], have utilized the Rician fading channel to model the downlink HAP channel
due to its ability to consider both LOS and NLOS components. Furthermore, as
the LoS component is often obstructed by various obstacles such as buildings,
trees, hills, and mountains, the effect of shadow fading has been highlighted
in some studies. To model the downlink HAP channel, shadowed-Rician fading was
utilized in [80, 81]. Shadowed-Rician fading is a more generalized form of
Rician fading, which assumes that the amplitude of the LoS component follows a
distribution that is different from the Rician distribution. The multi-state
HAP statistical channel model [82, 83] was developed based on the HAP
propagation channel models mentioned above. This model could simulate more
downlink HAP propagation states, such as LoS, shadowed, and blocked states. In
this type of channel model, different fading models were utilized to simulate
diverse propagation states, and a Markov chain was explored to theoretically
model and analyze the state transitions. Besides, the work in [84] analyzed
the channel model between HAPs, which took into account the antenna radiation
pattern, the effects of atmospheric factors, and the polarization mismatch of
the Tr and Rx antennas.

Geometry-based stochastic modeling methods capable of capturing
spatial–temporal downlink HAP channel characteristics were derived from a
predefined stochastic distribution of effective scatterers through applying
the fundamental laws of EM wave propagation [85]. The accuracy of this type of
channel model is highly dependent on how the scatterers around the Tx and Rx
are modeled. The scatterers are typically assumed to be distributed in either
regular geometric shapes or irregular shapes. For example, multipath
propagation models for downlink HAP channels based on (improved) circular
straight cone geometry were developed in [86, 87], respectively. The
propagation of reflected signals was modeled by taking into account all
scatterers within the coverage area of a HAP system. 3-D geometry-based
reference models for downlink HAP channels were proposed in [88, 89, 90] with
the assumption that the scatters in the vicinities of Rx and or Tx were non-
uniformly distributed within a regular geometry shape(s). In contrast to the
studies conducted in [88, 89, 90], which analyzed the impact of non-isotropic
scattering environments, the approach presented in [91] introduced a 3-D
geometry-based reference model that took into account both isotropic and non-
isotropic scattering for HAP channels. Moreover, the dynamic behavior of
scatterers and the potential for scatterers to reappear after they disappeared
were explored in [92, 93]. By analyzing the dynamic evolution of scatterers,
3-D geometry-based reference models were developed for HAP channels.

Stochastic models for HAP channels can be created by utilizing a TDL method
with varying numbers of taps. Each tap in this model can represent the fading
statistics of the multipath components of HAP channels. Empirical measurements
and numerical simulations can be used to analyze the fading statistics of
individual taps. For instance, considering the multipath effect and the
inherent dispersion characteristics of signals at different propagation paths,
the works in [94, 95] proposed to use TDL to model HAP mobile channels in a
wide-area environment.

####  III-D2 UAV channel models

UAV channel modeling is critical for the performance analysis and system
design of UAV communication systems. Nevertheless, there are some challenges
in UAV channel modeling; for example, the propagation features of UAV channels
are under-studied for spatial and temporal variations in non–stationary UAV
channels. The airframe shadowing and jittering are also under-explored. In
recent years, there has been a significant increase in attention from both
industry and academia towards the research on UAV channel modeling. In
industry, some technical standards for UAV channel modeling have been released
by Nippon Telegraph and Telephone Corporation (NTT), Nokia, Ericsson, Intel,
and Huawei, and so on [96, 97, 98, 99, 100]. In academia, many surveys on UAV
channel modeling have been published [101, 102, 103]. Some surveys, including
[101, 102, 103], have summarized the available UAV channel measurement
campaigns, as well as various large- and small-scale UAV fading channel
models. Besides, some important issues (e.g., airframe shadowing, Doppler
spread, blockage, scattering, and delay dispersion) in UAV channel modeling
were discussed.

####  III-D3 Differences between HAP and UAV channel models

There are many significant differences between HAP channel modeling and UAV
channel modeling, for example, whether consider the impacts of meteorological
factors and airframe shadowing on channel modeling. Restricted by the
deployment altitudes of UAVs and the allocated frequencies, few studies on UAV
channel modeling [104, 105, 106] have taken into account the influence of
meteorological factors, such as atmospheric absorption, rain and cloud
attenuation, and tropospheric scintillation. On the contrary, the
investigation of the influence of meteorological factors is a highly
researched topic in the field of HAP channel modeling. Particularly, at
frequencies above 10 GHz, the atmospheric absorption must be calculated [107].
Meanwhile, for elevation angles below 10 degrees, the atmospheric absorption
is recommended to be calculated for frequencies above 1 GHz. At frequencies
above 6 GHz, it is recommended to calculate rain and cloud attenuation.
Similarly, at frequencies above 6 GHz, tropospheric scintillation
corresponding to rapid amplitude and phase fluctuations of signals should be
calculated. Furthermore, due to the longer transmission path, the influence of
tropospheric scintillation becomes more pronounced at small elevation angles,
especially for high carrier frequencies.

The impact of airframe shadowing is seldom considered in HAP channel modeling.
However, in UAV channel modeling, the research on the airframe shadowing
impact has attracted much attention. In UAV communications, airframe shadowing
occurs when the UAV-to-ground (UtG) path is blocked by UAV maneuvering,
banking turns, or UAV structures such as wings, fuselage, and engines. The
shadowing results depend on some factors, like carrier frequency, antenna
placement, and the exact shapes, sizes, and materials of UAVs.

###  III-E Summary and Discussion

The signal propagation environment of NSIN differs significantly from that of
space networks, UAV networks, and terrestrial wireless networks. As a result,
the existing channel models for these network types may not be directly
applicable to NSIN. The channel modeling of NSIN should consider the phase
delay of deployed antennas (which may be greatly affected by unstable platform
movement), atmosphere attenuation, rain attenuation, tropospheric
scintillation attenuation, the effect of multipath, and Doppler frequency
shift. In this section, we provide detailed explanations of phase delay
modeling under platform jitter, the influence of atmospheric effects on NSIN
channel models, and the small-scale fading of NSIN channels. Besides, we
reveal the significant differences between HAP and UAV channel models and
review recent efforts in HAP channel modeling. However, there is a lack of
studies on the high-accurate channel estimation for HAP networks. Most
existing HAP channel models are established based on ray-tracing tools or
statistical/empirical distributions. There is a need to enhance the accuracy
of the channel model by mining and analyzing field measurement data, which
accurately reflects the realistic behaviors of NSIN propagation channels.
However, due to the high cost of conducting extensive HAP channel measurement
campaigns and certain restrictions imposed by aviation regulations,
researchers have made limited contributions to measurement-based HAP channel
modeling.

##  IV Networking of NSIN

In this section, we elaborate on the networking approaches for NSIN, mainly
from the perspectives of constructing and managing NSIN.

###  IV-A Deployment of NSIN

####  IV-A1 QoS-driven NSIN deployment

QoS-driven NSIN deployment refers to the optimization of the deployment
locations or trajectories of aerial platforms in NSIN based on space-time
distributions of serving users in a considered area, the goal of which is to
meet the users’ QoS requirements. Typically, users’ QoS requirements can be
characterized as their received signal-to-noise ratio (SNR) or achievable data
rates. Nevertheless, it is difficult to solve the QoS-driven NSIN deployment
problem. First, NSIN consists of multiple sub-networks deployed at different
altitudes. The sub-networks have many differences in capabilities, such as
payload, communication, and maneuverability. Therefore, studying the QoS-
driven NSIN deployment problem means a solution of a joint optimization and
deployment problem of a stereoscopic, multi-layered, heterogeneous network.
Secondly, the airspace environment in which NSIN are deployed is complex and
constantly changing. The airspace needs to be shared by the aerial platforms
of NSIN and civil aviation aircrafts. There are also many unknown and
uncertain risk areas in the airspace. Therefore, studying the QoS-driven NSIN
deployment problem needs to explore how to ensure the flight safety of aerial
platforms. To meet the above challenges, it is first necessary to
mathematically model the stereoscopic, multi-layered, and heterogeneous
characteristics of NSIN. A feasible approach to reflecting the stereoscopic
and multi-layered characteristics of NSIN is to define different coverage or
footprints for various platforms, design a time-varying sub-network selection
scheme, and establish a multi-category signal interference regime. In order to
characterize the heterogeneity of aerial platforms, a viable method is to
constrain the service capabilities of platforms from the perspective of
limiting available computing, storage, and network resources. Additionally, it
is necessary to plan offline or online trajectories for NSIN platforms by
detecting the surrounding environment. Next, formulating and solving the NSIN
deployment problem to meet the user’s QoS requirements, subject to the
constraints on the stereoscopic, multi-layered, and heterogeneous
characteristics and the flight safety requirement. The formulated optimization
problem is typically a multi-objective, multi-constrained, high-dimensional,
and serialized decision-making problem. Therefore, it is challenging to use
traditional optimization approaches to obtain the optimal or sub-optimal
solution. Instead, intelligent optimization, reinforcement learning, and graph
neural networks are better approaches to solve this type of problem.

The QoS-driven NSIN deployment problem is currently under active research, and
a number of related studies have been published in the literature during the
past few years [108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
120, 121, 122, 123]. For instance, the work in [108] formulated a HAP
constellation deployment problem with a goal of maximizing the network
capacity under the constraints on QoS metrics that were characterized by the
efficiency and reliability of communications. To efficiently search for
deployment locations of HAPs, an improved artificial immune algorithm was
designed. The works in [109, 110, 111] proposed to deploy a near-space
communication system to meet the QoS requirements of ground users. The
following problem was studied: how to strike a balance between the flying
speed of a near-space platform and the good coverage for users subject to a
complex distribution? The authors transformed the conflict between flying
speed and coverage into a multi-objective problem and developed various multi-
objective optimization evolutionary algorithms based on decomposition to solve
this kind of problem. The issue of deploying a stand-alone HAP and multiple
UAVs to provide communication coverage or partial computing services for
ground users also attracts much attention from the research community [112,
113, 114, 115]. For instance, the authors in [112] designed an NSIN
architecture to provide reliable communication services for terrestrial
vehicles. In this architecture, vehicles would receive both signals directly
from a HAP and relayed signals from multiple UAVs. To enhance the spectral
efficiency and downlink transmission rate of NSIN, researchers investigated
the joint design of non-orthogonal multiple access (NOMA) and UAV location
optimization. Furthermore, in [116], the joint optimization of UAV trajectory
and phase shift of RIS was studied to maximize the received SNR of terrestrial
users. An alternative optimization algorithm was designed to optimize the UAV
trajectory and adjust the RIS phase shift. In [117], researchers proposed a
HAP deployment algorithm that aimed to maximize the ratio of the HAP footprint
to the inter-HAP distance. The works in [118] designed a HAP-assisted wireless
sensor network (WSN) architecture to reduce the energy consumption of sensors
and boost the coverage of the WSN. A potential game approach was developed to
determine the deployment locations of HAPs so that the above goal could be
achieved. In [119], the deployment of HAPs for providing communication
services to terrestrial users was formulated as a potential game. To identify
the deployment locations of HAPs that could satisfy the QoS requirements of
more users, a restricted spatial adaptive play learning algorithm was devised.
The work in [120] investigated the methodology of HAP deployment to guarantee
ground users’ QoS requirements.To optimize the locations of HAPs, an enhanced
artificial immune approach based on immune review was developed. Additionally,
the deployment scenario of a HAP system for quick communication recovery in a
disrupted communication area was presented in [121]. To guarantee the QoS
requirements of users in a given area, a machine learning-based propagation
model was developed to optimize the deployment altitude of a HAP.

####  IV-A2 QoE-driven NSIN deployment

With the rapid growth of streaming services, it becomes increasingly important
to focus on the subjective experiences of users. QoS metrics can only measure
network performance and cannot reflect users’ subjective feelings about
service quality. The concept of QoE was proposed to tackle this issue. QoE
reflects the subjective feelings of users watching videos and becomes the main
performance evaluation criteria for video service quality.

Research on QoE in wireless communications is a hot and under-studied topic.
There is a consensus that a unified QoE evaluation model cannot be achieved.
This is because different video applications have diverse requirements for
video quality. For instance, short video applications focus on the opening
rate per second, high-definition long video applications have stringent
requirements for high bandwidth, video conferences, and online games pursue
low latency and high smoothness.

However, it is widely accepted that users cannot accept three categories of
feelings during video transmission, i.e., freeze, blur, and large delay.
Therefore, some underlying layer and upper layer mechanisms need to be
carefully designed so that users’ QoE requirements, including low latency,
high video quality, and smooth playback, can be satisfied.

It is easy to know that the QoE evaluation indicators are subjective. In this
case, one should analyze the relationship between users’ QoE and its
influencing factors and establish a mapping model between them. For example,
one can utilize the peak signal-to-noise rate (PSNR) to objectively measure
the quality of demodulated videos. To objectively measure the smoothness of
played videos, a viable approach is to design and optimize a logarithmic
utility function that takes into account factors such as the user’s achievable
data rate, desired playback rate, and application type.

It is not difficult to optimize a certain QoE evaluation indicator alone.
Nevertheless, it is highly challenging to simultaneously optimize all the
indicators owing to their mutual restrictions.

TABLE VIII: The recent studies on the QoE-driven UAV deployment in the literature. Refs. |  | Location/
---
trajectory
| Transmit
---
power
| Band-
---
width
Caching | MEC | Latency |  | Smooth
---
playback
| High
---
quality
| Single
---
UAV
| Multi-
---
UAVs
| Optimi-
---
zation
| AI/
---
ML
[124] | ✓ | ✓ | ✓ |  | ✓ | ✓ |  |  | ✓ |  | ✓ |
[125] | ✓ |  |  | ✓ |  | ✓ |  |  |  | ✓ | ✓ |
[126] | ✓ |  |  |  | ✓ | ✓ |  |  | ✓ |  | ✓ |
[127, 128] | ✓ | ✓ | ✓ |  |  |  | ✓ |  | ✓ |  | ✓ |
[129] | ✓ | ✓ |  |  |  |  | ✓ |  | ✓ |  | ✓ |
[130] | ✓ |  |  |  |  |  | ✓ |  |  | ✓ |  | ✓
[131] | ✓ | ✓ |  |  |  |  |  | ✓ |  | ✓ | ✓ |
[132] | ✓ | ✓ | ✓ |  |  |  |  | ✓ | ✓ |  | ✓ |
[133] | ✓ | ✓ | ✓ |  | ✓ |  |  | ✓ |  | ✓ | ✓ |
[134] | ✓ | ✓ |  | ✓ |  |  |  | ✓ |  | ✓ | ✓ |
[135] | ✓ |  |  |  |  | ✓ |  | ✓ |  | ✓ |  | ✓
[136] | ✓ | ✓ |  |  |  | ✓ | ✓ | ✓ | ✓ |  |  | ✓
[137] | ✓ | ✓ |  |  |  | ✓ | ✓ | ✓ |  | ✓ | ✓ |

There have been limited methods for QoE-driven HAP deployment thus far.
However, the research on QoE-driven UAV deployment has recently gained
extensive attention. Table VIII summarizes recent literature on QoE-driven UAV
deployment. For example, the works in [124, 125, 126] proposed to minimize the
video transmission latency by formulating and solving a UAV resource
optimization problem. In [124], the authors addressed the issue of jointly
optimizing computing offloading, UAV 3D trajectory, UAV transmission power
control, and system bandwidth to maximize computation efficiency and guarantee
users’ video transmission latency requirements. A latency-guided video
delivery framework was proposed for cache-enabled multi-UAV networks in [125].
The authors studied the optimization problem of joint UAV and cache placement
to minimize video delivery latency.

Different from [124, 125, 126], the works in [127, 128, 129, 130] proposed to
optimize UAV resources to ensure smooth video playback. In [127, 128], a
logarithmic function was used to model smooth playback utility with respect to
video transmission rate. The authors investigated joint optimization of UAV
transmit power, UAV trajectory, and system bandwidth to maximize the minimum
utility among all users, subject to constraints on UAV total power and video
playback. The work in [129] utilized the mean opinion score (MOS) to measure
the smoothness of playback video. The authors proposed to leverage a hybrid
deep reinforcement learning (DRL) approach to adjust UAVs’ moving directions
and distances to achieve energy-efficient and smooth video playback.

Several works including [131, 132, 133, 134] investigated the joint resource
optimization problem in UAV networks to ensure high-quality video reception by
users. In [131], the authors studied the joint optimization problem of UAV
trajectory and transmit power to maximize the minimum PSNR of users’ video
reconstruction quality. An iterative optimization algorithm was developed,
consisting of block-coordinated descent and successive convex approximation
schemes, to solve the problem. The authors in [132] proposed a joint
optimization approach to ensure the quality of received videos by users by
optimizing user scheduling, UAV trajectory, UAV transmit power, and bandwidth.
They developed an alternative optimization approach to solve the mixed-integer
and non-convex joint optimization problem.

However, the above works [131, 132, 133, 134] did not investigate the trade-
off among QoE indicators. To tackle this issue, the works in [135, 136, 137]
investigated the joint optimization of QoE indicators. For instance, the work
in [135] developed a UAV path planning scheme to deliver low-latency and high-
quality videos to users. The authors in [136] formulated the QoE by
considering three factors, including video quality, jitter between video
frames, and video latency. They jointly optimized the video resolution, UAV
trajectory, and UAV transmit power using the deep Q-network (DQN) and actor-
critic approaches to maximize the QoE of real-time video streaming.

###  IV-B Handoff Management

The handoff (or handover) means the shift of communication services for a
particular user from one platform footprint (i.e., cell coverage of a platform
on the ground) to another.

Both the mobility of serving users and the instability and mobility of
airborne platforms will trigger the handoff process. For instance, the
movement and mechanical vibrations of airborne platforms, as well as the
airflow in the troposphere and stratosphere, can cause changes in their
attitudes and locations. Consequently, the locations or shapes of cells may
also change. The unstable platform movement caused by airflow includes the
platform horizontal drift, vertical motion, rotation, and attitude angle
disturbance (i.e., yaw, pitch, and roll/swing) [138]. Nevertheless, there are
some differences in terms of the handoff frequency for users in the footprints
of airborne platforms. The handoff frequency is influenced by various factors,
including the distribution of users (e.g., inner users vs. edge users), the
speeds of both platforms and users (slow vs. fast), the coverage area of
platforms (large vs. small), and the status of platforms (e.g., normal vs.
abnormal operation). For instance, unstable platform movements will greatly
increase the handoff probabilities of edge users in a footprint.

Handoff types can be categorized into two groups: hard handoff and soft
handoff. In the case of a hard handoff, the session established by an old
network is terminated before it is re-established by a new network. The
situation is opposite in the case of a soft hand, where the session is
maintained by the new network before the old network breaks the session.

Besides, the handoff can be horizontal or vertical. Horizontal handoff
indicates the mitigation of a session from one footprint to another in the
same network. On the contrary, vertical handoff refers to the seamless
transition of a session between two heterogeneous networks.

The handoff process results in the mitigation of state information and PHY
layer connectivity from one footprint to another. Specifically, the handoff
process consists of three phases: footprint discovery, trigger, and execution
phases. During the footprint discovery phase, a user actively sends probes or
passively listens to broadcast service advertisements to discover several
footprints on assigned channels. According to the discovery, the user
establishes a list of candidate footprints prioritized by handoff criteria
(e.g., received signal strength (RSS), carrier-to-interference ratio (CIR),
congestion status, and surveillance information). During the trigger phase,
the decision to initiate a handoff is made based on various criteria, such as
sub-network rental fees, SNR, handoff latency, elevation angle, and the
availability of free channels. During the execution phase, the inter-footprint
mitigation of sessions takes place, ensuring the seamless transfer of all
contextual information related to the user.

A well-designed handoff algorithm is essential to reducing the overhead (e.g.,
session interruption, packet loss, handoff latency) of the handoff process
[139]. Therefore, we highlight some of the key handoff management strategies
developed for NSIN in the literature.

One of the significant goals of handoff algorithms is to ensure that the
disruption experienced by users during handoff is imperceptible. Recently, a
large number of methods have been introduced in the literature for reducing
the overhead during the handoff process [138, 139, 140, 141, 142, 143, 144,
145]. For example, the work in [138] investigated the horizontal handoff
between footprints served by the same HAP and the important problem of
controlling which footprint to switch to at which time. With the aid of the
measured RSS/CIR, a low-latency handoff algorithm was proposed. In this
algorithm, a time-reuse time-division multiple/time-division multiple access
(TDM/TDMA) frame structure was exploited. To maintain transparency in the
handoff process for users, a single-frequency variant was proposed in which
the HAP transmitted to or received from different spot beams (i.e.,
footprints) in various parts of the frame.

Handoff algorithms with a fixed RSS/CIR threshold, however, could not
effectively tackle the handoff issues in a HAP network. This is because the
HAP is usually in a quasi-stationary state, and the RSS of cell edge users in
the HAP will dynamically change. As a result, the fixed threshold setting will
cause frequent handoffs between cells. To address this issue, the work in
[146] proposed an adaptive handoff algorithm to trigger the handoff according
to predicted RSS by an improved least mean square (LMS) scheme. It was shown
that the adaptive handoff algorithm could significantly reduce the unnecessary
handoff time and keep the handoff failure probability at a lower level.

Except for developing some schemes to manage the handoff, many researchers
have paid much attention to the analysis of the impact of airborne platforms’
unstable movements on the handoff probability [147, 148, 149]. For instance,
the swing angle changes rapidly and unpredictably, making it challenging to
adjust the parameters and orientations of airborne antennas in real-time to
mitigate the impact caused by the swing. To this end, the work in [147]
discussed the swing state of a HAP, analyzed the impact of the swing state on
the handoff, and derived the average and maximum handoff probabilities. A
novel HAP swinging model based on fixed angle beamforming was proposed in
[148]. Owing to the platform swing, the so-called “Ping-Pong handoff” would
happen. Then, the authors analyzed the impact of platform swing in two cases:
1) all users executed the handoff immediately when the swing happened; 2) all
users refused to handoff (if triggered) to avoid the “Ping-Pong handoff”.
Furthermore, in [149], the issue of yaw shift of HAP was addressed, and its
influence on handoff was examined. A formula was derived to calculate the
number of handoff calls, which indicated that various factors such as users’
density and location distribution in the cell, HAP’s yaw angle, cell geometry,
and the number of active calling users could impact the number of handoff
calls.

In the above studies [138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149], a simple coverage model such as the circular and regular hexagonal
coverage was introduced to mathematically analyze the handoff. While the
mathematical analysis is simplified, it poses challenges to validating the
derived conclusions in practical engineering scenarios. Therefore, some
handoff algorithms based on irregular or inconsistent cell shapes were
developed [150, 151, 152]. The work in [150] proposed a smart cell shape
design for handoff management in a HAP network. Using user distribution and
behavior data, the smart network design divided the coverage area into smaller
spots and synthesized the desired cell by grouping some of these spots
together. Consequently, the frequent handoffs from moving users could be
significantly reduced. To investigate various issues related to the horizontal
HAP handoff process, the work in [151] designed inconsistent footprint shapes
for users locating at different positions of the service area using
directional antennas. Based on the results, it was found that a wide beam-
width antenna was suitable for serving users located in the center of the
service area, whereas users located at the edge of the service area would
benefit from a narrow beam-width antenna. To address the frequent handoff
issue caused by the user movement as well as the unstable HAP movement, an
adaptive handoff scheme with an overlapped footprint design was developed in
[152]. Particularly, this paper utilized the mechanism of cooperative
transmission between two HAPs to guarantee the QoS requirements of users at
the edge of the coverage area. To improve transmission reliability, the HAP
with a higher channel gain was selected for cooperative transmission.
Additionally, handoff decisions were made based on the user’s movement
direction and channel gain to minimize session interruption time caused by
frequent handoffs.

In the above studies [150, 151, 152], single-metric (e.g., RSS/CIR) or user-
side multi-metric (e.g., user distribution and user movement direction)
handoff decision algorithms were designed. However, the metrics on the
airborne platform side play a crucial role in the handoff decision-making
process. For instance, when multiple users make handoff decisions
simultaneously, solely relying on the RSS-based handoff algorithm can lead to
HAPs with high RSS overload and HAPs with low RSS idle, potentially causing
load imbalance within HAP networks. This, in turn, can result in network
congestion and packet loss. Therefore, some handoff algorithms considering
metrics on the airborne platform side were designed [153, 154]. For instance,
a load-balancing handoff algorithm considering both the RSS of users and the
residual energy of HAPs was developed in [153]. To minimize handoff failures
during changes in network traffic load, a fuzzy channel allocation scheme was
introduced in [154]. This scheme employed a guard channel policy and
dynamically adjusted the optimal number of reserved channels based on QoS
parameters. By efficiently allocating channel resources, the scheme
prioritized ongoing calls over new calls.

Diverse from the above handoff schemes, local and global surveillance
information collected by airborne platforms was utilized to assist the
handover decision-making in [155, 156, 157]. Particularly, to decrease handoff
frequency, local information such as the broadcast location, velocity, and
flying direction of the airborne platform was leveraged to predict its flight
trajectory. A global information-supported machine learning (ML) method could
effectively predict future traffic loads, and then a joint decision on handoff
and resource allocation could be made. Additionally, in order to ensure
uninterrupted UAV connectivity within an NSIN-integrated network, a predictive
decision algorithm based on global air control information and a handoff
strategy were devised in [158]. Leveraging the global air control information,
the predictive algorithm determined available access points for UAVs to
establish new links or recover from old links in future time slots.
Complementing the predictive algorithm, the handoff strategy seamlessly
maintained UAV connectivity by transitioning among soft handoff, tracking hard
handoff, and non-tracking hardoff states based on the predictive results.

However, the important issue of secure handoff was not investigated in the
above studies. Some recent studies discussed the secure access authentication
issue in the handoff process [159, 160, 161, 162]. Ensuring fast and secure
handover is crucial for maintaining seamless coverage of NSIN over a wide
area. The work in [159] investigated the optimization of the handoff latency
with a given security requirement in an NSIN-integrated network. A new handoff
approach that utilizes pre-authentication and security context transfer was
suggested. The proposed scheme distinguishes between two types of handoffs:
intra-domain and cross-domain. In the case of intra-domain handoff, the user
only needs to initiate an application to the controller. However, for cross-
domain handoff, the user must initiate an application to the management
center, which involves saving all relevant information related to the NSIN-
integrated network. To reduce the handoff latency while ensuring security,
some temporary proxy certificates were transferred in advance. Meanwhile, to
overcome the shortcomings of large handoff latency because of security
requirements, the operations of platform trajectory prediction and pre-
authentication were proactively performed based on periodically updated
network status information. The authors in [160] also developed a secure and
lightweight access authentication scheme for handoff management in an NSIN-
integrated network. To minimize authentication delay and signaling overhead
during the handoff process, the access authentication scheme for re-
authentication message transmission adopted a multicast communication mode.
The authentication process consisted of three phases: initialization,
registration, and multicast authentication. In the initialization phase, to
reduce the handoff latency, the handoff sequence was assigned in advance
according to the proxy authentication center (PAC) coverage area. In the
registration phase, to reduce the handoff latency caused by the configuration
of link local addresses between users and PACs, different PACs would maintain
the same link local unicast address. During the authentication phase, a
multicast authentication method was employed to minimize the number of
unnecessary access authentication messages.

###  IV-C Network Management

The recent studies on NSIN network management can be classified into network
topology management and integrated network management.

####  IV-C1 Network topology management

Compared to space networks and TN, NSIN possess distinctively dynamic
connections and topologies. For example, the flight trajectories of some
platforms may be random in a 3D airspace, resulting in intermittent and
unpredictable connections. The operational duration of flight platforms in
NSIN is significantly shorter compared to network nodes in space networks and
TN. In this case, NSIN should be resilient to the dynamical topology to
sustain service provision. Besides, as en essential component of 6G networks,
NSIN should be designed to efficiently support multiple types of traffic
concurrently. Adopting self-organization mechanisms holds promise for NSIN to
be resilient to topology changes and adapt to distinct service requests. These
mechanisms encompass self-configuration, self-healing, self-optimization, and
self-protection functions. Self-configuration indicates the automatic
recognition and registration of new neighbor nodes. Self-healing mechanisms in
NSIN automatically address node failures and connection interruptions,
ensuring uninterrupted services or mitigating service degradation.
Additionally, self-optimization mechanisms automatically optimize the
technical parameters of nodes to achieve specific objectives. Finally, to
ensure network security and data confidentiality, the self-protection
automatically protects NSIN from penetration by unregistered users. Let’s
illustrate the aforementioned process with an example. When a new node emerges
or an existing node fails, neighboring nodes undergo a neighbor discovery
process to acknowledge this change. Network modifications trigger a series of
message exchanges among nodes to reorganize the network. The next step is the
connectivity establishment of nodes during self-organization by executing
connection mechanisms in the PHY and upper layers. After establishing the
connections, the service recovery process will be performed to recover from
local service disruptions. Meanwhile, some optimization schemes, such as
energy optimization, will be implemented to implement network and service
optimization.

However, the task of designing dependable and effective self-organization
functionalities in NSIN is challenging. The processes of self-organization
require frequent measurements and probing. Then, the important issue of the
trade-off between the optimality of self-organization and the signaling cost
should be investigated. Incomplete, delayed, or even erroneous feedback may
happen, which may significantly impact the efficiency of self-optimization
processes and the service performance of the re-organized NSIN. Meanwhile,
ultra-reliable self-organization functionalities in NSIN should be designed to
minimize or avoid human intervention.

Some endeavors have been made to ease the design of self-organization
functionalities in NSIN [163, 164, 165, 166]. For example, considering the
unprecedented abilities of RIS in terms of enhancing spectral efficiency,
coverage expansion, and security enhancement, the work in [163] proposed to
utilize RIS technologies in NSIN-integrated air-space networks. With the
utilization of RIS, the quality of inter- and intra-layer communications was
improved, and the job of heterogeneous network self-organization was eased
[163].

By utilizing RIS to program the dynamic RF environment, the traditional
concept of cell boundaries could also be disrupted. Airborne platforms can be
configured as airborne-RISs to extend service coverage. Link disruption caused
by interference can also be mitigated by airborne-RISs. By analyzing a time
sequence link weight graph, the authors of [164] developed a link fault
recovery approach that targeted the unstable inter-domain neighbor
relationships, frequent routing updates, and slow routing convergence issues
in an NSIN-integrated space-ground network.The key idea of this approach was
to dynamically choose backup links and search for a recovery path with the
shortest length and the maximum path weight based on backup links when the
link failure occurred.

AI/ML techniques can also be explored to effectively tackle the important
issues in the self-organized NSIN. Deploying multiple HAPs as a network can
greatly boost communication coverage. However, the construction of HAP
networks is technically and economically challenging. The proposal for network
self-organization aims to eliminate the need for direct human intervention and
reduce operational costs. The work in [165] then applied reinforcement
learning (RL) and swarm intelligence approaches to solve the self-organization
and coordination problem of multiple HAPs for communication coverage. The work
in [166] investigated the coordination of multiple self-organizing HAPs in a
volcanic cloud emergency scenario for communication coverage. To coordinate a
swarm of HAPs, a swarm intelligence-based algorithm was developed. This
algorithm comprised different modes, including scouting mode, exploitation
mode, decision loop, and main exploitation mode. Similar to a foraging
behavior, the participating HAPs exchanged vital information as they explored
the environment. The HAPs achieved persistent and resilient communication
coverage by exchanging critical information and utilizing swarm techniques.
The swarm’s resilience was tested by simulating the failure of a participating
HAP.

####  IV-C2 Integrated network management

As mentioned above, NSIN are a type of heterogeneous, stereoscopic, multi-
tier, and time-varying networks. Different frequency bands may be allocated to
diverse sub-networks (or network segments), and the capabilities (e.g.,
communication, computing, and storage) of airborne platforms in different sub-
networks are heterogeneous; thus, the communication system of a specific sub-
network may be separate from other sub-networks. The separation of sub-
networks will significantly limit the reconfigurability and interoperability
of NSIN and greatly increase network management costs and complexity.

Exploring the concepts of software and virtualization, software-defined
networking (SDN) and network function virtualization (NFV) techniques can
redefine the NSIN architecture to tackle some challenges in NSIN, including
inconsistent air interfaces and complicated heterogeneity, as well as reduce
the management costs and complexity of NSIN. Furthermore, the flexible
integration of sub-networks in NSIN can be greatly enhanced by leveraging SDN
and NFV techniques. These techniques enable advanced network management
strategies, promote business agility, and facilitate service innovation.

On one hand, SDN facilitates centralized control in the data plane by
decoupling the controller from the switch. This grants the controller a
comprehensive overview of NSIN and enables the global management capability.
In the data plane, SDN-enabled platforms will receive instructions from the
centralized controller and do not need to understand diverse protocols. In
this way, the flexibility of managing these platforms can be significantly
improved.

On the other hand, NFV can implement various network functions through
visualization technologies and decouple network functions from proprietary
hardware devices. Thus, the dispatching of network functions as instances of
common software to service providers breaks through the barriers created by
dedicated hardware devices.

We propose an SDN- and NFV-based network management framework of NSIN for
providing flexible network services. In this framework, the data flow needs to
flow through a sequence of virtualized network functions (VNFs) to implement a
specific service. Different network services such as business management,
session management, mobility management, access control, service multiplexing,
and coverage maintenance can be implemented by diverse service function chains
(i.e., sets of ordered virtual functions with logical dependencies).

By exploring the concept of NFV, heterogeneous resources from diverse sub-
networks can be extracted into unified virtual resource pools for flexible
resource management, orchestration, and allocation. Separating the data plane
from the control plane enables the SDN controller to design efficient network
protocols and issue instructions for SDN-enabled platforms. This type of
framework enables flexible network reconfiguration, dynamically adapting
network service provisions to changing service requirements. When service
requests change or the capability of network service provisions changes (due
to dynamic network topology), reconfigured services can be easily implemented
by updating software or the sequence of VNFs.

Recently, many promising SDN and NFV solutions have been developed to manage
the integrated NSIN [141, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
177, 178, 179]. In [167], an SDN- and NFV-enabled system architecture
consisting of infrastructure, control, and application layers for NSIN-
integrated network management was designed. In the infrastructure layer, some
airborne platforms acted as SDN-enabled switches or gateways to forward data
packets based on flow tables. The infrastructure entities in each sub-network
were virtualized, and VNFs were deployed on demand. In the control layer, both
master SDN controllers and slave SDN controllers were configured to
effectively manage the network. The master SDN controller collaborated with
its slave counterparts to gather the global network status and oversee the
entire network topology for resource management at a high level, while the
slave SDN controllers undertook fine-grained resource management tasks. The
application layer would provide diverse programmable services and management
modules through some interfaces. The resource request issues of the
application layer would be interpreted by the interfaces into the instructions
of SDN controllers. In [175], an SDN/NFV-enabled NSIN-integrated network
management architecture was developed. To realize the SDN/NFV-enabled
architecture effectively, the crucial issues of the design of gateway
deployment policies, the optimal placement of SDN controllers, the embedding
of service function chains, and the establishment of traffic routes between
these chains were investigated. In [180], an integrated NSIN hierarchical
network architecture was proposed for beyond 5G (B5G) wireless networks. In
this architecture, a centralized SDN controller was embedded to manage the
operation of the whole NSIN and incorporated the cross-tier sub-networks at
the upper level. Three separate SDN controllers in the lower layer were
embedded into sub-networks for local network management (or fine-grained
management). In [181], an SDN-enabled NSIN-integrated network architecture was
developed for network management. Particularly, the optimal placement strategy
of an SDN controller for dynamic network topology management was studied.
Besides, an SDN/NFV-enabled NSIN-integrated network architecture was proposed
in [176] to implement unified network management and dynamic device
configuration. In the NSIN-integrated network, SDN controllers were deployed
to create SDN tunnels in the core network, and VNFs were deployed in the
clouds to enhance network flexibility and reliability.

###  IV-D Summary and Discussion

The deployment of NSIN is task-driven, and therefore, NSIN should be deployed
to satisfy the QoS and QoE requirements of tasks. It is essential to formulate
the NSIN deployment problem as an optimization problem. However, this type of
optimization problem is hard to formulate due to the multi-layered and
heterogeneous network structure as well as some restrictions on the flight
airspace and the platform itself. Therefore, great efforts should be spent in
the processes of modeling of problem constraints with consideration of network
characteristics, and novel approaches are desired to be designed to solve this
type of problem effectively. In this section, we discuss the existing works on
QoS- and QoE-driven NSIN deployment in detail, which may shed light on the
comprehensive modeling of NSIN deployment problems. Handoff has a significant
impact on the service performance of NSIN, especially for users located near
the boundaries of NSIN footprints. We explore the primary reasons for handoff
among NSIN-covered users, summarize the impact of handoff frequency, and
survey recent advancements in addressing handoff issues. We discuss the
network management of NSIN from the perspectives of network topology
management and integrated network management. Specifically, we provide
detailed explanations of the procedures involved in network self-organization
mechanisms for topology management, and we provide an overview of network
architectures for integrated network management based on SDN and NFV. Based on
our research on NSIN deployment, handoff management, and network management,
we can conclude that situational information, which includes security
situations and network situations, is crucial for designing effective
networking approaches. Yet, the research on situation-assisted NSIN networking
is still in its initial stage, and the accurate perception of situations in
NSIN is challenging. For instance, there are a large number of small threat
targets without obvious features, which makes it difficult to detect them in
time and accurately. Owing to its stereoscopic, multi-layered, and
heterogeneous characteristics, it is highly challenging to estimate the link
budget and network capacity of NSIN. Nevertheless, potential improvements in
network resource utilization and energy efficiency make it worthwhile to pay
more attention to situation-assisted NSIN networking.

##  V Transmission via NSIN

In this section, we overview the design of key technologies in the PHY layer,
MAC layer, network layer, and transmission layer that aim to solve the
effective transmission issue in NSIN.

###  V-A PHY Layer

Beamforming is one of the most significant techniques in the PHY layer. The
purpose of beamforming is to implement efficient coverage by electronically
controlling the amplitudes and phases of the weights of antenna array
elements. It can be divided into active beamforming and passive beamforming.

####  V-A1 Active beamforming

Active beamforming array is an architecture where RF amplifiers are utilized
as internal elements of an antenna array. According to the hardware structure
of the antenna array, the active beamforming architecture can be classified
into digital beamforming, analog beamforming, and hybrid beamforming.

Digital beamforming: For the digital beamforming architecture, all operations
(e.g., precoding, multiplexing, signal weighting, phase shifting) are
performed in the digital domain [182]. At the transmit end, amplitudes and
phases of digital signals can be simultaneously and flexibly adjusted. In
digital beamforming architecture, each antenna element has a dedicated RF
chain, enabling a variable number of beams based on the number of elements
forming the beam. Additionally, all beams can be steered, and algorithms can
synthesize a drive signal to optimize the desired characteristics of each
beam. Owing to its high flexibility in producing beams, many digital
beamforming methods for NSIN have been proposed in recent years [183, 184,