#  System Intelligence for UAV-Based Mission Critical with Challenging 5G/B5G
Connectivity

Cristiano Bonato Both  University of Vale do Rio dos Sinos - UNISINOS - São
Leopoldo, Brazil<EMAIL_ADDRESS>João Borges  Luan Gonçalves  Cleverson
Nahum  Federal University of Pará - UFPA - Belém, Brazil  Ciro Macedo  Federal
University of Goiás - UFG - Goiânia, Brazil  Aldebaro Klautau  Kleber Cardoso

###### Abstract

Unmanned aerial vehicles (UAVs) and communication systems are fundamental
elements in Mission Critical services, such as search and rescue. In this
article, we introduce an architecture for managing and orchestrating 5G and
beyond networks that operate over a heterogeneous infrastructure with UAVs’
aid. UAVs are used for collecting and processing data, as well as improving
communications. The proposed System Intelligence (SI) architecture was
designed to comply with recent standardization works, especially the ETSI
Experiential Networked Intelligence specifications. Another contribution of
this article is an evaluation using a testbed based on a virtualized non-
standalone 5G core and a 4G Radio Access Network (RAN) implemented with open-
source software. The experimental results indicate, for instance, that SI can
substantially improve the latency of UAV-based services by splitting deep
neural networks between UAV and edge or cloud equipment. Other experiments
explore the slicing of RAN resources and efficient placement of virtual
network functions to assess the benefits of incorporating intelligence in UAV-
based mission critical services.

###### keywords:

Artificial intelligence, mission critical services, UAVs, beyond 5G

††journal: Future Generation Computer Systems

##  1 Introduction

Annually, the economic losses from global disasters sum hundreds of billions
of dollars111https://ourworldindata.org/grapher/economic-damage-from-natural-
disasters and reap thousands of human lives. The impact on wildlife has more
diffuse numbers [1], but all of them expose a dramatic situation.
Communication systems are fundamental tools in these scenarios, since they
provide the means for proper coordination among the several teams involved in
diverse tasks, from saving lives to repairing infrastructures. More recently,
Unmanned Aerial Vehicles have been added as another valuable tools in the
context of Mission Critical (MC) services, offering help on localizing people
and animals, transporting medicines, improving communications, among other
relevant tasks [2]. For example, Search and Rescue (SAR) missions are services
usually performed in remote areas where the telecommunication infrastructure
is inoperative or severely damaged due to disasters, and UAVs can be extremely
helpful. However, the deployment and operation of UAVs in the Mission Critical
(MC) context still depends strongly in human intervention, what limits its
applicability and efficiency. Similar problem is observed with communication
systems, mainly involving data transport, which is becoming the most relevant
even in disasters scenarios. Considering these problems, standardization and
adoption of Artificial Intelligence/Machine Learning (AI/ML) solutions are
promising approach to tackle these issues [3].

In general, the investments in MC scenarios’ resources aim at preventing
losses, not in promoting profit. Therefore, it is essential to increase scale
and reduce costs in any MC-related solution. The adoption of worldwide
standards is a promising approach to achieve this goal. Additionally,
international cooperation in MC scenarios is quite common and standards become
again a natural choice. Nowadays, the 5th Generation (5G) and its evolution,
i.e., Beyond 5G (B5G), are a key set of standards in the general context and
also in the MC scenarios. MC communications have been a concern in the
standardization bodies since long time, however, the focus has been in a
specific set of MC services considered critical to teams involved in MC
scenario. Additionally, the support for UAV is very recent, only considered
effectively in the last 3rd Generation Partnership Project (3GPP) Releases, 15
[4] and 16 [5]. Nevertheless, these standards assume UAVs acting mainly as
User Equipment (UE) and commanded by human beings. While fleets of autonomous
UAVs, able to deploy and operate fully functional networks and services, have
been investigated in the literature [6, 7, 8], this context is still
considered futuristic and out of scope for the standardization bodies in the
near future. In this article, we argue that AI/ML can turn this vision into
reality sooner than is being expected.

AI/ML is already being widely adopted in UAV for autonomous operation [9].
Moreover, the standardization bodies of communication systems are in an
advanced stage of defining how to adopt and deploy AI/ML [10]. However, the
main efforts are focused on traditional infrastructure, i.e., without
considering UAV as part of the whole system and acting not only as UE but also
as part of the Radio Access Network (RAN) and even the 5G Core (5GC).
Furthermore, UAVs can offer edge computing resources, which enables several
new applications and services. Finally, UAVs can provide temporary
communication systems that are fully functional end-to-end. Software systems
such as 5GC and the virtualized and disaggregated Radio Access Network (RAN)
are receiving AI/ML updates to decrease the need for human intervention. While
these updates may be enough for traditional infrastructure, they cannot deal
with the challenging conditions faced by UAVs in MC scenarios. Additionally,
each AI/ML solution is limited to its related system, disregarding the broader
context that involves multiple interconnected systems, e.g., 5GC, RAN, and
Multi-access Edge Computing (MEC).

The contributions of this article are two-fold: (i) we introduce the System
Intelligence (SI) as an architecture for managing and orchestrating 5G/B5G
communication systems that operate over a heterogeneous infrastructure, which
includes UAVs. Besides describing SI and contextualizing it concerning
existing standards, (ii) this article also contributes with results obtained
with a testbed that supports experiments with UAV-based communications and AI
in future networks and computer systems.

The proposed SI must support advanced MC services in several scenarios,
including those in which only UAVs are available. SI focus on keeping all the
essential systems interacting properly and operating with the best performance
possible given the adverse conditions. Moreover, SI was designed following
standards, mainly the European Telecommunications Standards Institute (ETSI)
Experiential Networked Intelligence (ENI) specifications [10]. Regarding the
results, one of the experiments indicates that SI can improve MC services by
splitting neural networks between UAV and edge equipment, such that the
latency decreases by 29.87%. In another experiment, it is shown that
adequately slicing the resources leads to a decrease in average latency from
66 ms to 34 ms, approximately, when considering the downlink communication
between UAV and edge.

The remaining of this article is organized as follows. In Section 2, we
present the standardized approach’s background, considering the main
initiatives towards AI/ML solutions. In Section 3, we introduce our System
Intelligence solution, and in Section 4, we show the experiments with AI for
UAV-based SAR. We discuss the related work in Section 5 and present final
remarks and suggestions for future work in Section 6.

##  2 Background

In the last years, telecommunications standardization bodies, such as
International Telecommunication Union (ITU-T), ETSI, 3GPP, as well as
alliances between operators and manufacturers as Open Radio Access Network
(O-RAN), published specifications about the design, development, and deploy of
Artificial Intelligence (AI)/ Machine Learning (ML) for the 5G ecosystem and
also its evolution, i.e., B5G. Together, these specifications encompass a wide
scope, including the core, MEC, and RAN. Systems using AI in 5G/B5G networks
will surely be based on these specifications. However, most AI systems used in
5G are not compliant to standards at the current maturity stage yet. Moreover,
before AI systems become widespread, grasping all related specifications may
be a daunting task. The specifications overlap and have gaps concerning some
issues. Initially, this section briefly introduces the architectures defined
by these standards, which cover 5GC, RAN, and MEC, and also the integration
between them. After, the section presents a summary of the main initiatives
considering the AI/ML’s perspective for the 5G/B5G ecosystem. Besides
providing a short review of these specifications for the reader’s convenience,
it is also vital to contextualize the proposed SI and indicate what can be
used from the standards and what was missing.

The network system is mostly standardized by the 3GPP 5G, composed of the 5GC
built as a Service-Based Architecture (SBA) [4] and the Next-Generation Radio
Access Network (NG-RAN). An SBA allows flexible and stateless positioning of
virtual environments in the network segments that make up a 5G system.
Moreover, this architecture refers to how virtual networks’ functions are
created and deployed flexibly, widely using the concept of cloud computing to
develop, deploy, and manage services. In this context, Releases 15 [4] and 16
[5] introduce several features to 5GC that are useful in the context of MC
systems. For example, services can be implemented to expand the capabilities
of a MC system, decomposing functions with low granularity, making the service
light, and having a high capacity for sharing.

When contrasted with 3GPP specifications, the O-RAN Alliance introduced a
complementary set of NG-RAN standards, gaining relevant support from the
telecommunications industry. O-RAN addresses the split of the 5G base-station
(gNB) in three parts: (i) O-RAN Radio Unit (O-RU), (ii) O-RAN Distributed Unit
(O-DU), and (iii) Central Unit (CU). These splits for radio access
technologies can be designed, developed, and deployed for the sake of saving
costs or dealing with restrictions in energy consumption, such as in UAVs
networks. In this context, 3GPP and O-RAN define a disaggregated RAN, composed
of multiple Virtualized Network Functions (VNF). Therefore, we use the term
virtual RAN (vRAN) throughout the article to emphasize that both NG-RAN and
5GC can be implemented as a collection of VNF that must be appropriately
placed and chained to accomplish their tasks [11].

Figure 1: A standardized approach for supporting MC services based on the
integration of 3GPP 5G system (mainly core), O-RAN, and ETSI MEC system. The
AI/ML components already defined in the standards are highlighted in blue.

Given the importance of edge computing in MC scenarios, ETSI efforts in this
area are briefly described. ETSI specified a MEC system as software-only
entities to operate on top of a network edge’s virtualization infrastructure
[12]. This system consists of a platform to run applications on a particular
virtualization infrastructure, a platform manager to handle the specific
functionality’s management, and an orchestrator, which controls the whole
system and services. Moreover, the MEC system is aligned with the SBA
principles, and it was designed to be tightly integrated with 5GC [13]. Some
of the essential MEC services depend on this integration, with Application
Programming Interfaces such as Radio Network Information API, Location API, UE
Identity API, and Bandwidth Management API.

Figure 1 illustrates how MC services can be supported by a standardized set of
systems composed of a 5GC, vRAN according to the O-RAN architecture, and an
ETSI MEC system. All these systems can be seen as a large and complex
collection of VNFs that must adequately be managed and orchestrated to support
users’ application and services. The MC services add the challenge of managing
and orchestrating these VNFs over an infrastructure composed mainly of UAVs
and that can constantly change. Additionally, the figure illustrates how
interconnected are multiple systems. In fact, Service-Based Interface (SBI)
Network application function (Naf), and the reference points N1, N2, N3, and
N6 are only some of the interconnections among the 5GC, vRAN, and MEC system.
Finally, the figure highlights (in blue) the components related to AI/ML
already introduced by the standards.

UAVs have been used to aid military and rescue operations under challenging
areas [14, 15], performing several tasks, such as surveillance, inspection,
and mapping [16]. The presence of UAVs in these scenarios tends to increase,
with them being used for even more applications, including disaster management
[17, 18], performing MC tasks, leveraging the capacity of the new generation
of mobile networks. However, it is good to note that MC services provided in
disaster scenarios usually need to deal with reduced or even lack of radio
coverage [19]. For instance, recent hurricanes occurrences, such as Hurricane
Maria, resulted in the degradation of the functionality of about 95.6% and
76.6% of cellular sites in the affected areas [20]. In this context, the
rescue team needs to ensure that its UAVs will receive the radio resource
allocation dynamically during the mission through a network slice,
guaranteeing the necessary throughput and latency, for example, video
streaming and remote control.

In MC scenarios, 5GC, vRAN, and MEC must be improved to be operational under
the eventual harsh conditions imposed by the environment. In other words, MC
is challenging enough to require functionalities that are not still
incorporated in current specifications. When seeking a solution that supports
MC, part of this solution can be obtained from 3GPP specifications that
describe the 5GC architecture. For example, when considering the AI required
by MC systems, the component called Network Data Analytics Function (NWDAF) is
the approach of 3GPP for meeting the AI perspective in the 5G system written
in Release 15 [4] and 16 [5]. This component is responsible for collecting
several types of information from the network and its users. Any core
component and external access can consume the services provided by NWDAF. The
analytical data produced by NWDAF can be used by an AI agent that specifies
actions in the network context, for example, for UAV-based critical missions.
In this case, the 5GC plays proactively and takes real-time decisions to
provide the necessary services to 5G users, and NWDAF becomes a central point
for analytics in the 5G network.

Figure 2: Artificial intelligence standards for communication networks by ITU
and ETSI.

Complementing 5GC by 3GPP, the O-RAN architecture defines an AI perspective,
including the AI-enabled RAN Intelligent Controller (RIC) for both non-Real-
Time (non-RT) and near-Real-Time (near-RT) [9]. The non-RT functions include
service and policy management, higher layer procedure optimization, and model
training for the near-RT RAN functionality. The near-RT RIC is fit with radio
resource management and improves operational functions such as seamless
handover control, Quality of Service (QoS) management, and connectivity
management.

The specifications published by 3GPP and O-RAN are fundamentals and applied
for using of AI/ML solution on 5GC and vRAN, respectively. In a broader scope,
ITU-T and ETSI published a set of manuscripts to define terminology,
requirements, functionalities, and other essential concepts for characterizing
AI integration into communication networks. We organize the manuscripts from
these two standardization bodies considering the AI perspective in Figure 2
and summarize these specifications in Table 1. ITU-T introduces six main
specifications that cover AI in a next-generation network. These
recommendations focus on several aspects of the design of an AI framework. For
example, ITU-3111 presents the network management and orchestration framework,
and ITU-3170 shows the requirements for machine learning-based QoS assurance
for the network. Moreover, ITU-3172 introduces the architectural framework for
machine learning, and ITU-3173 discusses the framework for evaluating the
intelligence levels of networks. Furthermore, ITU-3174 considers the framework
for data handling to enable machine learning, and ITU-3175 covers the
functional architecture of machine learning-based quality of service assurance
in the networks.

Table 1: AI/ML specifications by ITU-T and ENI ETSI  Specification | Title
---|---
ITU-3111 | Network management and orchestration framework
ITU-3170 | Requirements for ML-based quality of service assurance for
| the IMT-2020 network
ITU-3172 | Architectural framework for ML in future networks including
| IMT-2020
ITU-3173 | Framework for evaluating intelligence levels of future networks
| including IMT-2020
ITU-3174 | Framework for data handling to enable ML in future networks
| including IMT-2020
ITU-3175 | Functional architecture of ML-based QoS assurance for
| the IMT-2020 network
ENI ETSI 002 | ENI requirements
ENI ETSI 003 | Context-Aware Policy Management Gap Analysis
ENI ETSI 004 | Terminology for Main Concepts in ENI
ENI ETSI 005 | Experiential Networked Intelligence - System Architecture
ENI ETSI 006 | Proof of Concepts Framework
ENI ETSI 007 | Definition of Categories for AI Application to Networks

Figure 2 also identifies some key ETSI documents. ENI ETSI 006 and 007 address
concepts and definitions of categories for AI applications to networks, such
as planning and optimization, service provisioning and assurance, data
management, operator experience, etc. Moreover, ENI ETSI 0055 presents a high-
level architecture for experiential networked intelligence [10], as well as
additional contents showing experimental network requirements (ENI ETSI 002),
context-aware policy management (ENI ETSI 003), and terminology for
experimental networks (ENI ETSI 004). This architecture is referred to as an
Assisted System (AS) composed of three classes that represent: (i) no AI-based
capabilities, (ii) AI is not in the control loop, and (iii) AI capabilities in
its control loop. Additionally, the architecture designs an API Broker to
serve as a gateway between different systems.

Even with such a relatively short review, it is clear that the body of recent
work aiming at defining how AI/ML should be used in future networks is
considerable. Before some of these recommendations become widely adopted in
the industry, it is essential to identify how they can be put together and
what remains to be defined. The next section uses some key aspects of these
recommendations and describes missing elements to compose an intelligent
system suitable for critical missions.

##  3 System Intelligence

In this section, we describe our proposal in detail, i.e., SI, and how it can
deal with the challenging scenarios previously introduced. Since adherence to
standards is one of our primary concerns, we built SI mainly in compliance
with ETSI ENI [21]. However, while ENI is a general-purpose abstract
specification, SI is an instantiation of the ENI ideas, being focused on MC
services scenarios and taking into account a complex infrastructure that
includes computing devices, gNBs, and, mainly, UAVs.

In Figure 3, we illustrate the introduction of SI and its integration with the
standardized systems previously presented. Similar to the ENI specification
[21], these systems that are managed by or receive recommendations from SI are
named Assisted Systems. Moreover, as in [21], we recognize that each assisted
system may present a distinct level in terms of capabilities related to AI/ML-
based decision-making. However, we adopt a different terminology to categorize
the systems, which is more appropriate to the MC services context, as
described in the following.

Figure 3: System Intelligence integration with the main Assisted Systems of
the Mission Critical scenarios.

We consider the MEC System as AI/ML unaware, which means that it has no AI/ML-
based decision-making capabilities. We categorize 5GC as AI/ML basic-aware,
since it has some AI/ML-based decision-making capabilities, mainly
characterized by the NWDAF component. Finally, we consider vRAN, as defined by
O-RAN, an AI/ML advanced-aware system, which means the existence of
sophisticated AI/ML-based decision-making capabilities, including an internal
AI in the control loop. MC services are provided by multiple AI/ML
applications, which can be either basic-aware or advanced-aware. From any
category, SI only needs a set of APIs that allows it to obtain data from the
assisted system (related to its state) and to provide commands or
recommendations for how to act, in general, to achieve a specific goal. It is
important to highlight that no change is necessary for any assisted system to
interact with SI appropriately. Actually, not even APIs need any adjustment.
As illustrated in Figure 3, a component named API Translator is responsible
for translating between APIs of SI and APIs of the Assisted Systems, if
necessary.

Figure 4: System Intelligence architecture based on ETSI ENI architecture.

The main elements of SI are presented in Figure 4. The Input Processor element
handles several types of data from different sources using connection points
between the API Translator and the external systems, e.g., 5GC, MEC, and vRAN.
These data are normalized and forwarded to the Semantic Bus, which connects
with the six internal SI elements [9]. These internal elements generate
results from processing and making decisions based on these standardized data.
The results can be new facts or new hypotheses, which can later be converted
into actions (by the Output Generator) to be applied to the systems. In the
following, we describe each of the internal SI elements and introduce their
deployment considering MC services and the infrastructure components,
especially UAVs.

### Knowledge Management

This component defines formalism for representing information and knowledge,
enabling the SI to analyze, apply, and validate decision-making processes.
Knowledge Management works with data and information, using a knowledge
representation that defines mechanisms for the set of entities’
characteristics and behavior. Moreover, this component enables SI to plan
actions and determine consequences by AI/ML and reasoning to direct action on
the set of entities. In this context, this component handles which context and
situation information is applied to the raw data, transforming it to
information and then knowledge.

Assuming a UAV-based MC use case, UAVs can provide essential data about the
context and situation. For example, object detection with AI/ML algorithms
applied to videos from searching and rescuing areas can help locating victims.
The AI/ML algorithms needed during the search stage may change from those
required for diagnostic and rescue. For instance, assume that Deep Neural
Networks are used for object detection [22], but the set of objects needs to
be detected change over time. Therefore, SI needs to have Knowledge Management
to fit different AI/ML algorithms loaded at UAVs for each context and
situation. Moreover, UAVs can be positioned for providing connectivity in
remote areas, being configured on-the-fly as repeaters, just amplifying the
signals, or as a full Base Station (BS) [23]. In this case, Knowledge
Management using context and situation information represented by estimated
interference signals and coverage areas, can assist in the decision-making
that regards choosing the better connectivity strategy for each MC service.

### Context Awareness

The Context Awareness component enables various data and information to be
easily correlated and integrated with the other SI components. In this case,
this component allows SI to provide customized services and resources
corresponding to that context. This component’s fundamental characteristic is
to enable SI to adapt its behavior according to changes in the context. For
example, the contextual history may be useful for driving policy decisions for
current and future interactions. Moreover, context knowledge offers a greater
level of reliability and usefulness over the whole systems.

SI must store data regarding the network, services, and users, such as the
number of users and the bandwidth requested along with an MC. For example, it
is possible to ensure that applications that benefit from low latency are
prioritized over others with less importance. The data considered by Context
Awareness is mandatory for instance, in SAR scenario that deals with the
transmission of Control Signal (CS) to inform UAVs about the flight plan for
an MC [24, 7] task, such as person identification or supply delivery.

### Cognition Management

This management allows SI to understand data and information input in the
system, i.e., defining how these were produced. In this case, the component
provides four functions: (i) perform inferences to generate new knowledge,
(ii) change existing knowledge, (iii) use raw data and historical data to
learn what is happening in a distinct context and situation, e.g., why the
data were generated, and which components could be affected, and (iv)
determine new actions to guarantee the goals of SI. Finally, the Cognition
Management component uses these four functions to validate and generate new
knowledge.

An MC service usually operates in dynamic scenarios regarding computation-
communication trade-offs between the UAVs and network infrastructure. In this
context, SI could provide Cognition Management to choose, change, and deploy
placement strategies for the computational vision and VNFs, evaluating their
effects on CPU and RAM usage, Moreover, all the changes over the configuration
of systems and flight plans must be handled through their history to generate
new knowledge.

### Situational Awareness

This component enables SI to understand what happened and how it influences
the SI goals. This component works to know how and why the current situation
presents such results. SI observes various situations evolving, examining them
for patterns within each condition and between different cases. This
observation process includes five actions: (i) collecting data, (ii)
understanding the significance of this data, (iii) determining what to perform
in response to a given event, (iv) making a decision, and (v) evaluating these
actions. In this way, it enables the application of context and policies to a
distinct situation using inference and historical data to learn what is
happening in one specific context, why, and what should be done in response to
it. It is essential to highlight the difference between Context Awareness and
Situational Awareness. The first one describes the state and environment in
which an entity exists. The second one incorporates contextual information and
other inputs to understand the meaning of data and behavior of the entire
assisted system and its operational environment.

One of UAV’s essential applications in MC service is to provide a quick
evaluation of the situation in SAR areas. UAVs promote versatility, fast
response time, and capacity to support several services. Therefore, all this
requires ability to learn about various evolving situations and examine
patterns within each condition among different MC services. In this case,
these services can allow Situational Awareness to keep predicting the
situation’s progression and how it affects the goals to achieve the MC task.

### Model-Driven Engineering

Model-Driven Engineering is an approach to software development where models
are used to understand, design, implement, deploy, operate, maintain, and
modify software systems. This component focuses on business logic, using an
abstract methodology. Therefore, this model supports three essential purposes:
(i) to ensure that several data models used in SI maintain a consistent
definition and understanding of concepts, (ii) to enable different policies at
various levels of abstraction to communicate with each other using a common
vocabulary and data dictionary, and (iii) to develop from a specification of
policy to its implementation.

The integration of several standards for supporting MC services, such as 3GPP
5GC, O-RAN, and ETSI MEC, demands software development models to provide
abstraction levels and a shared vocabulary between external systems and SI. It
is essential to define data models and APIs for supporting this integration
among the standards. Moreover, another crucial feature of model-driven is to
design different Access Point Name (APN) to direct users to various service
networks, for example, to guarantee the connectivity of UAVs, vRAN, MEC, 5GC,
and SI, to offer the better MC service.

### Policy Management

Policy Management provides uniform and intuitive mechanisms for providing
consistent recommendations and commands to ensure the scalable decisions
directing SI behavior. Three types of policies can be used in SI. The
Imperative Policy uses statements to change the state of a set of targeted
objects explicitly. Declarative Policy works based on ideas to describe what
needs to be done without defining how to execute this task. The Intent Policy
applies statements from a restricted natural language to express the policy’s
goals, but not how to accomplish these goals. SI can handle any combination of
these policies to define recommendations and commands to support and manage
the system.

An MC service is fundamental for controlling the network’s behavior, applying
security and control rules related to UAV’s session management, mainly for
functionalities associated with vRAN. This component should provide a mobility
policy to add the control of access restrictions to MC services in a given
area. Moreover, the policy should include the management of topics associated
with priority access to the channel of given UAV to others detriment.
Furthermore, this management should provide metrics related to QoS and
information regarding the data flow, which is obtained by regularly monitoring
events, for example, considering the transmission bit rate on the partitioning
strategy for the computational vision among UAVs, vRAN, MEC, and 5GC.

The next section presents experiments that aim at assessing specific aspects
of using SI in MC scenarios. The description of a SI system working in a
closed-loop, retrieving data, and imposing actions are out of this article’s
scope. However, the following section presents evidence using an actual
testbed on how MC services performance can be improved via SI.

##  4 Experiments with AI for UAV-Based SAR

This section describes SAR experiments as a use case to make more concrete the
discussion about the benefits of having SI in MC services. The goal of the
experiments is twofold: (i) provide concrete examples of how SI orchestrating
communications and AI/ML applications can positively impact MC services and
(ii) demonstrate how open-source software and low-cost off-the-shelf equipment
can be put together to assess essential issues related to AI/ML in 5G/B5G MC
scenarios.

The 5G/B5G network is where SI can orchestrate tasks such as the placement of
VNFs in different network elements. UAVs, for instance, can behave as UEs or
BSs, under the control of SI. Regarding AI/ML, UAVs fly around a disaster area
with equipment for performing computational vision (more specifically, object
detection via DNNs). SI has the flexibility of distributing AI/ML processing
among equipment in the cloud, edge, and UAVs themselves. This flexibility
allows SI to trade-off energy consumption and latency, for instance. Motivated
by a SAR situation in which the objects to be detected change along with the
mission, it is assumed that SI can partition (split) the layers of a DNN into
subsets and allocate distinct equipment to execute each subset of layers. For
instance, the first layers of a convolutional DNN, which detect simpler
features, can be kept fixed and executed by UAVs (acting as a UE). The last
layers, which are specialized to the objects of interest, can be implemented
by MEC (in vRAN or in the core) and continuously adapted according to the time
evolution of the SAR mission.

The communication network used in the experiments is implemented in a testbed
made available to promote reproducible
results.222https://github.com/lasseufpa/connected-ai-testbed This testbed is
based on the OpenAirInterface (OAI) software for vRAN deployment and the
free5GC software to implement a Non-Standalone 5GC. These two elements are
containerized with Docker, with automated deployment via Kubernetes. A summary
of the hardware used to execute the main software components is presented in
Table 2. The testbed employs Mininet to implement both the fronthaul and
backhaul topology. Mininet allows emulating switches and routers in the
transport network. In this case, the connection between vRAN and 5GC can have
different network topologies and behaviors, e.g., different link latencies,
bandwidths, and packet loss rates. Therefore, Mininet allows conveniently
emulating edge or cloud scenarios along with the experiments, reproducing
characteristics found in real scenarios. The automated Kubernetes cluster
orchestration complements this flexibility. Moreover, this automation makes it
easier to place 5GC and gNB VNFs (implemented as containers) according to the
mobile network scenario defined in each experiment.

We implemented object detection with OpenCV and Pytorch. The layers of the
adopted DNN were partitioned into two, and these subsets were executed by UAV
(the first subset of layers) and a cloud or edge equipment (the second subset
of layers). The DNN layers run by UAVs were processed by an NVIDIA Jetson
Nano, as indicated in Table 2. This board has a quad-core ARM<EMAIL_ADDRESS>GHz
CPU, with 4 GB 64-bit LPDDR4 memory and a 128-core NVIDIA Maxwell GPU.
Therefore, Jetson Nano represents a situation in which the UAV hardware may
not execute the full DNN in real-time or the last layers change along with the
mission.

Table 2: Testbed functionalities with associated hardware and software Function | CPU | RAM | Software
---|---|---|---
UPF, AMF, HSS, SMF, PCRF (5GC) | i5-7500 | 8 GB | Free5GC
RCC (vRAN) | i5-7500 | 8 GB | OpenAirInterface
RRH (vRAN) | i5-7500 | 8 GB | OpenAirInterface
Backhaul | i5-7500 | 8 GB | Mininet
UAV (Jetson Nano) | A57 | 4 GB | Pytorch, OpenCV

We devised two sets of experiments. The first concerns evaluating the impact
of having SI splitting the execution of a DNN between a UAV (as UE) and MEC
(in vRAN, i.e., in edge). The second set of experiments regards vRAN slicing
and VNF placement, which we assume to be also orchestrated by SI. In both
cases, SI is not fully implemented in a closed-loop. Instead of automatically
acting according to its inputs, some configurations regarding AI/ML and VNF
placement are manually defined and interpreted as SI actions. This
configuration simplifies the experiments and their description. A fully
working SI is out of this article’s scope and will be described in future
work. We describe the two sets of experiments and their results in the
following.

###  4.1 Distributed AI/ML

Factors such as computational resources, energy consumption, and latency must
be considered for real-time image understanding through AI/ML techniques when
UAVs capture visual information in MCs. These aspects should be addressed by
analyzing the computational cost usage through splitting the processing
between the UAV and other processing units. For this set of experiments, an
SSD-VGG16 DNN was trained to detect four classes: person, car, bicycle, and
motorcycle. The adopted dataset was UFPark, described by Nascimento et al.
[25], which consists of videos recorded in a campus parking lot. When datasets
with videos recorded from UAVs are available, it will be interesting to
compare the performance with models trained with videos obtained from fixed
cameras, as adopted in this work.

The original video resolution was subsampled to 300$\times$300 pixels with
three color channels, and the DNN input is a frame of this video. The trained
DNN had 40 layers and $24.15\times 10^{6}$ parameters (weights), each
represented with 32 bits. DNN was split into two subsets of layers according
to three configurations. In the first configuration, UAV executed from the
first to the third layer. Therefore, the quantized scores (activation values)
of this third layer were sent through the network, and a cloud or edge
equipment executed the remaining 37 layers. The other two configurations
adopted splits after the sixth and tenth layers, respectively. Figure 5
depicts the results for these three configurations concerning the average
number of Frames per Second (FPS) (bottom) and the latency (top), considering
the time to execute both subsets of DNN layers and the time to transmit the
data in the uplink from UAV to the edge or cloud equipment.

Figure 5: Performance of AI/ML application using different splits of a DNN
between UAV and a terrestrial equipment at edge or cloud.

The results in Figure 5 explore three different split configurations in both
edge and cloud scenarios, and identify that, in this case, it is beneficial to
have 10 DNN layers processed by UAV. In contrast, the remaining 30 layers are
processed at the edge or cloud. These scenarios need to assess QoS measures
and adapt the applications running through the transport network indicates
that SI can improve MC services by properly orchestrating communication
aspects and also the AI/ML applications.

###  4.2 RAN slicing and VNF placement

SAR and other MC scenarios require communication networks that maximize the
probability of successful mission completion. As indicated by the previous
experiments, orchestrating AI/ML applications can bring significant advantages
in realistic situations. Therefore, we now illustrate how SI can optimize the
network for improved robustness to the MC services. In this set of
experiments, the SI system is assumed to orchestrate VNFs and manage radio
resources to attend two distinct sets of requirements, imposed by applications
running on UAV (as UE) and three traditional UEs (e.g., smartphones),
respectively. These four devices are connected to a BS using Long Term
Evolution (LTE) with a bandwidth of 5 MHz for downlink, which corresponds to a
total rate of approximately 18 Mbps to be distributed among the connected
devices.

Figure 6: Throughput in RAN slicing experiment.

UAV executes an MC service for the SAR mission that requires a constant bit
rate of 13 Mbps. The other UEs are requesting 5 Mbps each, for applications
that are not critical. We contrast two situations: (a) SI imposes a strategy
based on vRAN slicing to protect the UAV communication versus (b) fair
scheduling without such vRAN slicing. Figure 6 shows these two situations via
the throughput values for UAV and the three traditional UEs’ accumulated
rates. Moreover, the UAV application does not reach the required 13 Mbps,
considering the scenario without vRAN slicing (circle markers). When
considering the SI-driven scenario using vRAN slicing (x markers), the slice
exclusively created for UAV can guarantee the 13 Mbps target. In this case,
the three traditional UEs only have access to the remaining radio resources,
which are equally distributed among them.

Figure 7: Latency when using RAN slicing and VNF placement.

In Figure 7, the curves contrast the results with and without slicing but
concerning latency between UAV and equipment at the edge or cloud that is
serving UAV data. This scenario considers that the same devices of the
previous vRAN slicing experiment are connected to the network and request the
same throughput. The application server and 5GC (with VNFs, such as UPF and
AMF) are either located at the edge or cloud. For emulating the transport
network in the cloud scenario, Mininet was used to impose a backhaul topology
with a total latency of 100 ms. Moreover, the curves in Figure 7 show the
effect of both actions: vRAN slicing and VNF placement. VNF allocations on
edge provide better latency values than allocating at the cloud. Furthermore,
vRAN slicing reduces the latency value experienced by UAV in both edge and
cloud scenarios. Overall, the results indicate the impact of SI actions in
guaranteeing SAR demands related to throughput and latency.

##  5 Related Work Regarding UAV-Based MC

Previously, mainly in Section 2, we reviewed existing academic articles and
standards to contextualize the proposed SI. In this section, we complement the
literature overview by focusing on a non-exhaustive list of recent manuscripts
that discuss integrating the main elements of AI/ML-based MC services,
eventually using UAVs. As discussed, MC applications (e.g., SAR) must be
supported by telecommunication infrastructures, considering many elements of
vRAN, edge, core, and even cloud [26]. In this context, one can observe the
great attention of academia and industry for the utilization of UAVs in SAR
[27]. The following paragraphs provide information on how these elements can
be put together and help understand the proposed SI’ relations with previous
works.

Besides the mentioned communication infrastructure elements, many MC
applications rely on computational vision [8]. The performance of MC
applications such as SAR can be largely improved by considering AI/ML, edge,
and multi-UAV technologies together [26]. Aligned with this perspective, two
recent surveys were published [28, 22]. Xu et al. [28] discussed concepts that
allow the distinction between Edge Intelligence and Intelligent Edge. For the
authors, Edge Intelligence focuses on intelligent applications in the edge
environment with edge computing assistance and protection of users’ privacy.
On the other hand, Intelligent Edge aims to solve edge computing problems
using AI solutions, e.g., resource allocation optimization. Moreover, Wang et
al. [22] emphasize that Edge Intelligence seeks to facilitate Deep Learning
(DL) services via edge computing. Furthermore, DL can be integrated into edge
computing frameworks to build an Intelligent Edge for dynamic, adaptive edge
maintenance and management. Finally, the authors discuss the challenges
regarding Edge intelligence and Intelligent Edge. The main requirement is to
design a complete system framework covering data acquisition, service
deployment, and the placement of AI/ML models considering processing and
network resources.

Pham et al. [7] introduced an article presenting the research on the
integration of MEC with 5G and beyond. The authors discuss MEC for UAV
communication and the integration between 5GC and vRAN. The authors explain
how UAVs can improve wireless communications, providing cost-effective, fast,
flexible, and efficient deployments. Moreover, UAVs can provide on-the-fly
communications and establish Line-of-Sight (LoS) communication links to users
in a complementary network for SAR emergencies and disaster reliefs. In this
context, Pham et al. [7] describe two typical scenarios integrating UAV and
MEC. An application considers UAVs operating as aerial users of the cellular-
connected network. In this case, the MEC server-based BS can provide seamless
and reliable wireless communications for UAVs to improve computation
performance. Another application refers to UAVs working as aerial BSs and
equipped with a MEC server. Therefore, MEC-enabled UAV servers give
opportunities for mobile users to offload heavy computation tasks. Finally,
the authors highlight several challenges integrating UAV with the MEC system,
such as mobility control and trajectory optimization, communication and
computation resource optimization, energy-aware resource allocation, and user
grouping and UAV association.

Specifically, about SAR as an MC task, Queralta et al. [2] show the research
efforts within the AutoSOS project. This project designs an autonomous multi-
robot SAR assistance platform using AI models for object detection. The
platform operates in the reconnaissance missions over the sea by executing
adaptive DL algorithms in UAVs and boats. Moreover, UAVs can autonomously
reconfigure their spatial arrangement to allow multi-hop communication, for
example, when a direct connection between a UAV transmitting information and
the vessel is unavailable. This reconfiguration uses algorithms for autonomous
decision-making at the edge devices, i.e., UAVs, considering independent task
migration and communication priority decisions within the multi-UAV system. In
this context, the authors discuss the need to integrate a single multi-agent
control loop using DL techniques for advanced vision, communication
constraints, spatial awareness, and computation distribution. Moreover,
Queralta et al. highlight that this integration cannot require high
computational resources since a UAV’s low energy consumption is needed to
increase its autonomy.

Table 3: Related work involving UAVs assisted by AI/ML and focused on MC services over 5G/B5G infrastructures | Multi |  |  |  |  |  |  | MC
---|---|---|---|---|---|---|---|---
Article | UAV | Edge | AI | 5GC | MEC | vRAN | Cloud | Services
[28] |  | ✓ | ✓ |  |  |  | ✓ |
[22] |  | ✓ | ✓ |  | ✓ |  | ✓ |
[7] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
[2] | ✓ | ✓ | ✓ |  |  |  |  | ✓
[29] | ✓ |  | ✓ |  | ✓ | ✓ |  |

Glimpsing a 6th Generation (6G) network, Zhang et al. [29] propose a UAV-to-
Everything (U2X) communication framework. The authors argue that UAVs are
unsuited for achieving a high data rate by directly connecting the terrestrial
cellular networks. Therefore, the authors apply three techniques for U2X
communications: cooperative sensing and transmission protocol, UAV trajectory
design, and radio resource management considering vRANs. Together, they
provide a feasible architecture for UAV sensing utilization in the 6G network.
Moreover, the authors discuss the open problems of U2X communications, such as
UAV Cooperation with U2X Communications to reduce the cost of power and
spectrum resources, as well as MEC with U2X Communications to minimize the
computation workload of the base station and to improve QoS.

We summarize this short sample of the state-of-the-art in Table 3, which
indicates the fundamental elements for supporting UAV-based critical missions
considered in each article, such as Multi-UAV, edge, AI, MEC, vRAN, cloud, and
MC services. According to our literature review, the manuscripts do not
discuss the integration of all elements that concern the proposed SI. However,
the literature clearly indicates the trend towards an intelligent global
system with a cognitive control loop using AI/ML techniques.

##  6 Conclusion and Future Work

Standardization and wide adoption of AI/ML are key strategies to reach scale,
reduce cost, and achieve MC services efficiency that heavily depends on
communication and computing systems. UAVs significantly contribute to
improving MC services but demand standards and efficient AI/ML for optimized
performance. In this article, we presented SI, an architecture for providing
full AI/ML capabilities for standardized systems supporting MC services. In
addition to describing the whole architecture and its interaction with the
assisted system, we also presented experiments that illustrate SI benefits.
The initial results show the challenges imposed in some search and rescue
scenarios while also offering the potential gains obtained with the
introduction of SI.

The described experiments are timely for researching 6G and future generation
computer systems because the current state-of-the-art in UAV-based has not yet
found the requirements for MC tasks, such as the timeliness of data
processing. The present research seeks solutions to how UAV can improve their
sensing range and how UAVs can become more intelligent through cooperation.
Aspects such as delays need to be taken into account, and simulators are
rather limited concerning mimicking all variability found in critical
missions.

While the experiments of this article exercised some of the essential
components of SI architecture, implementing this software that covers all its
components is still lacking. Therefore, as future work, we intend to develop
and publicly available a functional implementation of SI that minimally
illustrates all components. Given the size and complexity of this task, we
plan to perform it in phases. Each one is complemented by additional use cases
that illustrate the components involved and the benefits obtained.
Additionally, we are interested in investigating how to evolve the AI/ML
awareness level of assisted systems. For example, the necessary changes to
turn MEC at least AI/ML basic-aware and turn 5G core AI/ML advanced-aware. In
this context, the concept of ML pipeline introduced by the specification ITU-T
Y.3172 seems a promising approach for adding awareness to assisted systems.

## Acknowledgment

This article was conducted with partial financial support from the National
Council for Scientific and Technological Development (CNPq) under grant number
130555/2019-3.

5G

    5th Generation
6G

    6th Generation
ENI

    Experiential Networked Intelligence
AI/ML

    Artificial Intelligence/Machine Learning
MC

    Mission Critical
B5G

    Beyond 5G
AIMET

    AI Model Efficiency Toolkit
C2

    Command and Control
RAN

    Radio Access Network
non-RT

    non-Real-Time
near-RT

    near-Real-Time
vRAN

    virtual RAN
DRL

    Deep Reinforcement Learning
SI

    System Intelligence
ML

    Machine Learning
PNI-NPN

    Public Network Integrated NPN
SNPN

    Standalone NPN
PLMN

    Public Land Mobile Network
AN

    Access Network
CN

    Core Network
NWDAF

    Network Data Analytics Function
SON

    Self-Organizing Network
NS

    Network Slicing
NPN

    Non-Public Network
NRF

    Network Repository Function
B5G

    Beyond 5G
Naf

    Network application function
AS

    Assisted System
UPF

    User Plane Function
3GPP

    3rd Generation Partnership Project
5GC

    5G Core
ETSI

    European Telecommunications Standards Institute
gNB

    Next generation NodeB
SBA

    Service-Based Architecture
NG-RAN

    Next-Generation Radio Access Network
SBI

    Service-Based Interface
DNN

    Deep Neural Network
DL

    Deep Learning
FPS

    Frames per Second
UAV

    Unmanned Aerial Vehicle
EI

    Edge Intelligence
eMBB

    enhanced Mobile Broadband
URLL

    ultra-Reliable Low Latency
SAR

    Search and Rescue
SDAR

    Search, Diagnostic, and Rescue
BS

    Base Station
AI

    Artificial Intelligence
MEC

    Multi-access Edge Computing
MIMO

    Multiple Input Multiple Output
NF

    Network Function
NFV

    Network Functions Virtualization
SDN

    Software-Defined Networking
SDR

    Software-Defined Radio
VNF

    Virtual Network Function
USRP

    Universal Software Radio Peripherals
VM

    Virtual Machine
SRS

    Software Radio Systems
UE

    User Equipment
BBU

    Base Band Unit
RRH

    Remote Radio Head
QoS

    Quality of Service
VNF

    Virtualized Network Functions
RIC

    RAN Intelligent Controller
C-RAN

    Cloud Radio Access Network
O-RAN

    Open Radio Access Network
O-RU

    O-RAN Radio Unit
CU

    Central Unit
O-DU

    O-RAN Distributed Unit
LTE

    Long Term Evolution
EPC

    Evolved Packet Core
gNB

    5G base-station
MME

    Mobility Management Entity
S-/P-GW

    Serving-/Packet Data Network-Gateway
HSS

    Home Subscriber Server
FFT

    Fast Fourier Transform
MAC

    Media Access Control
API

    Application Programming Interface
IoT

    Internet of Things
TDD

    Time Division Duplex
OAI

    OpenAirInterface
OS

    Operating System
PHY

    Physical Layer
CR

    Cognitive Radio
IAB

    Integrated Access Backhaul
CAPEX

    CAPital EXpenditures
OPEX

    OPerational EXpenditures
ITU-T

    International Telecommunication Union
APN

    Access Point Name
CS

    Control Signal
LoS

    Line-of-Sight
U2X

    UAV-to-Everything

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  *[SAR]: Search and Rescue
  *[UAVs]: Unmanned Aerial Vehicle
  *[MC]: Mission Critical
  *[AI/ML]: Artificial Intelligence/Machine Learning
  *[5G]: 5th Generation
  *[B5G]: Beyond 5G
  *[UAV]: Unmanned Aerial Vehicle
  *[3GPP]: 3rd Generation Partnership Project
  *[UE]: User Equipment
  *[5GC]: 5G Core
  *[RAN]: Radio Access Network
  *[MEC]: Multi-access Edge Computing
  *[SI]: System Intelligence
  *[ETSI]: European Telecommunications Standards Institute
  *[ENI]: Experiential Networked Intelligence
  *[ITU-T]: International Telecommunication Union
  *[O-RAN]: Open Radio Access Network
  *[AI]: Artificial Intelligence
  *[ML]: Machine Learning
  *[SBA]: Service-Based Architecture
  *[NG-RAN]: Next-Generation Radio Access Network
  *[gNB]: 5G base-station
  *[O-RU]: O-RAN Radio Unit
  *[O-DU]: O-RAN Distributed Unit
  *[CU]: Central Unit
  *[VNF]: Virtualized Network Functions
  *[vRAN]: virtual RAN
  *[VNFs]: Virtualized Network Functions
  *[SBI]: Service-Based Interface
  *[Naf]: Network application function
  *[NWDAF]: Network Data Analytics Function
  *[RIC]: RAN Intelligent Controller
  *[non-RT]: non-Real-Time
  *[near-RT]: near-Real-Time
  *[QoS]: Quality of Service
  *[AS]: Assisted System
  *[API]: Application Programming Interface
  *[gNBs]: 5G base-station
  *[APIs]: Application Programming Interface
  *[BS]: Base Station
  *[CS]: Control Signal
  *[APN]: Access Point Name
  *[UEs]: User Equipment
  *[BSs]: Base Station
  *[DNNs]: Deep Neural Network
  *[DNN]: Deep Neural Network
  *[OAI]: OpenAirInterface
  *[FPS]: Frames per Second
  *[LTE]: Long Term Evolution
  *[DL]: Deep Learning
  *[LoS]: Line-of-Sight
  *[6G]: 6th Generation
  *[U2X]: UAV-to-Everything