THEME ARTICLE: SOCIETY 5.0: HUMAN CENTRIC,
DECENTRALIZED AND HYPERAUTOMATED
5G/SDR-Assisted Cognitive Communication
in UAV Swarms: Architecture and
Applications
Muhammad Zeeshan , Walton Institute of Information and Communication Systems Science, Waterford
Institute of Technology, X91 P20H, Waterford, Ireland
Muhammad Umar Farooq
44000, Pakistan
Adnan Akhunzada
and Kashif Shahzad, National University of Sciences and Technology, Islamabad,
, University Malaysia Sabah, Sabah, 88400, Malaysia
Unmanned aerial vehicles (UAVs) are in general faster to deploy and easier to
operate due to many off-the-shelf guidance, navigation, and control solutions. Such
solutions usually support short-range line-of-sight operations. However, operating a
swarm of UAVs for surveillance purposes in diverse terrains requires cost-effective
wireless connectivity, infrastructure-less operation, adaptive mobility models,
multiclass routing solutions, and a tailored ground control station . In this article,
we address the challenge of UAV swarm communications by presenting a
framework that offers an open-interface communication and networking solution
for surveillance operations in urban/outreach areas. It is based on a hybrid
connectivity module that can enable the coexistence of 5G infrastructures,
adaptive multiband SDR waveforms empowered with cooperative communication
capacities, and satellite communications for continuous swarm operation in any
demographic area. In addition, we also discuss some of the current and futuristic
applications and scenarios that can benefit from the provided solution.
E
lucidating the requirements of modern era,
where tasks are carried out by drones, aka
UAVs is not a far-fetched concept anymore.
Smart delivery systems for domestic applications are
an inevitable transition of UAV applications from commercial to domestic usage, pretty much announcing
the age where there are “drones for everyone.” From leisure pursuits to disaster management, from environmental mapping to search and rescue operations, from
supervisory tasks to delivering mail, UAVs are the
answer.1 Like humans, the coordinated effort brings
both efficiency and reliability via diversity and redundancy. A number of drones attempting a task in a coordinated manner gives rise to what we call a swarm. With
anticovid and vaccination protests every other day, living
1520-9202 ß 2022 IEEE
Digital Object Identifier 10.1109/MITP.2022.3161318
Date of current version 30 June 2022.
28
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life by the rule of law is not a walk in a park anymore. Mob
management2 and patrolling seems to be the active area
of unwanted learning for law enforcement agencies.3
Swarms can be operated in either leader or leaderless formation.4 In both cases, drones not only have to communicate with the GCS but also have diverse data sharing
needs. The requirements of high processing for
machine learning, if required, have the data acquisition and processing needs of their own.5
Recently, several communication technologies and
protocols are being explored to provide communication
and networking support in drone swarms. In Khan et al.’s
work,6 the IEEE 802.11ah standard with link adaptation
support through multicode CDMA and multicarrier
CDMA is proposed to meet the diverse range and service
quality requirements in a drone swarm network. A
scheme to enhance the communication support while
simultaneously providing secure links in drone swarm
network is proposed in Raja et al.’s work.7 By taking
the locations of all the drones, a wireless mesh network
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SOCIETY 5.0: HUMAN CENTRIC, DECENTRALIZED AND HYPERAUTOMATED
is formulated by using a decentralized controller.
An intelligent algorithm is presented that finds the
shortest communication path among the drones. In an
infrastructure-based drone swarm network, GCS is
responsible for the exchange of information among all
the drones, whereas in FANET, all the drones communicate with each other through mutual relay.8 Another
challenge in the drone swarm patrolling operation is
the multiple drone coordination and path planning. A
dynamic multitarget tracking and sensing framework is
proposed in Patrizi et al.’s work9 for UAV-assisted surveillance. The scheme performs intelligent pairing of
UAVs with targets by defining the target’s reputation. A
mission-based drone swarm coordination protocol is
presented in Fabra et al.’s work10 that allows multiple
drones to perfectly coordinate their flight when performing planned missions.
Based on the current state-of-the-art, a number of
research gaps have been identified. This includes the
following:
1) lack of a complete solution for robust communication empowered by suitable networking protocols, along with effective formation and control;
2) an adaptive communication algorithm capable
of achieving diverse range, throughput and service requirements under varying channel conditions is not available;
3) limited work on the practical integration of
4G/5G with SDR waveforms for drone swarm
networks; and
4) lack of mobility models for a diverse range of
patrolling/surveillance operations.
The aim of this article is to provide a 5G/SDRassisted cognitive communication solution for drone
swarm surveillance in urban/outreach areas using a
hybrid connectivity module (HCM) that can enable the
coexistence of 5G infrastructures and adaptive multiband SDR waveforms. It would empower the cooperative
communication capacities and abridge these communication technologies to achieve reliable connectivity
among multiple drones and GCS in almost all geographical areas. The seamless connectivity via efficient next
generation SDR waveforms and suitable algorithms can
ensure an efficient implementation of versatile UAV
swarm operations.11 Such strong technology enables a
group of UAVs to operate in a swarm, forming an air IoT.
We also discuss intelligent mobility models to facilitate
drone swarm operations for security/surveillance purposes. The pictorial representation of some possible
UAV swarm operations is illustrated in Figure 1.
May/June 2022
5G/SDR-ASSISTED COGNITIVE
COMMUNICATION ARCHITECTURE
The complete architecture of the proposed framework
is divided into several modules presented in the
following.
Development of Reliable
Communication Solution
The most crucial part is the development of a communication solution for drone swarm network. The unavailability of 5G infrastructure in some remote/outreach areas
limit the reliance of UAV swarm network on standard 5G
backbone. A key approach is to develop a HCM. The aim
is to combine standard 5G infrastructure12 and satellite
communication (SatComm) with adaptive multiband
multimode SDR waveforms with cooperative communication support. HCM provides a bridge between these
communication protocols, resulting in a reliable communication link among multiple drones and GCS in almost
every geographical area. As indicated in Figure 2, the first
stage of the HCM is the selection of an appropriate
medium depending upon the desired quality-of-service
(QoS) and throughput. If 5G communication is not available or SDR medium is capable of fulfilling the required
service demand, the SDR medium is selected. In the next
phase, the proposed HCM selects one of the wideband
or narrowband mode based on the throughput and range
requirement. The wideband mode is selected if the operating range is small and/or channel condition is better.
On the other hand, the narrowband mode is selected if
the operating range is large and/or channel condition is
poor, keeping the UAVs connected to each other. Once
the wideband or narrowband mode is selected, the HCM
selects appropriate parameters or mode of operation
based on the QoS, throughput requirement and the channel state.
Link adaptation at the physical layer of both the
narrowband and wideband schemes involves the
development of multiple modes of operation, offering
various operating ranges and throughput with specific
bit error rate (BER) performance. To achieve this end,
parameter adaptation in both the narrowband and
wideband schemes is introduced at the physical layer
to define multiple modes of operation.
Multihop Flying Ad Hoc Network
(FANET) Operation
The multihop 3-D flying ad hoc network (3D-FANET)
operation enables the swarm to collaboratively operate
over any terrain, have extended coverage area, and
achieve connectivity to the GCS. This enables the complete swarm to operate on SDR waveform, and benefit
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SOCIETY 5.0: HUMAN CENTRIC, DECENTRALIZED AND HYPERAUTOMATED
FIGURE 1. Overview of the UAV swarm based in-air-IoT.
from 5G/Satcomm according to operational requirements. The 3D-FANET operation best benefits from a
cross-layer MAC/routing protocol design. The MAC design
usually employs a hybrid of TDM/FDM mechanism, which
is capable of adapting to the network size without any central infrastructure. Use of hybrid TDM/FDM mechanism is
preferred over carrier sensing/random access protocols
because of their capability to support QoS, and the relatively poor performance of carrier sensing protocols over
long operational ranges. Moreover, the link level parameters, such as connectivity status and mapping of neighbors to supported service classes, are made available to
the routing protocol. Based on this information, the
routing algorithm makes QoS-aware routing decisions
by proactively or reactively generating network-wide
topology information broadcasts. The impact of topology information broadcasts is minimized by small relay
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set selection. For instance, visualizing the swarm as a
unit-disk graph helps selecting a small connected dominating set (CDS) to relay the broadcast messages.13 This
curtails any additional overhead to the radio network
due to the routing algorithm.
Heterogeneous Drone Swarm Support
The solution eliminates the need for any platform-specific UAVs. Coexistence of multiple platforms enhances system capabilities, allows expansions with the
improvements in drone technology, increases suitability of the drones for a particular swarm operation, and
also reduces the financial overlay. Drones/UAVs vary in
terms of size, flying ranges, cost, battery capacity, carried equipment weight, and communication requirements. From the implementation perspective, a key
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SOCIETY 5.0: HUMAN CENTRIC, DECENTRALIZED AND HYPERAUTOMATED
entire swarm operation. Therefore, a suite of path planning algorithms is required for a successful swarm operation.14 For instance, Manhattan grid models can be used
for swarm operations in urban highways; whereas, selfdeployable point convergence model can be used in rescue operations. The selection of an appropriate mobility
model is based on the UAV swarm application, as shown
in Figure 2.
Drone Charging for Uninterrupted
Operation
In order to provide uninterrupted swarm operation for
long-duration continuous surveillance and monitoring,
a simple and efficient drone charging mechanism is
adopted. Patrolling posts, and other charging facilities
may act as charging stations for drones. All the drones
in a swarm are charged on rolling basis by utilizing
reserve drones. For each group of drone swarm, there
is always an additional reserved drone to replace
the drone gateway to avoid communication collapse.
This mechanism, along with the platform-independent
drone swarm support, ensures a continuous surveillance operation with minimal changes in the network.
COMPUTATIONAL COMPLEXITY
FIGURE
2.
5G/SDR-assisted
cognitive
communication
architecture.
approach is to use multisize SDRs for various drones
and ground stations, depending upon their role in the
network, to make the network more energy-efficient.
The miniature UAV class, having 2–25 kg weight, is recommended for flying drones along with requisite
resources. The heterogeneous swarm operation is
monitored from command and control center with minimal human intervention in terms of routing protocols,
waveform selection, mobility management etc.
Mobility Model Mapping to the
Operational Needs
Usually, several drones participate in a swarm to collaboratively complete operations, targeted for different dissimilar terrains. Conventional path planning methods for
ad hoc networks are usually not best suited for swarm
operations, due to their 3-D setup, speed, communication ranges, and operational diversity. Moreover, such
algorithms can result in UAV collisions and communication gray zones, resulting in UAV damage and risking the
May/June 2022
The computational complexity of the proposed framework depends on multiple factors. We propose filterbank multicarrier and continuous phase modulation
(CPM) for wideband and narrowband waveforms,
respectively. It will result in a complexity in the order
of Mlog2 ðMÞ and K 2 L, where M, K, and L are the
number of subcarriers, states, and CPM sequence
length, respectively. The HCM operation is implemented using fuzzy inference in the form of conditional statements. The complexity associated with
MAC and routing protocol designs depends upon the
protocol class. We propose hybrid TDM/FDM MAC
designs to guarantee latency and data rates. Modern
TDM/FDM MAC protocols offer control phase time
pffiffiffiffiffi
latency of N , and data phase time latency in the
order of dNc , where d, N, and Nc are the number of
data slots, nodes, and band classes, respectively.11
Topology information exchanged during control phase
can also be fed to the routing engine to form proactive
routing zones, minimizing the need for multihop route
searches. Moreover, modern reactive routing protocols can use optimal folding approaches like CDS to
curtail the number of route search messages. Modern
protocols can construct CDS in N þ 3P messages,13
where P is the maximal independent set size.
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SOCIETY 5.0: HUMAN CENTRIC, DECENTRALIZED AND HYPERAUTOMATED
FIGURE 3. Working and evaluation of the HCM for UAV
swarm communication.
PERFORMANCE EVALUATION
We evaluated the performance of the proposed framework via extensive MATLAB simulations as proof of concept. To achieve this end, we consider locust monitoring
scenario in an outreach desert in the absence of 5G
infrastructure. Specifically, the working of HCM for UAV
swarm communication based on underlying channel
conditions and required throughput is evaluated. This is
shown in Figure 3 with the help of SNR and throughput
analysis of three types of band classes. For two narrowband waveforms (having bandwidth of 100 and 200 kHz)
and one wideband waveform (having bandwidth of
1 MHz), we show the theoretical throughput of all the
possible modes within a band class versus Eb =N0 for
which BER approaches to zero for each mode. Referring
to the horizontal dotted line shown in Figure 3, if the
throughput requirement of a particular UAV is 1000
kbps and Eb =N0 ¼ 10 dB, HCM will select best possible
mode from 200-kHz narrowband waveform. On the
other hand, if Eb =N0 ¼ 20 dB for the same throughput
requirement, the HCM successfully finds suitable mode
of the wideband waveform as indicated in the figure,
which fulfills the desired throughput requirement.
CURRENT AND FUTURE
APPLICATIONS
The developed heterogeneous communication solution
for UAV swarms finds many applications ranging from
surveillance and security to agriculture monitoring. An
FIGURE 4. Current and futuristic applications of UAV swarms.
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SOCIETY 5.0: HUMAN CENTRIC, DECENTRALIZED AND HYPERAUTOMATED
overview of the possible use cases/applications is shown
in Figure 4. For instance, monitoring ecological and environmental conditions15 favorable to the survival and
breeding of desert locust require multiple UAV swarms
for distributed ground monitoring and decisions-making
regarding control interventions against the initial locust
congregations. Such swarm operations can benefit from
SatComm for GCS connection, and hybrid SDR waveforms can be used for interswarm and intraswarm communications. On the other hand, patrolling and mobsurveillance in urban areas require collaborative communication among UAVs to form multiangular video feed
across different locations of interest,16 enabling rapid
data collection with high quality, range, and resolution.
In such operations, 5G communications can be used for
GCS connection, hybrid SDR waveforms for intraswarm
communications, and cooperative communication for
multihop operation. Similarly, rail-track monitoring may
involve 5G/SDR narrowband communication for extended range coverage.
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connected swarm of multiple UAVs,” IEEE Access,
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4. X. Fu et al., “A formation maintenance and
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communication,” Phys. Commun., vol. 42, 2020,
CONCLUSION
The seamless communication requirement of UAV
swarms to operate from peacetime to hostile scenarios while addressing diverse service requirements is
the need of time. In this article, we provide a solution
to this challenge by presenting a 5G/SDR-enabled
communication architecture for UAV swarms. This
HCM enables the coexistence of 5G infrastructures,
adaptive multiband SDR waveforms empowered with
cooperative communication capacities, and SatComm
for continuous swarm operation in any geographical
terrain. We also present some of the current and futuristic applications, and use cases that are the sheer
beneficiaries of the proposed solution.
Art. no. 101159.
7. G. Raja, S. Anbalagan, A. Ganapathisubramaniyan,
M. S. Selvakumar, A. K. Bashir, and S. Mumtaz,
“Efficient and secured swarm pattern multi-UAV
communication,” IEEE Trans. Veh. Technol., vol. 70,
no. 7, pp. 7050–7058, Jul. 2021.
8. W. Chen, J. Liu, H. Guo, and N. Kato, “Toward robust
and intelligent drone swarm: Challenges and future
directions,” IEEE Netw., vol. 34, no. 4, pp. 278–283,
Jul./Aug. 2020.
9. N. Patrizi, G. Fragkos, K. Ortiz, M. Oishi, and E. E.
Tsiropoulou, “A UAV-enabled dynamic multi-target
tracking and sensing framework,” in Proc. IEEE Glob.
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10. F. Fabra et al., “MUSCOP: Mission-based UAV
swarm coordination protocol,” IEEE Access, vol. 8,
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ACKNOWLEDGMENTS
This work was supported by the Science Foundation
Ireland and the Department of Agriculture, Food, and
Marine on behalf of the Government of Ireland VistaMilk research centre under Grant 16/RC/3835.
11. K. Shahzad, M. U. Farooq, M. Zeeshan, and S. A. Khan,
“Adaptive multi-input medium access control (AMIMAC) design using physical layer cognition for tactical
SDR networks,” IEEE Access, vol. 9, pp. 58364–58377,
2021.
12. Y. Huang, Q. Wu, R. Lu, X. Peng, and R. Zhang, “Massive
MIMO for cellular-connected UAV: Challenges and
promising solutions,” IEEE Commun. Mag., vol. 59,
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MUHAMMAD UMAR FAROOQ is an assistant professor at the
College of Electrical and Mechanical Engineering, National
University of Sciences and Technology, Islamabad, 44000,
Pakistan. Contact him at ufarooq@ceme.nust.edu.pk.
“Prospects of distributed wireless sensor networks for
urban environmental monitoring,” IEEE Aerosp.
Electron. Syst. Mag., vol. 34, no. 6, pp. 44–52, Jun. 2019.
KASHIF SHAHZAD is currently working toward the Ph.D.
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degree in electrical engineering with the College of Elec-
and resilient swarms: Autonomous decentralized
lightweight UAVs to the rescue,” IEEE Consum.
trical and Mechanical Engineering, National University of
Electron. Mag., vol. 9, no. 4, pp. 34–40, Jul. 2020.
Contact him at kashif.shahzad@ceme.nust.edu.pk.
Sciences and Technology, Islamabad, 44000, Pakistan.
MUHAMMAD ZEESHAN is a postdoctoral researcher at the
34
Waterford Institute of Technology, X91 P20H, Waterford,
ADNAN AKHUNZADA is an associate professor at the Fac-
Ireland, and an associate professor at the National University of
ulty of Computing and Informatics, University Malaysia
Sciences and Technology, Islamabad, 44000, Pakistan. He is a
Sabah, Sabah, 88400, Malaysia. He is a senior member of the
member of the IEEE. Contact him at mzeeshan@ieee.org.
IEEE. Contact him at adnak@dtu.dk.
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