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Sarkar, N.I.; Gul, S. AI-Based Unmanned Aerial Vehicles Networks. Encyclopedia. Available online: https://encyclopedia.pub/entry/45219 (accessed on 27 July 2024).
Sarkar NI, Gul S. AI-Based Unmanned Aerial Vehicles Networks. Encyclopedia. Available at: https://encyclopedia.pub/entry/45219. Accessed July 27, 2024.
Sarkar, Nurul I., Sonia Gul. "AI-Based Unmanned Aerial Vehicles Networks" Encyclopedia, https://encyclopedia.pub/entry/45219 (accessed July 27, 2024).
Sarkar, N.I., & Gul, S. (2023, June 06). AI-Based Unmanned Aerial Vehicles Networks. In Encyclopedia. https://encyclopedia.pub/entry/45219
Sarkar, Nurul I. and Sonia Gul. "AI-Based Unmanned Aerial Vehicles Networks." Encyclopedia. Web. 06 June, 2023.
AI-Based Unmanned Aerial Vehicles Networks
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This entry is adapted from the peer-reviewed paper 10.3390/drones7050322

To enhance the overall performance of the unmanned aerial vehicles (UAVs) networks and to address some specific problems, new features in the network are being designed as autonomous features. This approach not only provides optimum solutions for the targeted problems but also supports the dynamic properties of a UAV network. 

artificial intelligence autonomous UAV AI-based UAV networks

1. Resource Management and Network Planning

Optimal management and planning of network resources are very crucial for the success of any network. This is particularly true when it comes to networks where human intervention is minimal. For unmanned aerial vehicles (UAVs), this feature is being focused on by many researchers, and various challenges are being identified and addressed. Table 1 provides a summary of related surveys on UAV network resource management and planning.
Table 1. Summary of related survey: UAV network resource management and planning.
Scope Autonomous Features Computational Intelligence Channel Modeling Interference Management Security and Safety Reference
Cooperative path planning, collisions Path planning √ Non-convex modeling - - [1]
Cooperative path planning, resource allocation Path planning √ - - - [2]
Cooperative UAVs, trajectory conflicts Conflict detection and resolution √ - - - [3]
Channel modeling   √ Shadowing channel √ - [4]
Interference management Path planning √ Rician distribution √ - [5]
Interference-aware path planning Path planning √ Free-space path loss with 6 GHz √ - [5]
Interference management Spatial configuration √ Customized √ - [6]
Interference management Transmission power, trajectory planning √ Large-scale path loss √ - [7]
Interference management Transmission power √ Free-space path loss √ - [8]
Resource management Energy consumption, transmission power √ Various √ - [9]
Collision free navigation Trajectory planning √ - - - [10]
Risk-aware path planning Path planning √ - - - [11]
Mobility challenges   - - - √ [12]
Resource management User association √ Free-space path loss √ - [13]
Delay-aware throughput maximization Trajectory planning √ Free-space path loss - - [14]
UAV placement Energy efficiency and optimization √ Path loss outdoor/indoor penetration - - [15]
Collision free navigation Trajectory planning √ - √ - [16]
Swarm-based UAV Path planning √ - - - [17]
Physical layer security security and cooperation √ Free-space path loss √ √ [18]
Secure UAV communication Cooperative scheduling √ Free-space path loss - √ [19]
Physical layer security Cooperative trajectory and optimization √ Free-space path loss √ √ [20]
Physical layer security Cooperative resource allocation √ Free-space path loss - √ [21]
Quality of Experience (QoE) Cooperative resource allocation √ LOS and Non-LOS - - [22]
Secure UAV communication UAV defense √ - - √ [23]
Software-defined radio (SRD) Localization of unwelcomed UAVs √ - √ √ [24]
In [9], the autonomous distribution of resource management for UAV networks is being discussed. The authors have used the concept of game theory and highlighted various game models that may be used for optimal resource management among UAVs. In specific, five-game theory models were discussed, including coalition, potential, graphical, mean-field, and Stackelberg. Each model is explained based on its goals, design of utility function, and strategies, which implies their application areas.
Reference [10] focused on real-time planning for UAV’s path under dynamic conditions. A discrete algorithm, along with a probabilistic graph, is being used to achieve a path with no collisions. Another similar study to explore path planning for UAVs is conducted in [11]. In this study, the information from both static and dynamic paths is utilized to provide a path. A step-by-step adaptive path planning technique is proposed to achieve optimal results. Another interesting idea is that UAVs are considered additional users in the 5G cellular networks [12]. The study highlights various challenges that are encountered by service providers to facilitate new users.

2. Multiple Access and Routing Protocols

Multiple access and routing protocols are another challenging domain for UAV networks. As UAVs are supposed to be co-located with other networks, the natural choice for multiple access for UAVs was initially space division multiple access techniques [25][26][27]. However, as time passes, many researchers have started exploring other techniques that prove to be more efficient in the case of UAV networks. Most of these include orthogonal, non-orthogonal, or rate-splitting techniques. Table 2 provides a summary of related surveys on UAV multiple access and routing protocols.
In orthogonal multiple access techniques, the interference is reduced by ensuring that the simultaneous communications are orthogonal to one another. Multiple research studies [13][28] have been conducted to achieve this goal. Some propose to use time division multiple access (TDMA) and frequency division multiple access (FDMA) in their system models. Another group of researchers focused on using code division multiple access (CDMA) [29][30][31]. On the other hand, orthogonal frequency division multiple access (OFDMA) is explored by some researchers [14][32][33], while some other research studies are conducted exploring space division multiple access (SDMA) [34][35][36][37][38][39].
To overcome the above-stated limitation of orthogonal multiple access techniques, much work is being performed using non-orthogonal techniques, mainly using joint optimization of the base stations (BSs), including power [40][41][42], trajectory [43][44][45], and placement [46][47]. Although non-orthogonal multiple access techniques can perform better by covering many users with non-orthogonal yet correlated channels, however, there is a case when its performance can degrade exponentially, i.e., when the number of antennas is greater than the number of users scheduled. To avoid such circumstances, another effective technique is explored.
Rate-splitting multiple access techniques for UAVs seem to overcome the problems of both orthogonal and non-orthogonal multiple access.

3. Power Control and Energy Efficiency

For drones and unmanned aerial vehicles (UAVs), power control and energy efficiency are other very critical realms. As these network nodes are not on the ground, an uninterrupted supply of power and efficient utilization of energy become very crucial. Many research studies have addressed this area and proposed several solutions, some targeting multiple power sources, including battery [48][49], hydrogen fuel [50][51], solar [52][53], and hybrid [54][55], that can be utilized by the UAVs. While others focus on how efficiently energy can be consumed. Energy efficiency affects all the operations of a UAV. Many studies have been conducted to explore UAV placement for optimal energy efficiency [15][56].
Another aspect explored in this domain is path planning. Various approaches are being used to explore this area, including sample-based [16][57], space search [17], and biological searching techniques [58][59][60].
Table 2. Summary of related survey: UAV multiple access and routing protocols.
Scope Autonomous Features Computational Intelligence Channel Modeling Interference Management Security and Safety Reference
Channel access Cyclic multiple channel access √ Free-space path loss with LOS - - [28]
MAC protocol Energy consumption, Packet-error-rate (PER) √ Free-space path loss with LOS - - [29][30]
Performance evaluation of MAC PER √ Rician fading - - [31]
Trajectory optimization Trajectory planning √ Free-space path loss with LOS, correlated Rician fading, Rayleigh fading, Rician K-factor √ - [34]
mm wave UAV cellular network Beam forming √ Quasi-static, Rayleigh fading √ - [35]
Channel access with time-modulated array (TDM) Beam forming, performace √ Free-space path loss with LOS √ - [37]
Trajectory optimization Trajectory planning, power control √ Additive white Gaussian noise (AWGN) √ - [41]
MAC protocol Power optimization √ Rician fading √ - [42]
MAC protocol Throughput optimization √ Free-space path loss - - [43]
MAC protocol Power optimization √ Free-space path loss with LOS √ - [44]
MAC protocol Trajectory planning, resource management √ LOS and Non-LOS (NLOS) √ - [45]
MAC protocol Power optimization √ LOS, NLOS √ - [46]
MAC protocol Power and placement optimization √ Additive white Gaussian noise (AWGN) √ - [47]
Rate-splitting Beam forming √ Additive white Gaussian noise (AWGN) √ - [61]
Rate-splitting Spectral efficiency √ Additive white Gaussian noise (AWGN) √ - [62]

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