UAV flight coordination for communication networks: genetic algorithms versus game theory

Abstract

The autonomous coordinated flying for groups of unmanned aerial vehicles that maximise network coverage to mobile ground-based units by efficiently utilising the available on-board power is a complex problem. Their coordination involves the fulfilment of multiple objectives that are directly dependent on dynamic, unpredictable and uncontrollable phenomena. In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology. Genetic algorithms and non-cooperative games are employed for the generation of optimal flying solutions. The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts. Coupled with a communication model that addresses environmental conditions, they optimise flying to maximising the number of supported ground-based units. Results of large-scale scenarios highlight the ability of genetic algorithms to evolve flexible sets of manoeuvres that keep the flying vehicles separated and provide optimal solutions over shorter settling times. In comparison, game theory is found to identify strategies of predefined manoeuvres that maximise coverage but require more time to converge.

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

“…For describing the air-to-ground channels in a 3D plane with less environmental factors, these methods are preferred [372] [373]. The air-to-air propagation channel, unlike the air-to-ground channel, is primarily used in multi-hop UAV networks for the purpose of autonomous coordinating and managing between UAVs, as well as supporting back-haul radio connectivity to complement current communication systems [138] [374]. Furthermore, the propagation properties of air-to-air channels are comparable to propagation characteristics in free space and are heavily reliant on line-of-sight propagation and ground reflection effects [138].…”

“…Unfortunately, current spectrum allocation techniques cannot be used for UAV networks. This is because UAV communication networks are dynamic in nature [374] and used frequency spectrum during the flight may need to be changed continuously to be able to provide reliable services to the end users [382]. Prior to the advent of drone networks, the use of radio frequency spectrum was reserved primarily for terrestrial networks (e.g., personal, indoor, cellular, etc.)…”

A constrained optimization model for the provision of services in a 5G network with multi-level cybersecurity investments

“…Authors in Giagkos et al. ( 2021 ) analyse the coordination of network-enabled UAVs that provide communication coverage to multiple mobile users on the ground (with the object of maximizing the set of mobiles covered by UAVs by balancing the power consumption); they propose also a genetic algorithm and a non-cooperative game approach to generate flying trajectories. Authors in Murray and Raj ( 2020 ) formulate a multiple flying sidekicks traveling salesman problem as an MILP problem, where customer parcels may be delivered by different UAVs and a single delivery truck.…”

Network-Aware Genetic Algorithms for the Coordination of MALE UAV Networks

“…In our previous work, we describe how genetic algorithms can cooperatively relocate UAVs to maximise coverage [9]. Flying trajectories are described by Dubins paths [10] consisting of 3 segments as depicted in Fig.…”

“…Calculating the packing arrays for each UAV plays an important role in the efficiency of the searching algorithm. The packing algorithm that assigns users to appropriate UAVs was initially introduced in Giagkos et al [9] and thus, its details are omitted. Packing favours those users that are low-maintenance (closer to the centre of the footprint) and, in turn, maximises the total coverage.…”