Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach

TL;DR

This work tackles post-disaster UAV networks where rapid, scalable deployment requires balancing energy, SNR, and coverage amid strong dynamics. It introduces a large-scale multi-aggregator aggregative game to capture collective interference and coverage effects, and presents two learning schemes: PBLLA (payoff-based) and SPBLLA (synchronous payoff-based) that update strategies under constrained action sets. SPBLLA, leveraging synchronous updates and stochastic process theory, achieves substantially faster convergence and better SNR/coverage in simulations, illustrating its suitability for large, dynamic UAV deployments. The study demonstrates that incorporating aggregative structure and synchronized learning significantly improves efficiency and network performance in disaster-response scenarios.

Abstract

Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.

Figures (16)

• Figure 1: The topological structure of UAV ad-hoc networks. a) The UAV ad-hoc network supports user communications. b) The coverage of a UAV depends on its altitude and field angle. c) There are two kinds of links between users, and the link supported by UAV is better.
• Figure 2: Coverage overlap between two UAVs. When two UAVs are close, there will be overlap areas among them and the utility of coverage will decrease.
• Figure 3: Two trees rooted at $s_3$
• Figure 4: Effect of dynamic degree index $\tau$ on PBLLA ($10^6$ iterations). Severe dynamic scenarios cause fewer utilities of the whole networks.
• Figure 5: Effect of dynamic degree index $\tau$ on PBLLA's fluctuation. Two horizontal lines show the fluctuation range. Higher dynamic scenario causes severe fluctuation.

Theorems & Definitions (13)

• Definition 1
• Definition 2
• Definition 3
• Theorem 1
• Theorem 2
• Theorem 3
• Definition 4
• Definition 5
• Definition 6
• Theorem 4