Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

In the context of fifth-generation (5G)/beyond-5G (B5G) wireless communications, post-disaster emergency networks have recently gained increasing attention and interest. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. To achieve efficient and rapid communication networks, in this paper we jointly take power and coverage control into account during the UAV deployment. The noncooperative game theoretical model and amended binary log-linear algorithm (BLLA) have been adopted to achieve the optimal system performance. To deal with the challenges brought by highly dynamic post-disaster circumstances, we employ the aggregative game which is able to capture the strategies updating constraint and strategy-deciding error in large-scale UAV network. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and hence reduce strategy updating time and energy 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 with superior performance gain