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In the context of fifth-generation (5G)/beyond-5G (B5G) wirelesscommunications, post-disaster emergency networks have recently gainedincreasing attention and interest. Unmanned aerial vehicles (UAV)-enabledad-hoc network is a significant contingency plan for communication afternatural disasters, such as typhoon and earthquake. To achieve efficient andrapid communication networks, in this paper we jointly take power and coveragecontrol into account during the UAV deployment. The noncooperative gametheoretical model and amended binary log-linear algorithm (BLLA) have beenadopted to achieve the optimal system performance. To deal with the challengesbrought by highly dynamic post-disaster circumstances, we employ theaggregative game which is able to capture the strategies updating constraintand strategy-deciding error in large-scale UAV network. Moreover, we propose anovel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) tolessen information exchange and hence reduce strategy updating time and energyconsumption. Ultimately, the experiments indicate that, under the samestrategy-deciding error rate, SPBLLA's learning rate is manifestly faster thanthat of the revised BLLA with superior performance gain.

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