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

In the context of 6G, airborne post-disaster emergency networks (PENs) could be resilient in calamities and offer hope for disaster recovery in the underserved disaster zone. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is such a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. Specially, we present possible technological solutions for PENs targets for counteracting any large-scale disasters to achieve efficient communication and rapid network deployment. To this end, in this paper we jointly take power and coverage control into account during the UAV network configuration. An innovative noncooperative game theoretical model and improved 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 networks. 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. Superior performance gains are seen in SNR and network coverage and hence render a great network solution in emergency scenarios.