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

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