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