Q-learning based aerial base station placement for fairness enhancement in mobile networks
In this paper, we use an aerial base station (aerial-BS) to enhance fairness in a dynamic environment with user mobility. The problem of optimally placing the aerial-BS is a non-deterministic polynomial-time hard (NP-hard) problem. Moreover, the network topology is subject to continuous changes due to the user mobility. These issues intensify the quest to develop an adaptive and fast algorithm for 3D placement of the aerial-BS. To this end, we propose a method based on reinforcement learning to achieve these goals. Simulation results show that our method increases fairness among users in a reasonable computing time, while the solution is comparatively close to the optimal solution obtained by exhaustive search.
|Conference||7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019|
Ghanavi, R. (Rozhina), Sabbaghian, M. (Maryam), & Yanikomeroglu, H. (2019). Q-learning based aerial base station placement for fairness enhancement in mobile networks. In GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings. doi:10.1109/GlobalSIP45357.2019.8969198