Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks
This work addresses fairness enhancement in mobile networks with user mobility, but it is incremental as it applies reinforcement learning to a known optimization problem.
The paper tackled the NP-hard problem of optimally placing an aerial base station in a dynamic mobile network to enhance fairness among users, achieving results that are comparatively close to the optimal solution with reasonable computing time.
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.