Power Control using Distributed Reinforcement Learning for Forward Link Soft Handoff in Cellular CDMA Systems

Power control for forward link soft handoff users in cellular CDMA systems is important in order to obtain good forward link performance. The two existing schemes in the 3GPP specification, balancing power control (BPC) and site selection diversity transmission (SSDT), both have shortcomings due to their static properties. In this paper, we propose a dynamic power control scheme which can modify the power control policy according to changing environment situations. We apply a distributed reinforcement learning (DRL)-based coordinated decision-making method to achieve dynamic power control. The main idea is to learn the optimal transmission power levels from BSs under different environmental situations with multiple mobile users. Our simulation results show that the proposed scheme effectively combines the advantages of existing schemes in order to maximize the forward link capacity.