Intra-RAN Online Distributed Reinforcement Learning For Uplink Power
Control in 5G Cellular Networks
Abstract
Uplink power control plays a significant role in maintaining a good
signal quality at the serving cell while minimizing interference to
neighboring cells, thus maximizing the system performance.
Traditionally, a single value open-loop power control (OLPC) parameter,
P0, is configured for all the user equipments (UEs) in a cell, and often
same setting is used for similar cells. Recent studies have demonstrated
that optimal P0 depends on many factors, which yields a complex
multidimensional optimization problem and there are no efficient
approaches known to solve it under practical system-level settings. In
this paper, we propose a solution based on reinforcement learning (RL)
where each BS autonomously adjusts its P0 setting to maximize its
throughput performance. As compared to conventional sub-optimal
approach, our solution encompasses a smart clustering of UEs, where each
cluster specifies its own P0. The proposed solution is evaluated by
extensive system level simulations, where our results demonstrate a
potential performance enhancement as compared to the baseline proposals.