Reinforcement Learning Power Control Algorithm Based on Graph Signal Processing for Ultra-Dense Mobile Networks

Abstract

Ultra-dense mobile networks (UDMNs) represent a promising technology for improving the network performance and providing the ubiquitous network accessibility in the beyond 5 G (B5G) mobile networks. Heterogenous densely deployed networks can dynamically offer high spectrum efficiency and enhance frequency reuse, which ultimately improves quality of service (QoS) and the user experience. However, mass inter- or intra-cell interference generated from overlap between small cells greatly limits network performance, especially when there is mobility between UEs and access points (APs). Even so, when network density increases, the complexity of conventional allocation methods can increase also. In this paper, we investigate a power control of downlink (DL) connection in the UNMNs with different types of APs. We propose a reinforcement learning (RL) power allocation algorithm based on graph signal processing (GSP) for ultra-dense mobile networks. Firstly, we construct a realistic system model under ultra-dense mobile networking, which includes the system channel mode and instantaneous rate. Then we employ a GSP tool to analyze network interference, the interference analysis results for the entire network are obtained to determine optimal RL power allocation. Finally, simulation results indicate that the proposed RL power control algorithm outperforms baseline algorithms when applied to a ultra-dense mobile networks.