Optimal Resource Allocation via Machine Learning in Coordinated Downlink Multi-Cell OFDM Networks under Imperfect CSI

Abstract

Considering a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). In this paper, we divide it into a user scheduling subproblem and a power allocation subprolem, and then adopt a resource allocation algorithm based on imperfect channel state information (CSI). Universal frequency reuse is considered, and the cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. Since the wireless communication environment may change rapidly and need real-time computation, we then propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time and is capable of ”on-the-fly” adaptation to a time-varying environment. Simulation results verify the effectiveness of the DNN implementation, especially when the number of cells and subcarriers is large.