Optimal Resource Allocation via Machine Learning in Coordinated Downlink Multi-Cell OFDM Networks under High Mobility

For a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). Doppler shift, however, leads to a loss of subcarrier orthogonality, resulting in inter-carrier interference (ICI), especially in a high speed environment. In this paper, we solve the resource allocation problem by considering ICI caused by Doppler spread and imperfect channel state information (CSI) caused by estimation errors, quantization errors and feedback delay. However, the resultant resource allocation algorithm is so complicated that it may not be applicable to the wireless communications environment under high mobility since it may change rapidly and therefore needs real-time computation. As such we propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time while achieving very good prediction accuracy. Simulation results verify the influence of Doppler shift on the SE performance and the effectiveness of DNNs in terms of computing time.