Deep Learning Based Massive MIMO Beamforming for 5G Mobile Network

The rapid increasing of the data volume in mobile networks forces operators to look into different options for capacity improvement. Thus, modern 5G networks became more complex in terms of deployment and management. Therefore, new approaches are needed to simplify network design and management by enabling self-organizing capabilities. In this paper, we propose a novel intelligent algorithm for performance optimization of the massive MIMO beamforming. The key novelty of the proposed algorithm is in the combination of three neural networks which cooperatively implement the deep adversarial reinforcement learning workflow. In the proposed system, one neural network is trained to generate realistic user mobility patterns, which are then used by second neural network to produce relevant antenna diagram. Meanwhile, third neural network estimates the efficiency of the generated antenna diagram returns corresponding reward to both networks. The advantage of the proposed approach is that it leans by itself and does not require large training datasets.