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To accommodate the explosive wireless traffics, massive multiple-inputmultiple-output (MIMO) is regarded as one of the key enabling technologies fornext-generation communication systems. In massive MIMO cellular networks,coordinated beamforming (CBF), which jointly designs the beamformers ofmultiple base stations (BSs), is an efficient method to enhance the networkperformance. In this paper, we investigate the sum rate maximization problem ina massive MIMO mobile cellular network, where in each cell a multi-antenna BSserves multiple mobile users simultaneously via downlink beamforming. Althoughexisting optimization-based CBF algorithms can provide near-optimal solutions,they require realtime and global channel state information (CSI), in additionto their high computation complexity. It is almost impossible to apply them inpractical wireless networks, especially highly dynamic mobile cellularnetworks. Motivated by this, we propose a deep reinforcement learning baseddistributed dynamic coordinated beamforming (DDCBF) framework, which enableseach BS to determine the beamformers with only local CSI and some historicalinformation from other BSs.Besides, the beamformers can be calculated with aconsiderably lower computational complexity by exploiting neural networks andexpert knowledge, i.e., a solution structure observed from the iterativeprocedure of the weighted minimum mean square error (WMMSE) algorithm.Moreover, we provide extensive numerical simulations to validate theeffectiveness of the proposed DRL-based approach. With lower computationalcomplexity and less required information, the results show that the proposedapproach can achieve comparable performance to the centralized iterativeoptimization algorithms.

Deep Reinforcement Learning for Distributed Dynamic Coordinated Beamforming in Massive MIMO Cellular Networks