Abstract
Massive multiple input multiple output (MIMO) has been widely applied in latest wireless communication due to its significant capacity and array gains. In practice, directional antennas rather than omnidirectional ones are used in the large-scale array of base station (BS). And the optimization of its array orientation, i.e., azimuth and tilt angles, becomes the key for realizing massive MIMO gains. In this paper, we first formulate the optimization of BS array orientation for average sum-rate maximization for the channels with hierarchical statistical structure. Via exploiting beamspace statistics to compress the channel statistical representation and employing deep reinforcement learning to relax the requirement on prior channel knowledge, an efficient scheme for azimuth and tilt adjustment is proposed to improve long-term sum-rate performance. Simulations verify that the proposed scheme achieves better performance-complexity tradeoff compared to conventional schemes.
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