Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems

Beam codebooks are integral components of future millimeter wave MIMO systems. Therefore, it is critical to optimize these codebooks for efficient and reliable communications. Prior work has focused on single-cell codebook learning problems and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences is that the underlying environment becomes non-stationary, as the behavior of one basestation may influence the learning of the others. Further, we avoid information exchange between different learning nodes which leads to a fully decentralized system with increased learning difficulties. To tackle the non-stationarity, the averaging of measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator, and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function is proposed to decouple the learning of the multiple agents running at different nodes. Results show that the developed solution is capable of learning well-shaped codebook patterns for different networks and significantly suppress the interference without requiring any information exchange between basestations.