Distributed Precoding for Virtual Sum-Rate Maximization in Network Massive MIMO Systems

This paper investigates the distributed precoding for network massive multi-input multi-output (MIMO) communications without data sharing between cells. In order to restrict the information exchange, which imposes significant requirements on signaling overhead, we first reformulate the original weighted sum-rate maximization problem into a cell-specific form. With this reformulated problem, we take a virtual weighted sum-rate, whose expression only depends on precoders in a single cell and some initial values, as the objective function of an approximated problem. A stationary point of this non-concave virtual weighted sum-rate maximization problem is then achieved iteratively through the minorize-maximize (MM) algorithm. After exchanging a virtual covariance matrix generated locally, each base station (BS) can solely optimize its precoding matrix in parallel without any exchange during the optimization procedure. Numerical results show that the proposed method performs well in the sense of achievable sum-rate.