Distributed Precoding for Network Massive MIMO Systems Without Data Sharing

Abstract

This article delves into the distributed precoding for network massive multiinput–multioutput (NM-MIMO) systems where no user data stream is shared among the base stations (BSs). Aiming to navigate the challenge of minimizing information exchange for weighted sum-rate (WSR) maximization, which inherently places substantial demands on signaling overheads, we begin by reformulating the original problem as a BS-specific format. Inspired by such a rewritten form, we propose a virtual WSR as the objective function to derive an approximation of this reformulated problem. For a specific BS, the calculation of this virtual WSR is solely contingent upon the precoders within its own cell and low-dimensional virtual covariance matrices as initial values. Through an iterative approach facilitated by the minorization-maximization (MM) algorithm, we attain a stationary point for the maximization of nonconcave virtual WSR. With the locally generated virtual covariance matrices exchanged as initial values, the precoding matrix within each cell can be optimized independently and concurrently. This method eliminates the need for additional external exchanges throughout the iterative process. The simulation indicates the efficacy of our proposed approach in achieving a favorable WSR.