Multicell MISO Downlink Weighted Sum-Rate Maximization: A Distributed Approach

Abstract

We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g., fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.