An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments.

I. Introduction

Consider a multiple-input multiple-output (MIMO) interfering broadcast channel (IBC) in which a number of transmitters, each equipped with multiple antennas, wish to simultaneously send independent data streams to their intended receivers. As a generic model for multiuser downlink communication, MIMO-IBC can be used in the study of many practical systems such as digital subscriber lines (DSLs), cognitive radio systems, ad hoc wireless networks, wireless cellular communication, to name just a few. Unfortunately, despite the importance and years of intensive research, the search for optimal transmit/receive strategies that can maximize the weighted sum-rate of all users in a MIMO-IBC remains rather elusive. In fact, even for the simpler case of MIMO interference channel, the optimal strategy is still unknown. This lack of understanding of the capacity region has motivated a pragmatic approach whereby we simply treat interference as noise and maximize the weighted sum-rate by searching within the class of linear transmit/receive strategies.