Rank-constrained separable semidefinite programming for optimal beamforming design

Consider a downlink communication system where multi-antenna base stations transmit independent data streams to decentralized single-antenna users over a common frequency band. The goal of the base stations is to jointly adjust the beamforming vectors so as to minimize the transmission powers while ensuring the signal-to-interference-noise ratio (SINR) requirement of individual users within the system, and keeping lower interference level to other systems which operate in the same frequency band and in the same region. This optimal beamforming problem is a separable homogeneous quadratically constrained quadratical programming (QCQP), and it is difficult to solve in general. In this paper, we give conditions under which strong duality holds, and propose an efficient algorithm for the optimal beamforming problem. First, we study rank-constrained solutions of a general separable semidefinite programming (SDP), and propose a rank reduction procedure to achieve a lower rank solution. Then we show that the SDP relaxation of a class of the optimal beamforming problem has a rank-one solution, which can be obtained by invoking the rank reduction procedure.