报告题目：Multiuser Downlink Beamforming: Rank-Constrained SDP Approach
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 to minimize the transmission powers while ensuring the signal-to-interference-noise ratio (SINR) requirement of each user within the system. At the same time, it may be necessary to keep the interference generated on other coexisting systems under a certain tolerable level. In addition, one may want to include general individual shaping constraints on the beamforming vectors. This beamforming problem is a separable homogeneous quadratically constrained quadratic program (QCQP), which is difficult to solve in general. In this talk, we will give conditions under which strong duality holds (the problem is then easier to solve), and propose efficient algorithms for the optimal beamforming problem. First, we study rank-constrained solutions of general separable semidefinite programs (SDPs), and propose rank reduction procedures to achieve a lower rank solution. Then we show that the SDP relaxation of three classes of optimal beamforming problem always has a rank-one solution, which can be obtained by invoking the rank reduction procedures.
Yongwei Huang (M’09) received the Ph.D. degreein operations research from the Chinese University ofHong Kong, Hong Kong, in 2005. He is a Research Associate with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology. He has held several research appointments, namely, at the Department of Systems Engineering and EngineeringManagement, Chinese University of Hong Kong; Department ofBiomedical, Electronic, and Telecommunication Engineering,University of Napoli "Federico II", Italy. His research interests are relatedto optimization theory and algorithms, including conic optimization, robustoptimization, combinatorial optimization, and stochastic optimization, andtheir applications to signal processing for wireless communications and radar.