数学与系统科学研究院

计算数学所学术报告会

报告人：       Prof. Zhiquan Luo

                 Department of Electrical and Computer Engineering, University of Minnesota, USA

报告题目：

Probabilistic Analysis of SDP Relaxation for Binary Quadratic Minimization with Application to Wireless Communication

报告摘要：

  Abstract: Despite its optimal bit-error-rate (BER) performance, the maximum-likelihood (ML) detection is known to be NP-hard and suffers from high computational complexity. The currently popular suboptimal detectors either achieve a polynomial time complexity at the expense of BER performance degradation (e.g., MMSE Detector), or offer a near ML performance with a complexity that is exponential in the worst case. This work considers a highly efficient (polynomial worst case complexity) quasi-ML detection method based on Semi-Definite (SDP) relaxation. It is shown that, for a standard vector Rayleigh fading channel, this SDP-based quasi-ML detector achieves, in the high signal-to-noise ratio (SNR) region, a BER which is identical to that of the exact ML detector. In the low SNR region we use the random matrix theory to show that the SDP-based detector serves as a constant factor approximation to the ML detector for large systems.

报告时间： 2005年6月24日(周五) 上午10:30-11:30

报告地点：科技综合楼三层311报告厅

          欢迎大家参加!