Performance Study of RSS ML Detection with Imperfect Channel and Noise Estimates

RSS ML MIMO detection scheme which reduces the search space greatly comparing with the ideal ML detection but still achieves the near optimal performance is proposed. The search space reducing is carried out before performing the exhaustive search. Based on the output of ZF equalization, the metrics used for determining the reduced search space, which are the posterior probabilities in this paper, can be computed. In this paper, we focus on investigating the performance of the proposed detection scheme under the condition of imperfect channel and noise estimates. According to the simulation results, the near optimal performance can be obtained while the detection complexity is far less than that of the ideal ML detection.