A unified receiver for MIMO communication with imperfect channel knowledge

Abstract

The problem of MIMO communications with imperfect channel knowledge at the receiver is considered. Imperfect channel knowledge results from errors due to noise and errors due to time variations which cause the channel estimates to become outdated. A receiver structure named the Unified Generalized Likelihood Ratio Detector (UGLRD) is presented. The UGLRD is based on joint symbol-channel estimation, and it accommodates variations in the availability and the reliability of the Channel State Information (CSI) at the receiver. It can be applied to blind and to differential MIMO systems. The performance of this receiver is compared with that of a conventional MIMO receiver for different channel conditions. It is shown that the UGLRD outperforms the conventional receiver at the cost of higher computational complexity which is addressed using an efficient search algorithm.

Performance of a MIMO receiver using joint channel-symbol estimation in the presence of channel errors

“…In principle, at high SNRs the performance of a MIMO receiver which applies joint channel-symbol estimation approaches the performance of an This work was supported in part by the National Science Foundation under grant CCF-0725366 and in part by Huawei Technologies Co. Ltd. optimal Maximum-Likelihood (ML) MIMO receiver which assumes perfect channel knowledge. This occurs independently of the amount of error encountered in channel estimation [5]. The price paid for this improved performance is increased computational complexity which can be addressed through search efficient algorithms.…”

“…Compromising the quality of the channel estimate through the minimal usage of pilots results in severe performance degradation of the conventional Mismatched Maximum Likelihood (MML) MIMO receiver [1], [2]. A better receiver structure then is one which relies of joint channel-symbol estimation techniques [3], [4], [5]. In principle, at high SNRs the performance of a MIMO receiver which applies joint channel-symbol estimation approaches the performance of an This work was supported in part by the National Science Foundation under grant CCF-0725366 and in part by Huawei Technologies Co. Ltd. optimal Maximum-Likelihood (ML) MIMO receiver which assumes perfect channel knowledge.…”