Multipass Channel Estimation and Joint Multiuser Detection and Equalization for MIMO Long-Code DS/CDMA Systems

Open Access
Research Article

Abstract

The problem of joint channel estimation, equalization, and multiuser detection for a multiantenna DS/CDMA system operating over a frequency-selective fading channel and adopting long aperiodic spreading codes is considered in this paper. First of all, we present several channel estimation and multiuser data detection schemes suited for multiantenna long-code DS/CDMA systems. Then, a multipass strategy, wherein the data detection and the channel estimation procedures exchange information in a recursive fashion, is introduced and analyzed for the proposed scenario. Remarkably, this strategy provides, at the price of some attendant computational complexity increase, excellent performance even when very short training sequences are transmitted, and thus couples together the conflicting advantages of both trained and blind systems, that is, good performance and no wasted bandwidth, respectively. Space-time coded systems are also considered, and it is shown that the multipass strategy provides excellent results for such systems also. Likewise, it is also shown that excellent performance is achieved also when each user adopts the same spreading code for all of its transmit antennas. The validity of the proposed procedure is corroborated by both simulation results and analytical findings. In particular, it is shown that adopting the multipass strategy results in a remarkable reduction of the channel estimation mean-square error and of the optimal length of the training sequence.

Keywords

Channel Estimation Training Sequence Data Detection Multiuser Detection Spreading Code 

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Copyright information

© Stefano Buzzi 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.DAEIMIUniversità degli Studi di CassinoCassinoItaly

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