Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Multipass Channel Estimation and Joint Multiuser Detection and Equalization for MIMO Long-Code DS/CDMA Systems | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2006 Article ID 24132 Pages 1-13 DOI WCN 2006 24132 Multipass Channel Estimation and Joint Multiuser Detection and Equalization for MIMO Long-Code DS CDMA Systems Stefano Buzzi DAEIMI Universita degli Studi di Cassino Via G. Di Biasio 43 03043 Cassino Italy Received 8 April 2005 Revised 16 October 2005 Accepted 28 November 2005 Recommended for Publication by Wolfgang Gerstacker 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. Copyright 2006 Stefano Buzzi. This
