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: Research Article Appropriate Algorithms for EstimatingFrequency-Selective Rician Fading MIMO Channels and Channel Rice Factor: Substantial Benefits of Rician Model and Estimator Tradeoffs | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID 753637 14 pages doi 2010 753637 Research Article Appropriate Algorithms for EstimatingFrequency-Selective Rician Fading MIMO Channels and Channel Rice Factor Substantial Benefits of Rician Model and Estimator Tradeoffs Hamid Nooralizadeh1 and Shahriar Shirvani Moghaddam2 1 Faculty Member of Electrical Engineering Department Islamshahr Branch Islamic Azad University Islamshahr 3314767653 Tehran Iran 2Department of Electrical and Computer Engineering Shahid Rajaee Teacher Training University SRTTU Tehran 16788-15811 Tehran Iran Correspondence should be addressed to Hamid Nooralizadeh h_n_alizadeh@ Received 8 May 2010 Revised 13 July 2010 Accepted 17 August 2010 Academic Editor Claude Oestges Copyright 2010 H. Nooralizadeh and S. Shirvani Moghaddam. 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. The training-based channel estimation TBCE scheme in multiple-input multiple-output MIMO frequency-selective Rician fading channels is investigated. We propose the new technique of shifted scaled least squares SSLS and the minimum mean square error MMSE estimator that are suitable to estimate the above-mentioned channel model. Analytical results show that the proposed estimators achieve much better minimum possible Bayesian Cramer-Rao lower bounds CRLBs in the frequency-selective Rician MIMO channels compared with those of Rayleigh one. It is seen that the SSLS channel estimator requires less knowledge about the channel and or has better performance than the conventional least squares LS and MMSE estimators. Simulation results confirm the superiority of the proposed channel estimators. Finally to estimate the channel Rice factor an algorithm is proposed and its efficiency is verified using
