1st International ICST Conference on Communications and Networking in China

Research Article

RLS Channel Estimation and Data Detection in Space-Time Coded MIMO-OFDM Systems

  • @INPROCEEDINGS{10.1109/CHINACOM.2006.344760,
        author={Yongming  Liang and Hanwen  Luo and Renmao Liu and Chongguang  Yan},
        title={RLS Channel Estimation and Data Detection in Space-Time Coded MIMO-OFDM Systems},
        proceedings={1st International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2007},
        month={4},
        keywords={},
        doi={10.1109/CHINACOM.2006.344760}
    }
    
  • Yongming Liang
    Hanwen Luo
    Renmao Liu
    Chongguang Yan
    Year: 2007
    RLS Channel Estimation and Data Detection in Space-Time Coded MIMO-OFDM Systems
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2006.344760
Yongming Liang1,2, Hanwen Luo1, Renmao Liu2, Chongguang Yan2
  • 1: Department of Electronic Engineering, Shanghai Jiao Tong University, China
  • 2: Advanced R&D Centre, Sharp Electronics (Shanghai) Co., Ltd, China

Abstract

Multiple-input and multiple-out (MIMO) can increase the channel capacity. Orthogonal frequency division multiplexing (OFDM) can mitigate the effects of delay spread in the frequency selective-fading channels. And space-time coding techniques can improve the system performance. So the combination of MIMO, OFDM and space-time coding techniques (space-time coded MIMO-OFDM) is an attractive method for high-data-rate wireless applications. Moreover, accurate channel state information is essential to diversity combination, coherent detection and decoding in a space-time coded MIMO-OFDM system. Therefore, a method of RLS (recursive least squares) channel estimation and maximum likelihood (ML) data detection is proposed in this paper. The RLS filter is employed in proposed channel estimation method. Simulation results confirm that this proposed RLS channel estimation method has better performances than the LMS (least mean square) or LS (least square) channel estimation method at the cost of moderate computational complexity.