A suboptimal minimum mean-squared error estimation （MMSE） is proposed for a dispersive wireless channel in the absence of its .orrelation matrix for multipleinput multiple-output ort,ogonal frequency division mult...A suboptimal minimum mean-squared error estimation （MMSE） is proposed for a dispersive wireless channel in the absence of its .orrelation matrix for multipleinput multiple-output ort,ogonal frequency division multiplexing （MIMO - OFDM） transmission. It utilizes a fast subspace approximation tracking to separate signal subspace with a limited set of channel estimates. The subspace rank is adjusted by pre-set thresholds in different signal-to-noise ratios （SNRs）. The performance comparison among the proposed algorithm, least square based, and the optimal MMSE estimation is shown by numerical simulation under a spatially correlated multi-tap channel scenario. It demonstrates that the approach has better normalized mean square error than recursive least square estimation and yields 3 dB gain over the latter.展开更多
基金National Natural Science Foundation of China (No.60572157)International Cooperation Foundation of Shanghai Jiaotong University,China (No.2008DFA11950)
文摘A suboptimal minimum mean-squared error estimation （MMSE） is proposed for a dispersive wireless channel in the absence of its .orrelation matrix for multipleinput multiple-output ort,ogonal frequency division multiplexing （MIMO - OFDM） transmission. It utilizes a fast subspace approximation tracking to separate signal subspace with a limited set of channel estimates. The subspace rank is adjusted by pre-set thresholds in different signal-to-noise ratios （SNRs）. The performance comparison among the proposed algorithm, least square based, and the optimal MMSE estimation is shown by numerical simulation under a spatially correlated multi-tap channel scenario. It demonstrates that the approach has better normalized mean square error than recursive least square estimation and yields 3 dB gain over the latter.
