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- W2369970529 abstract "By transforming received signals from frequency domain to transform-domain, subspace based channel estimation can distinguish between signal subspace and noise subspace using channel statistic information to separate signal from noise with low complexity. In this paper, an adaptive threshold algorithm is deduced based on MSE criterion, which average the successive OFDMs in time domain and efficiently counters the AWGN and ICI. Using the Statistic feature of Gaussian distribution of the noise, the GPT algorithm is proposed. To be adaptive with fading of the channel, robust TAAT algorithm is proposed. The two algorithm proposed in this paper can achieve high resolution of signals without pre-channel information, not subject to frequency leakage and doesn't rely on channel statistic information which causes instability to MST and lowpass algorithms. Simulations under VA30 channel show the algorithm can approaching the performance of perfect DFT channel estimation." @default.
- W2369970529 created "2016-06-24" @default.
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- W2369970529 date "2007-01-01" @default.
- W2369970529 modified "2023-09-24" @default.
- W2369970529 title "Improvement on Subspace Based Channel Estimation in OFDM Systems" @default.
- W2369970529 hasPublicationYear "2007" @default.
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