OFDM系统中基于子空间分解的信道估计的改进

基于子空间分解的信道估计通过将接收端信道的频域信道响转换到变换域,将信号子空间和噪声子空间分离,既能有效的分离噪声,又具有较低的复杂度.通过对信道估计均方误差的推导,本文提出了一种阈值算法GPT将信号噪声分离的信道估计算法.通过在时域上对连续的OFDM符号求平均,有效的抑制了AWGN和ICI.进一步考虑信道衰落,对阈值进行自适应调整,提出了TAAT算法,仿真证明文中提出的两种算法均无需信道先验信息,能够正确的高精度的区分信号和噪声子空间,不受频谱泄漏的影响,克服了MST和低通截短算法对信道统计参数依赖性具有鲁棒性.在VA30,典型城市信道信道仿真表明,该算法性能逼近理想DFT时域信道估计,即假设信道信号径位置已知,噪声径完全去除.

Improvement on Subspace Based Channel Estimation in OFDM Systems

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.