OFDM系统的自适应低秩信道估计

Low-rank adaptive method for sub-optimal channel estimation in OFDM system

为了降低正交频分复用OFDM(Orthogonal Frequency division Multiplexing)系统中最小均方误差MMSE(Minimum Mean Square Error)信道估计算法的复杂度,并且改善由于信道的统计特性与先验知识不匹配而导致的MMSE估计性能恶化,提出了一种自适应的低秩信道估计算法.该算法利用信道的时间平均相关取代统计相关,结合了基于特征值分解的低秩建模,从而近似地实现MMSE估计.借助于子空间跟踪,该算法可以自适应地估计信道相关矩阵的主特征空间及噪声方差,以迭代的方式逼近最优的MMSE估计,而且复杂度较低.进一步分析指出基于信道延时子空间跟踪的估计算法是该算法的一种特例,理论分析和仿真结果均表明这种新算法在低信噪比时可以显著改善信道估计的准确性.

A sub-optimal channel estimation algorithm in orthogonal frequency division multiplexing （OFDM） system was proposed, to reduce the complexity of traditional minimum mean square error （MMSE） channel estimator, and to alleviate the performance deterioration of MMSE estimator due to the mismatch of the estimator-to-channel statistics. This algorithm utilizes time-average channel correlation matrix in place of exact channel correlation matrix and approximates the MMSE estimator by a low-rank structure. Based on subspace tracking, this algorithm adaptively estimates the noise power and the principal eigenspace of time-average channel correlation matrix, and asymptotically converges to the solution of optimal MMSE estimation. Further theoretical analysis indicates that delay subspace tracking algorithm is a special case of this newly proposed algorithm, and both theoretical analysis and simulation results illustrate that this algorithm can significantly improve channel estimation accuracy at low signal to noise ratio（SNR） with reduced implementation complexity.