采用奇异值分解方法的半盲信道辨识算法

Semi-Blind channel identification method based on singular value decomposition

摘要:

针对全盲信道辨识算法无法辨识含公零点信道且对信道阶数误差敏感的问题，提出一种半盲信道辨识算法。通过奇异值分解将信道矩阵分解为同维矩阵与酉矩阵乘积的形式，分别利用接收数据和已知符号求解同维矩阵与酉矩阵，最终得到信道矩阵的闭式解。该算法有效地克服了全盲信道辨识算法的诸多局限性，避免了传统半盲方法面临的最优加权选择问题，性能稳定，且对信道噪声与信道阶数都具有较强的鲁棒性。仿真实验验证了所提算法的有效性与优越性。

Abstract:

Most of blind channel identification algorithms cannot estimate the channel with common zeros and are sensitive to the channel order error. A Semi-Blind(SB) channel identification method is proposed in this paper. Through this algorithm, the channel matrix is decomposed into a same dimensional matrix and a unitary matrix in the form of their product based on Singular Value Decomposition(SVD). By using the received data and some known symbols to solve same dimensional matrix and the unitary matrix respectively, the closed form solution of channel response is obtained. The proposed algorithm effectively overcomes many limitations of blind channel identification algorithms. It avoids the selection of optimal weighted parameter of the traditional semi-blind methods. It is stable and robust to channel noise and channel order. Simulation results verify the effectiveness and superiority of the proposed algorithm.