基于平滑l0范数的OFDM系统稀疏信道估计

摘要

Channel estimation is one of the key technologies in wireless communication system. In order to improve the accuracy of channel estimation, for the problem that the existing channel estimation methods based on the theory of compressed sensing can only reconstruct channel parameters under the circumstances of knowing sparse degrees of channel impulse response in advance, a new method of Orthogonal Frequency Division Multiplexing( OFDM) sparse channel estimation based on Compressive Sensing(CS) was proposed, and the SLO algorithm used smoothed l0 norm. In this paper, the hyperbolic tangent function was used to improve SLO algorithm. Simulated experiment results show that under the same condition, compared the traditional Least Square and matching pursuit with compressible samples matching pursuit-algorithm, the proposed algorithm's convergence speed is faster, and the estimated mean square error is smaller, which can reduce the complexity of the system.%信道估计是无线通信系统的关键技术.为了提高信道估计的精确性,针对现有压缩传感的信道估计算法需预先知道信道稀疏度的问题,提出了一种新的基于压缩传感理论的正交频分复用系统信道估计平滑l0范数(Smoothed l0 Norm,SLO)算法,并引入双曲正切函数对SL0算法进行改进.仿真结果表明,在相同条件下,与传统最小二乘(LS)以及匹配追踪(MP)和压缩采样匹配追踪(CoSaMP)算法比较,改进算法收敛速度快,估计值均方误差小,降低了系统复杂度.