非高斯噪声环境下基于RLS的稀疏信道估计算法

Recursive least square based sparse channel estimation under non-Gaussian noise background

现有的信道估计算法大多是基于高斯噪声模型假设。然而在实际无线通信环境中,常常出现脉冲噪声使得噪声不再满足高斯模型,而是满足一种广义高斯分布(GGD)噪声模型。采用传统的自适应信道估计算法(如递归最小二乘(RLS)算法)无法抑制这种非高斯噪声的干扰。对此提出一种可抑制非高斯噪声干扰的RLS信道估计算法。该算法通过在标准RLS算法中引入两种稀疏约束函数(L1-范数和L0-范数)来有效地挖掘稀疏结构信息。通过蒙特卡罗仿真,验证了提出的信道估计算法的估计性能比标准RLS算法更好。

Most of the proposed channel estimation algorithms are based on the assumption of Gaussian noise model. In actual wire-less communication environment, however, the existence of impulse noise makes it no longer satisfy the Gaussian noise model, but meet the generalized Gaussian distribution（ GGD） noise model. The traditional adaptive channel estimation algorithm, such as recursion least square（ RLS） algorithm, cannot suppress the non- Gaussian noise interference. Under this background, a RLS channel estimation algorithm, which inhibits the non- Gaussian noise interference, is proposed. The proposed algorithm exploits sparse structure informa-tion effectively by introducing two kinds of sparse constraint functions（ L1-norm and L0-norm） into the standard RLS algorithm.The Monte Carlo simulation results show that the proposed algorithm has the better estimation performance than the standard RLS.