Nonlinear channel equalization by multi-kernel adaptive filter

In this paper, a novel adaptive channel-equalization scheme using multiple kernels is proposed. The proposed scheme equalizes distorted signals by means of a linear combination of multiple Gaussian functions having different means and variances. The combination coefficients are computed by the multi-kernel normalized least squares with block soft-thresholding (MKNLMS-BT) algorithm. To deal with channel nonstationarity, an efficient technique to control the block soft-thresholding operator itself adaptively is presented, based on the block ℓ_p-norm (0 <; p <; 1). The proposed scheme enjoys low computational complexity and global optimality (unlike the Volterra filtering and neural network approaches) as well as high estimation accuracy. Simulation results show the efficacy of the proposed scheme both in stationary and nonstationary environments.