Steady-state mean-square performance analysis of the block-sparse maximum Versoria criterion

Abstract

The maximum Versoria criterion algorithm (MVC) exhibits lower steady-state misalignment and less complexity as compared to the maximum correntropy criterion (MCC) algorithm in the scenario of non-Gaussian impulsive noises. However, few scholars have discussed improving the MVC algorithm in sparse channels. This paper presents a block-sparse MVC (BS-MVC) algorithm by introducing the regularization norm, which has desirable performance in block-sparse channels and has excellent robustness to non-Gaussian conditions with impulsive noises. The steady-state excess mean-square error (EMSE) is discussed in Gaussian and non-Gaussian noise conditions. The effectiveness of BS-MVC and the theoretical expression is validated using multiple simulations. The BS-MVC achieves a lower steady-state mean-square deviation (MSD) and maintains a fast convergence rate compared with MCC and MVC algorithms.