Analysis and Design of Unified Architectures for Zero-Attraction-Based Sparse Adaptive Filters

Zero-attraction-based adaptive filters are widely used for sparse system identification, where a suitable penalty function is integrated with the least mean square (LMS) framework to improve the convergence behavior of the identification process. In this brief, we have made an attempt to implement some of the most popular zero-attracting algorithms in hardware. The complexity of realization associated with these algorithms is investigated in detail. Following the above analysis, several architectural simplifications are proposed for the reduced-complexity implementation of their penalty functions. We then use these realizations to develop a set of novel design strategies for the efficient implementation of these algorithms. Simulation results show that the performance loss for the proposed algorithms is minimal compared to their standard versions. A detailed synthesis study is also carried out to validate the proposed structures, which demonstrates that the hardware overhead in the proposed designs is marginal compared to the existing delayed LMS architecture.