l 0-norm penalised shrinkage linear and widely linear LMS algorithms for sparse system identification

摘要: In this study, the authors propose an l 0-norm penalised shrinkage linear least mean squares (l 0-SH-LMS) algorithm and widely 0-SH-WL-LMS) for sparse system identification. The proposed algorithms exploit priori posteriori errors to calculate varying step-size, thus they can adapt time-varying channel. Meanwhile, in cost function introduce a penalty term that favours sparsity enable applicability condition. Moreover, 0-SH-WL-LMS also makes full use of non-circular properties signals interest improve tracking capability estimation performance. Quantitative analysis convergence behaviour verifies capabilities algorithms. Simulation results show compared with existing squares-type algorithms, perform better channels faster rate lower steady-state error. When channel changes suddenly, filter variation quickly.