2019/12/18 by Vahid Vahidpour, Vahidpour, Vahid, Amir Rastegarnia +7
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Direction-of-Arrival Estimation Techniques #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Parallel #Signal Processing (eess.SP) #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #cs.DC #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2001.08981
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arxiv created 2019/12/18 · openalex publication_date 2019/12/18 · arxiv updated 2020/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally- and memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partial-update schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partial-update implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for non-circular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples.