Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis
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, te...
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| Published in | IEEE transactions on signal processing Vol. 68; pp. 3146 - 3157 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
| ISSN | 1053-587X 1941-0476 |
| DOI | 10.1109/TSP.2020.2993938 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Rastegarnia, Amir Khalili, Azam Vahidpour, Vahid Bazzi, Wael M. Sanei, Saeid |
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| SubjectTerms | Adaptive filters Algorithms Approximation algorithms Augmented CLMS Computational complexity Computational efficiency Computer simulation Computing costs Energy conservation Iterative methods Learning curves Mathematical analysis non-circular Numerical prediction partial-update Performance analysis Power consumption Power demand sequential algorithm Signal processing algorithms Steady state stochastic-algorithm widely linear model |
| Title | Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis |
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