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A New Affine Projection Algorithm with Adaptive \(l_{0}\)-norm Constraint for Block-Sparse System Identification

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Abstract

To improve the performance of identifying the block-sparse systems, firstly, a new sparsity aware block-sparse proportionate affine projection algorithm (BS-PAPA) with a fixed zero attractor is proposed in this paper. The proposed algorithm is obtained by integrating a weighted \(l_{0}\)-norm penalty into the cost function of the BS-PAPA. Adding the penalty results in zero attraction terms in the weight update recursive equation of the BS-PAPA, which helps in the shrinkage of inactive coefficients. The proposed algorithm is named \(l_{0}\)-block-sparse proportionate affine projection algorithm (\(l_{0}\)-BS-PAPA). The convergence analysis in the mean is derived for the \(l_{0}\)-BS-PAPA. Secondly, for applications having time varying measurement noise, an adaptive zero attractor \(l_{0}\)-BS-PAPA is also developed by adaptive optimisation of the zero attractor. This one is more robust to the varying degree of measurement noise than the former. A variable step-size \(l_{0}\)-BS-PAPA is also introduced to further enhance the performance of the \(l_{0}\)-BS-PAPA. Computer simulation experiments reveal that the \(l_{0}\)-BS-PAPA outperforms the existing algorithms in block-sparse systems in terms of convergence rate, normalised misalignment, and tracking ability.

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References

  1. S.M. Boopalan, S. Alagala, A. Ramalingam, A memory sparse proportionate affine projection algorithm for echo cancellation: analysis and simulations. Arab. J. Sci. Eng. 47(3), 3367–3381 (2022). https://doi.org/10.1007/s13369-021-06219-w

    Article  Google Scholar 

  2. B.K. Das, A. Mukherjee, M. Chakraborty, Block-sparsity-induced system identification using efficient adaptive filtering, in 2020 National Conference on Communications (NCC), pp. 1–6 (2020). https://doi.org/10.1109/NCC48643.2020.9056024

  3. D.L. Duttweiler, Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Trans. Speech Audio Process. 8(5), 508–518 (2000). https://doi.org/10.1109/89.861368

    Article  Google Scholar 

  4. Y. Gu, J. Jin, S. Mei, \(l_{0}\) norm constraint LMS algorithm for sparse system identification. IEEE Signal Process. Lett. 16(9), 774–777 (2009). https://doi.org/10.1109/LSP.2009.2024736

    Article  Google Scholar 

  5. G. Guo, Y. Yu, R.C. Lamare, Z. Zheng, L. Lu, Q. Cai, Proximal normalized subband adaptive filtering for acoustic echo cancellation. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 2174–2188 (2021). https://doi.org/10.1109/TASLP.2021.3087951

    Article  Google Scholar 

  6. L. Ji, J. Ni, Sparsity-aware normalized subband adaptive filters with jointly optimized parameters. J. Frankl. Inst. 357(17), 13144–13157 (2020). https://doi.org/10.1016/j.jfranklin.2020.09.015

    Article  MathSciNet  MATH  Google Scholar 

  7. D. Jin, J. Chen, C. Richard, J. Chen, Model-driven online parameter adjustment for zero-attracting LMS. Signal Process. 152, 373–383 (2018). https://doi.org/10.1016/j.sigpro.2018.06.020

    Article  Google Scholar 

  8. A.W.H. Khong, P.A. Naylor, Efficient use of sparse adaptive filters, in 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, pp. 1375–1379 (2006)

  9. Y. Li, Z. Jiang, O.M.O. Osman, X. Han, J. Yin, Mixed norm constrained sparse APA algorithm for satellite and network echo channel estimation. IEEE Access 6, 65901–65908 (2018). https://doi.org/10.1109/ACCESS.2018.2878310

    Article  Google Scholar 

  10. M.V. Lima, W.A. Martins, P.S. Diniz, Affine projection algorithms for sparse system identification, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5666–5670 (2013) https://doi.org/10.1109/ICASSP.2013.6638749

  11. J. Liu, S.L. Grant, Proportionate adaptive filtering for block-sparse system identification. IEEE/ACM Trans. Audio Speech Lang. Process. 24(4), 623–630 (2016). https://doi.org/10.1109/TASLP.2015.2499602

    Article  Google Scholar 

  12. J. Liu, S.L. Grant, Proportionate affine projection algorithms for block-sparse system identification, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 529–533 (2016). https://doi.org/10.1109/ICASSP.2016.7471731

  13. K. Ozeki, Theory of Affine Projection Algorithms for Adaptive Filtering (Springer, Berlin, 2016)

    Book  MATH  Google Scholar 

  14. C. Paleologu, J. Benesty, S. Ciochina, A variable step-size affine projection algorithm designed for acoustic echo cancellation. IEEE Trans. Audio Speech Lang. Process. 16(8), 1466–1478 (2008). https://doi.org/10.1109/TASL.2008.2002980

    Article  Google Scholar 

  15. A. Sayed, Fundamentals of Adaptive Filtering (Wiley, New York, 2003)

    Google Scholar 

  16. D. Sun, L. Liu, Y. Zhang, Recursive regularisation parameter selection for sparse RLS algorithm. Electron. Lett. 54(5), 286–287 (2018). https://doi.org/10.1049/el.2017.4242

    Article  Google Scholar 

  17. C. Wang, Y. Zhang, Y. Wei, N. Li, A new \(l_0\)-LMS algorithm with adaptive zero attractor. IEEE Commun. Lett. 19(12), 2150–2153 (2015). https://doi.org/10.1109/LCOMM.2015.2490665

    Article  Google Scholar 

  18. W. Wang, H. Zhao, A novel block-sparse proportionate NLMS algorithm based on the \( l_{2,0}\) norm. Signal Process. 176(107), 671 (2020). https://doi.org/10.1016/j.sigpro.2020.107671

    Article  Google Scholar 

  19. Y. Yu, T. Yang, H. Chen, R.C. de Lamare, Y. Li, Sparsity-aware SSAF algorithm with individual weighting factors: performance analysis and improvements in acoustic echo cancellation. Signal Process. 178(107), 806 (2021). https://doi.org/10.1016/j.sigpro.2020.107806

    Article  Google Scholar 

  20. Y. Yu, L. Lu, Y. Zakharov, R.C. Lamare, B. Chen, Robust sparsity-aware RLS algorithms with jointly-optimized parameters against impulsive noise. IEEE Signal Process. Lett. 29, 1037–1041 (2022). https://doi.org/10.1109/LSP.2022.3166395

    Article  Google Scholar 

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Correspondence to Senthil Murugan Boopalan.

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Boopalan, S.M., Alagala, S. A New Affine Projection Algorithm with Adaptive \(l_{0}\)-norm Constraint for Block-Sparse System Identification. Circuits Syst Signal Process 42, 1792–1807 (2023). https://doi.org/10.1007/s00034-022-02197-y

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