Acknowledgement
The authors are grateful for the funding support of the Science and Engineering Research Board (SERB), Department of Science & Technology (DST), Government of India, vide file no. SRG/2020/002486.
References
- P. Qarabaqi and M. Stojanovic, Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels, IEEE J. Ocean. Eng. 38 (2013), no. 4, 701-717. https://doi.org/10.1109/JOE.2013.2278787
- Y. Zhang, Y. V. Zakharov, and J. Li, Soft-decision-driven sparse channel estimation and turbo equalization for MIMO underwater acoustic Communications, IEEE Access 6 (2018), 4955-4973. https://doi.org/10.1109/ACCESS.2018.2794455
- Y. Tian, X. Han, J. Yin, and Y. Li, Adaption penalized complex LMS for sparse under-ice acoustic channel estimations, IEEE Access 6 (2018), 63214-63222.
- Z. Qin, J. Tao, and X. Han, Sparse direct adaptive equalization based on proportionate recursive least squares algorithm for multiple-input multiple-output underwater acoustic communications, J. Acous. Soc. Am. 148 (2020), no. 4, 2280-2287. https://doi.org/10.1121/10.0002276
- P. Kumar, V. K. Trivedi, and P. Kumar, Recent trends in multicarrier underwater acoustic communications, (IEEE Underwater Technology, Chemmai, India), Feb. 2015, pp. 1-8.
- L. Liu, D. Sun, and Y. Zhang, A family of sparse group Lasso RLS algorithms with adaptive regularization parameters for adaptive decision feedback equalizer in the underwater acoustic communication system, Phys. Commun. 23 (2017), 114-124. https://doi.org/10.1016/j.phycom.2017.03.005
- K. Pelekanakis and M. Chitre, Comparison of sparse adaptive filters for underwater acoustic channel equalization/estimation, (IEEE International Conference on Communication Systems, Singapore), Nov. 2010, pp. 395-399.
- S. Haykin, Adaptive filter theory, Pearson Education, 2014.
- B. Widrow and S. D. Stearns, Adaptive signal processing, Pearson, India, 2016.
- E. J. Candes, M. B. Wakin, and S. P. Boyd, Enhancing sparsity by reweighted l1 minimization, J. Fourier Anal. Appl. 14 (2008), no. 5-6, 877-905. https://doi.org/10.1007/s00041-008-9045-x
- D. L. Duttweiler, Proportionate normalized least-mean-squares adaptation in echo cancelers, IEEE Trans. Speech Audio Process. 8 (2000), no. 5, 508-518. https://doi.org/10.1109/89.861368
- K. Kumar, R. Pandey, M. L. N. S. Karthik, S. S. Bhattacharjee, and N. V. George, Robust and sparsity-aware adaptive filters: A Review, Signal Process. 189 (2021). https://doi.org/10.1016/j. sigpro.2021.108276
- S. S. Bhattacharjee, D. Ray, and N. V. George, Adaptive modified versoria zero attraction least mean square algorithms, IEEE Trans. Circ. Syst. II: Express Briefs 67 (2020), no. 12, 3602-3606.
- M. N. S. Jahromi, M. S. Salman, A. Hocanin, and O. Kukrer, Mean-square deviation analysis of the zero-attracting variable step-size LMS algorithm, Signal, Image Video Process. 11 (2017r), no. 3, 533-540. https://doi.org/10.1007/s11760-016-0991-5
- Y. Chen, Y. Gu, and A. O. Hero, Sparse LMS for system identification, (IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan), 2009, pp. 3125-3128.
- O. Taheri and S. A. Vorobyov, Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis, Signal Process. 104 (2014), 70-79.
- X. Cao, F. Tong, B. Li, and S. Zheng, Experimental evaluation of norm constraint sparsity exploitation for shallow water acoustic communication, Appl. Acoust. 180 (2021), 108111.
- C. Wang, Y. Zhang, Y. Wei, and N. Li, A new l0-LMS algorithm with adaptive zero attractor, IEEE Commun. Lett. 19 (2015), no. 12, 2150-2153. https://doi.org/10.1109/LCOMM.2015.2490665
- F. Y. Wu, Y. H. Zhou, F. Tong, and R. Kastner, Simplified p-norm-like constraint LMS algorithm for efficient estimation of underwater acoustic channels, J. Mar. Sci. Appl. 12 (2013), no. 2, 228-234. https://doi.org/10.1007/s11804-013-1189-7
- Y. Tian, X. Han, J. Yin, H. Chen, and Q. Liu, An improved least mean square/fourth direct adaptive equalizer for under-water acoustic communications in the Arctic, Acta Oceanol. Sin. 39 (2020), no. 9, 133-139.
- D. Kari, I. Marivani, F. Khan, M. O. Sayin, and S. S. Kozat, Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis, Digit. Signal Process. A Rev. J. 68 (2017), 57-68. https://doi.org/10.1016/j.dsp.2017.05.006
- S. Zhou, N. Xiu, Y. Wang, L. Kong, and H. Qi, A null-spacebased weighted l1 minimization approach to compressed sensing, Inf. Infer. 5 (2016), no. 1, 76-102.
- A. Al-Shabili, L. Weruaga, and S. Jimaa, Adaptive sparsity tradeoff for ℓ1-constraint NLMS algorithm, (IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China), 2016, pp. 4707-4711.
- Y. Li, Y. Wang, and T. Jiang, Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation, AEU - Int. J. Electron. Commun. 70 (2016), no. 7, 895-902. https://doi.org/10.1016/j.aeue.2016.04.001
- Y. Wang and Y. Li, Sparse multipath channel estimation using norm combination constrained set-membership NLMS algorithms, Wirel. Commun. Mob. Comput. 2017 (2017). https://doi.org/10.1155/2017/8140702
- S. Zhang and J. Zhang, Set-membership NLMS algorithm with robust error bound, IEEE Trans. Circ. Syst. II: Express Briefs 61 (2014), no. 7, 536-540.
- R. Pogula, T. K. Kumar, and F. Albu, Robust sparse normalized LMAT algorithms for adaptive system identification under impulsive noise environments, Circ. Syst. Signal Process. 38 (2019), no. 11, 5103-5134. https://doi.org/10.1007/s00034-019-01111-3
- C. H. Lee, B. D. Rao, and H. Garudadri, Proportionate adaptive filtering algorithms derived using an iterative reweighting framework, IEEE/ACM Trans. Audio Speech Lang. Process. 29 (2021), 171-186. https://doi.org/10.1109/TASLP.2020.3038526
- K. Pelekanakis and M. Chitre, New sparse adaptive algorithms based on the natural gradient and the L0-norm, IEEE J. Ocean. Eng. 38 (2013), no. 2, 323-332. https://doi.org/10.1109/JOE.2012.2221811
- Z. Jin, Y. Li, and Y. Wang, An enhanced set-membership PNLMS algorithm with a correntropy induced metric constraint for acoustic channel estimation, Entropy 19 (2017), no. 6. https://doi.org/10.3390/e19060281
- I. Hassani, M. Arezki, and A. Benallal, A novel set membership fast NLMS algorithm for acoustic echo cancellation, Appl. Acoust. 163 (2020). https://doi.org/10.1016/j.apacoust.2020.107210
- Y. Li, Y. Wang, and L. Sun, A flexible sparse set-membership NLMS algorithm for multi-path and acoustic echo channel estimations, Appl. Acoust. 148 (2019), 390-398. https://doi.org/10.1016/j.apacoust.2019.01.002
- J. Lim, H. Pang, and K. Lee, Time delay estimation based on log-sum and l p -norm penalized minor component analysis, J. Acoust. Soc. Am. 143 (2018), no. 6, 3979-3984. https://doi.org/10.1121/1.5042353
- A. Kumar and P. Kumar, Underwater acoustic channel estimation via basic-CS and modified-CS using 2-D frequency sampling, (IEEE International Conference on Advanced Communication Technologies and Signal Processing), 2020, pp. 1-6.
- O. Macchi, N. Bershad, and M. Mboup, Steady-state superiority of lms over ls for time-varying line enhancer in noisy environment, IEE Proc. F Radar Signal Process. 138 (1991), no. 4, 354-360. https://doi.org/10.1049/ip-f-2.1991.0046
- L. Horowitz and K. Senne, Performance advantage of complex LMS for controlling narrow-band adaptive arrays, IEEE Trans. Acoust. Speech Signal Process. 29 (1981), no. 3.
- Y. Li and M. Hamamura, An improved proportionate normalized least-mean-square algorithm for broadband multipath channel estimation, The Sci. World J. 2014 (2014), 1-9.
- M. S. Ahmed, N. S. Mohd Shah, Y. Y. Al-Aboosi, M. S. M. Gismalla, M. F. L. Abdullah, Y. A. Jawhar, and M. Balfaqih, Filter orthogonal frequency-division multiplexing scheme based on polar code in underwater acoustic communication with nonGaussian distribution noise, ETRI J. 43 (2021), no. 2, 184-196. https://doi.org/10.4218/etrij.2019-0564
- A. Kumar and P. Kumar, Pilot-assisted maximum-likelihood estimation for underwater acoustic communication, IEEE Int. Conf. Comput. Commun. Secur. 1 (2020), 5-10.
- Y. Zhang, R. Venkatesan, O. A. Dobre, and C. Li, Efficient estimation and prediction for sparse time-varying underwater acoustic channels, IEEE J. Ocean. Eng. 45 (2020), no. 3, 1112-1125. https://doi.org/10.1109/JOE.2019.2911446