Adaptive sparse system identification using normalized least mean fourth algorithm

International Journal of Communication Systems

Volumn 28, Issue 1, 2015, Pages 38-48

  Gui, Guan ^a   Adachi, Fumiyiuki ^a  

^a TOHOKU UNIVERSITY   (Japan)

Author keywords

Adaptive sparse system identifications (ASSI); Adaptive system identifications (ASI); Least mean fourth (LMF); Least mean square (LMS); Sparse penalty

Indexed keywords

ADAPTIVE SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); RELIGIOUS BUILDINGS; SIGNAL TO NOISE RATIO;

LEAST MEAN FOURTH; LEAST MEAN SQUARE (LMS); LOW SIGNAL-TO-NOISE RATIO; NORMALIZED LEAST MEAN FOURTH ALGORITHMS; NORMALIZED LEAST MEAN SQUARE; SPARSE PENALTY; SPARSE SYSTEM IDENTIFICATION; STRUCTURE INFORMATION;

ALGORITHMS;

EID: 84916215712     PISSN: 10745351     EISSN: 10991131     Source Type: Journal    
DOI: 10.1002/dac.2637     Document Type: Article

References (23)

1. Gui G, Peng W, Adachi F. Improved Adaptive Sparse Channel Estimation Based on the Least Mean Square Algorithm. IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, April 7-10, 2013; 3130-3134.
2. Gui G, Peng W, Adachi F. Adaptive system identification using robust LMS/F algorithm. International Journal of Communication Systems (Wiley) 2013; 1-8. DOI: 10.1002/dac.2517.
3. Deng H, Doroslovacki M. Proportionate adaptive algorithms for network echo cancellation. IEEE Transactions on Signal Processing 2006; 54(5):1794-1803.
4. Widrow B, Stearns D. Adaptive Signal Processing, no. 4. Prentice Hall: New Jersey, 1985.
5. Gui G, Peng W, Adachi F. Sub-nyquist rate ADC sampling-based compressive channel estimation. Wireless Communication and Mobile Computing 2013; 1-10. DOl: 10.1002/wcm2372.
6. Gui G, Peng W, Adachi F. High-resolution compressive channel estimation for broadband wireless communication systems. International Journal of Communication Systems (WILEY) 2012; 2012:1-12. DOI: 10.1002/dac.2483.
7. Gui G, Mehbodniya A, Adachi F. Bayesian Sparse Channel Estimation and Data Detection for OFDM Communication Systems. IEEE 78th Vehicular Technology Conference (VTC-Fall), Las Vegas, USA, 2-5 Sept. 2013.
8. Ba M. Compressive sensing for ultra-wideband channel estimation: on the sparsity assumption of ultra-wideband channels. International Journal of Communication Systems (WILEY) 2013. Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/dac.2548.
9. Eweda E. Global stabilization of the least mean fourth algorithm. IEEE Transactions on Signal Processing 2012; 60(3):1473-1477.
10. Walach E, Widrow B. The least mean fourth (LMF) adaptive algorithm and its family. IEEE Transactions on Information Theory 1984; 30(2):275-283.
11. Eweda E, Zerguine A. New insights into the normalization of the least mean fourth algorithm. Signal, Image and Video Processing 2011; 7(2):255-262.
12. Kermoal JP, Schumacher L, Pedersen KI, Mogensen PE, Frederiksen F. A stochastic MIMO radio channel model with experimental validation. IEEE Journal on Selected Areas in Communications 2002; 20(6):1211-1226.
13. Vuokko L, Kolmonen V-M, Salo J, Vainikainen P. Measurement of large-scale cluster power characteristics for geometric channel models. IEEE Transactions on Antennas and Propagation 2007; 55(11):3361-3365.
14. Gui G, Adachi F. Sparse LMF algorithm for adaptive channel estimation in low SNR region. International Journal of Communication Systems (Wiely) 2013; 1-10. DOl: 10.1002/dac.253.
15. Deng H, Member S. Improving convergence of the PNLMS algorithm for sparse impulse response identification. IEEE Signal Processing Letters 2005; 12(3):181-184.
16. Chen Y, Gu Y, Hero, III AO. Sparse LMS for System Identification. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), no. 3, 2009; 3125-3128.
17. Gui G, Mehbodniya A, Adachi F. Adaptive Sparse Channel Estimation Using re-Weighted Zero-Attracting Normalized Least Mean Fourth. 2nd IEEE/CIC International Conference on Communications in China (ICCC), Xian, China, Aug.12-14 2013.
18. Gui G, Mehbodniya A, Adachi F. Adaptive Sparse Channel Estimation for Time-Variant MIMO Communication Systems. IEEE 78th Vehicular Technology Conference (VTC-Fall), Las Vegas, USA, 2-5 Sept. 2013; 1-5.
19. Gui G, Mehbodniya A, Adachi F. Least Mean Square/Fourth Algorithm for Adaptive Sparse Channel Estimation. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, UK, Sept. 8-11 2013; 1-5.
20. Donoho DL. Compressed sensing. IEEE Transactions on Information Theory 2006; 52(4):1289-1306.
21. Su G, Jin J, Gu Y, Wang J. Performance analysis of L0-norm constraint least mean square algorithm. IEEE Transactions on Signal Proecessing 2012; 60(5):2223-2235.
22. Gui G, Adachi F. Improved least mean square algorithm with application to adaptive sparse channel estimation. EURASIP Journal on Wireless Communications and Networking 2013; 2013(204):1-18. DOl: 10.1186/1687-1499-2013-204
23. Huang Z, Xiang D, Huang A, Gui G, Adachi F. Regularization Selection Method for LMS-Type Sparse Multipath Channel Estimation. The 19th Asia-Pacific Conference on Communications (APCC), Bali Island, Indonesia, August 29-31, 2013; 1-5.