Recurrent neural network based narrowband channel prediction

IEEE Vehicular Technology Conference

Volumn 5, Issue , 2006, Pages 2173-2177

  Liu, Wei ^a   Yang, Lie Liang ^a   Hanzo, Lajos ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; EXTENDED KALMAN FILTERS; REAL TIME SYSTEMS; RECURRENT NEURAL NETWORKS;

CHANNEL PREDICTORS; FULLY CONNECTED RECURRENT NEURAL NETWORKS (FCRNN); GLOBAL EXTENDED KALMAN FILTER (GEKF); REAL TIME RECURRENT LEARNING (RTRL);

CHANNEL ESTIMATION;

EID: 34047152766     PISSN: 15502252     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. L. Hanzo, C. H. Wong and M. S. Yee "Adaptive wireless transceivers: turbo-coded, turbo-equalized and space-time coded TDMA, CDMA, and OFDM Systems," John Wiley & Sons - IEEE Press, 2002.
2. L. Hanzo, M. Münster and B. J. Choi and T. Keller "OFDM and MC-CDMA for broadband multi-user communications, WLANs and broadcasting," John Wiley & Sons - IEEE Press, 2003.
3. A. Duel-Hallen, S. Hu and H. Hallen, "Long range prediction of fading signals: enabling adaptive transmission for mobile radio channels," IEEE Signal Processing Magzine, vol. 17, pp. 62-75, May 2000.
4. J. K. Huang and J. H. Winters, "Sinusoidal modeling and prediction of fast fading processes," Global Telecommunications Conference, pp. 892-897, November 1998.
5. J. B. Andersen, J. Jensen, S. H. Jensen and F. Frederiksen, "Prediction of future fading based on past measurements," Proceedings of IEEE Vehicular Technology Conference, pp. 151-155, September 1999.
6. R. Steele and L. Hanzo "Mobile Radio Communications: Second and Third-Generation Cellular and WATM Systems," John Wiley & Sons-IEEE Press, 1999.
7. B. Visweswaran and T. Kiran, "Channel prediction based power control in W-CDMA systems," First International Conference on 3G Mobile Communication Technologies, pp. 41-45, March 2000.
8. X. M. Gao, J. M. A. Tanskanen and S. J. Ovaska, "Comparison of linear and neural network-based power prediction schemes for mobile DS/CDMA systems," Proceedings of IEEE Vehicular Technology Conference, pp. 61-65, April 1996.
9. X. M. Gao,X. Z. Gao, J. M. A. Tanskanen and S. J Ovaska, "Power prediction in mobile communication systems using an optimal neural-network structure," IEEE Transactions on Neural Networks, vol. 8, pp. 1446-1455, November 1997.
10. W. C. Jakes "Microwave mobile communications," Wiley, 1974.
11. S. Hay kin, "Neural networks: a comprehensive foundation," Prentice Hall, Inc, 1999.
12. D. P. Mandic and J. A. Chambers, "Recurrent neural networks for prediction: learning algorithms, architectures and stability," John Willy & Sons, 2001.
13. G. Kechriotis and E. S. Manolakos, "Training fully recurrent neural networks with complex weights," IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 41, pp. 235-238, March 1994.
14. G. Kechriotis, H. Zervas and H. S. Manolakos, "Using recurrent neural networks for adaptive communication channel equalization," IEEE Transactions on Neural Networks, vol. 5, pp. 267-278, March 1994.
15. G. V. Puskorius and L. A. Feldkamp, "Decoupled extended Kalman filter training of feedforward layered networks," International Joint Conference on Neural Networks, pp. 771-777, July 1991.
16. G. V. Puskorius and L. A. Feldkamp, "A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification," Proceedings of the IEEE, pp. 2259-2277, November 1998.
17. N. Benvenuto, M. Marchesi, F. Piazza and A. Uncini, "A Comparison between real and complex valued neural networks in communication applications," Proceedings of the 1991 Internal Conference on Neural Networks, pp. 1177-1180, June 1991.
18. R. J. Williams and D. Zipser, "A Learning Algorithm for Continually Running Fully Recurrent Neural Networks," Neural Computation, vol. 1, pp. 270-280, March 1989.
19. S. Haykin, "Kalman filtering and neural networks," John Wiley & Sons, 2001.
20. S. Singhal and L. Wu, "Training multilayer perceptions with the extended Kalman algorithm," Advances in Neural Information Processing Systems 7, pp. 133-140, 1989.
21. L. A. Feldkamp and G. V. Puskorius, "Training of robust neural controllers," IEEE Conference on Decision and Control, pp. 2754-2759, December 1994.
22. G. V. Puskorius and L. A. Feldkamp, "Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks," IEEE Transactions on Neural Networks, vol. 5, pp. 279-297, March 1994.
23. J. Choi, A. C. Lima and S. Haykin, "Kalman filter-trained recurrent neural equalizers for time-varying channels," IEEE Transactions on Communications, vol. 53, pp. 472-480, March 2005.
24. J. Choi, M. Bouchard and T. H. Yeap, "Decision feedback recurrent neural equalization with fast convergence rate," IEEE Transactions on Neural Networks, vol. 16, pp. 699-708, May 2005.
25. P. H. G. Coelho, "A complex EKF-RTRL neural network," International Joint Conference on Neural Networks, pp. 120-125, July 2001.
26. P. H. G. Coelho, "Adaptive channel equalization using EKF-CRTRL neural networks." International Joint Conference on Neural Networks, pp. 1195-1199, May 2002.