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Nawaz, Syed Junaid; I. Tiwana, Moazzam; N. Patwary, Mohammad; M. Khan, Noor; I. Tiwana, Mohsin; Haseeb, Abdul (2017)
Publisher: ktu.edu
Types: Article
Subjects: Channel estimation, genetic algorithms, superimposed training, channel.
This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] S. Bernard, Digital communications. NJ: Prentice Hall, 2001.
    • [2] W. U. Bajwa, J. Haupt, A. M. Sayeed, R. Nowak, “Compressed channel sensing: A new approach to estimating sparse multipath - c-hann+els”, in Proc. IEEE, vol. 98, no. 6, 2010, pp. 1058-1076. [Online]. Available: http://dx.doi.org/10.1109/JPROC.2010.2042415
    • [3] J. K. Tugnait, W. Luo, “On channel estimation using superimposed training and first -order statistics”, IEEE Commun. Letters, vol. 7, no. 9, pp. 413-415, 2003. [Online]. Available: http://dx.doi.org/ 10.1109/LCOMM.2003.817325
    • [4] R. Carrasco-Alvarez, R. Parra-Michel, A. Orozco-Lugo, J. Tugnait, “Time-varying channel estimation using two dimensional channel orthogonalization and superimposed training”, IEEE Trans. on Signal Process., vol. 60, no. 8, pp. 4439-4443, 2012. [Online]. Available: http://dx.doi.org/10.1109/TSP.2012.2195658
    • [5] L. He, Y.-C. Wu, S. Ma, T.-S. Ng, H. Poor, “Superimposed trainingbased channel estimation and data detection for OFDM amplify-andforward cooperative systems under high mobility”, IEEE Trans. Signal Process., vol. 60, no. 1, pp. 274-284, 2012. [Online]. Available: http://dx.doi.org/10.1109/TSP.2011.2169059
    • [6] A. Orozco-Lugo, M. Lara, D. McLernon, “Channel estimation using implicit training”, IEEE Trans. Signal Process., vol. 52, no. 1, pp. 240-254, 2004. [Online]. Available: http://dx.doi.org/10.1109/ TSP.2003.819993
    • [7] J. Tugnait, X. Meng, “On superimposed training for channel estimation: performance analysis, training power allocation, and frame synchronization”, IEEE Trans. Signal Process., vol. 54, no. 2, pp. 752-765, 2006. [Online]. Available: http://dx.doi.org/10.1109/ TSP.2005.861749
    • [8] K. Yen, L. Hanzo, “Genetic algorithm assisted joint multiuser symbol detection and fading channel estimation for synchronous CDMA systems”, IEEE J. on Sel. Areas in Commun., vol. 19, no. 6, pp. 985- 998, 2001. [Online]. Available: http://dx.doi.org/10.1109/49.926355
    • [9] H. Ali, A. Doucet, D. I. Amshah, “GSR: A new genetic algorithm for improving source and channel estimates”, IEEE Trans. on Circuits and Sys., vol. 54, no. 5, pp. 1088-1098, 2007. [Online]. Available: http://dx.doi.org/10.1109/TCSI.2007.893507
    • [10] G. Routraya, P. Kanungo, “Genetic algorithm based RNN structure for Rayleigh fading MIMO channel estimation”, in Proc. Engineering, vol. 30, 2012, pp. 77-84.
    • [11] K. Yen, L. Hanzo, “Genetic-algorithm-assisted multiuser detection in asynchronous CDMA communications”, IEEE Trans. on Veh. Technol., vol. 53, no. 5, pp. 1413-1422, 2004.
    • [12] S. Chen, Y. Wu, “Maximum likelihood joint channel and data estimation using genetic algorithms”, IEEE Trans. on Signal Process., vol. 46, no. 5, pp. 1469-1473, 1998. [Online]. Available: http://dx.doi.org/10.1109/78.668813
    • [13] M. Jiang, J. Akhtman, L. Hanzo, “Iterative joint channel estimation and multi-user detection for multiple-antenna aided OFDM systems”, IEEE Trans. on Wireless Commun., vol. 6, no. 8, pp. 2904-2914, 2007. [Online]. Available: http://dx.doi.org/10.1109/TWC. 2007.05817
    • [14] S. J. Nawaz, K. I. Ahmed, M. N. Patwary, N. M. Khan, “Superimposed training-based compressed sensing of sparse multipath channels”, IET Communications, vol. 6, no. 18, pp. 3150- 3156, 2012. [Online]. Available: http://dx.doi.org/10.1049/ietcom.2012.0162
    • [15] Z. Jun-yi, M. Wei-xiao, J. Shi-lou, “Sparse underwater acoustic OFDM channel estimation based on superimposed training”, J. of Marine Science and Appl., vol. 8, no. 1, pp. 65-70, 2009. [Online]. Available: http://dx.doi.org/10.1007/s11804-009-8015-2
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