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A New Method for Detection of Discrete Data Transmitted over Non-Linear Dynamic Wireless Channels | ||
پدافند الکترونیکی و سایبری | ||
Article 6, Volume 3, Issue 2, January 2020, Pages 75-85 PDF (1.16 M) | ||
Author | ||
Mohammad Hassan Majidi* | ||
Assistant Professor, Faculty of Electrical and Computer Engineering, Birjand University, Birjand, Iran | ||
Receive Date: 11 July 2015, Revise Date: 21 June 2023, Accept Date: 19 September 2018 | ||
Abstract | ||
In this paper, channel estimation and data detection under non linear time-varying channel are investigated. The model of non linear time varying channel that we focused on is known as switching state space model (SSSM). This model combines the hidden Markov model (HMM) and the linear state space model (LSSM). In this paper based on the EM approach, we propose a new iterative method for joint data detection and channel estimation. Monte Carlo simulations show that the bit error rate (BER) of the proposed scheme is close to BER of the Viterbi algorithm (VA) with perfect channel state information (CSI). | ||
Keywords | ||
Switching state space mode (SSSM); The EM approach; Joint channel and data detection; The Viterbi algorithm; Per survivor processing (PSP) Technique | ||
References | ||
[1] G. D. Forney, “The Viterbi algorithm,” IEEE, vol. 61, pp. 268–278, 1973. [2] L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal Decoding of Linear Codes for minimizing symbol error rate,” IEEE T Inform Theory, vol. 20, pp. 284–287,March 1974. [3] R. Raheli, A. Polydoros, and C. Tzou, “Per-Survivor Processing: A General Approach to MLSE in Uncertain Environments,” IEEE T Commun., vol. 43, pp.354–364, 1975. [4] S. Haykin, “Adaptive Filter Theory,” Prentice Hall, fifth edition, 2013. [5] P. Diniz, “Adaptive Filtering: Algorithms and Practical Implementation,” Springer Science & Business Media, 2012. [6] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Trans. ASME, Journal of Basic Engineering, vol. 82, pp. 35-45, 1960. [7] Z. Zhu and H. Sadjadpour, “An adaptive per-survivor processing algorithm,” IEEE T Commun., vol. 50, pp. 1716-1718, November 2002. [8] M. H. Majidi, M. Pourmir, and S. M. S. Sadough, “Kalman Filter-Based Discrete Data Estimation for Linear Dynamic Wireless Channels,” Proc. 3rd International Conference on Computer and Knowledge Engineering (ICCKE 2013), pp. 380-383, Oct. 31- Nov. 1 2013. [9] Z. Ghahramani and G. E. Hinton, “Switching State-Space Models,” Tech. Rep., King’s College Road, Toronto M5S3H5, 1996. [10] Z. Ghahramani and G. E. Hinton, “Variational Learning for Switching State-Space Models,” Neural Comput 12(4), pp. 831–864, 2000. [11] C. A. Popescu and Y. S. Wong, “Nested Monte Carlo EM Algorithm for Switching State-Space Models,” IEEE T Knowl Data En, vol. 17, no. 12, Dec. 2005. [12] H. Lu , D. Zeng and H. Chen, “Prospective Infectious Disease Outbreak Detection Using Markov Switching Models.” IEEE T Knowl Data En, vol. 22, no. 24, Dec. 2010. [13] S. Saha and G. Hendeby, “Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems, 2014 IEEE Workshop on Statistical Signal Processing, pp. 272-275, July 2014. [14] J. Kalawoun, P. Pamphile, G. Celeux, K. Biletska, and M. Montaru, “Estimation of the battery state of charge: a switching Markov state-space model,” EUSIPCO'2015, Nice, France, Aug. 2015. [15] A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. R. Statist. Soc., vol. 76, pp. 341-353, 1977. [16] S. Julier and J. Uhlmann, “Unscented filtering and nonlinear estimation,” P IEEE, vol. 92, pp.401–422, Mar.2004. [17] P. M. Djuric, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J. Miguez, “Particle filtering,” IEEE SIGNAL PROC MAG., vol. 20, pp. 19–38, Sept. 2003. [18] Y. Li and X. Huang, “The simulation of independent Rayleigh faders,” IEEE T COMMUN, vol. 50, no. 9, pp. 1503-1514, 2002. [19] H. Wang and P. Chang, “On verifying the first order Markovian assumption for a Rayleigh fading channel model,” IEEE T VEH TECHNOL, vol. 45, no. 2, pp. 353-357, May,1996. [20] K. E. Baddour and N. C. Beaulieu, “Autoregressive models for fading channel simulation,” In Proceedings of the IEEE Global Telecommunications Conference, pp. 1187-1192, Nov. 2001. [21] G. L. Stuber, Principles of Mobile Communications, Springer; 3rd edition, 2012. [22] M. H. Majidi. “Bayesian estimation of discrete signals with local dependencies,” Ph.D. Thesis. Supélec, France, June 2014. | ||
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