EVALUATION OF THE ERROR PERFORMANCE OF THE IEEE802.16 STANDARD BASED ON A HIDDEN MARKOV MODEL

Year 2019, Volume: 37 Issue: 4, 1480 - 1496, 01.12.2019

Shaghayegh Kordnoorı Hamidreza Mostafaeı Mohammad Hassan Behzadı

Abstract

IEEE802.16 (WiMAX) is a standard that supports high data rate in a wide area with multi-traffic communication, low implementation and the possibility of creating broadcast, multicast and mesh networks. In this paper the Hidden Markov Model as a Discrete Channel Model has been employed to model the burst errors generated from IEEE 802.16/WiMAX; moreover, the precise Hidden Markov Models using Baum-Welch Algorithm have been obtained by estimating the optimal order of these models with comparing statistics such as Average log-likelihood, Probability of Error, P(0^m |1) and Auto-Correlation function. Additionally, the parameters of the best models have been derived. The impacts of a number of Baum-Welch Algorithm iterations and the modulation order on the optimal order estimation with respect to different (T_s) were investigated using extensive simulations.

Keywords

Stochastic processes, hidden Markov model, WiMAX, error analysis, OFDM, parameter estimation.

References

• [1] A. Tripathi , V. Anand and A. K. Jain, (2016) “1st International Conference on Innovation and Challenges in Cyber Security”
• [2] S. J. Shubhangi and A. P. Laturkar,( 2014) “Ber improvement in OFDM using coding techniques,” J. Electrical Electron. Eng. Res., vol. 2, no. 3, pp. 167-173.
• [3] V. Savaux, A. Skrzypczak and Y. Louet,( 2016) “Theoretical bit error floor analysis of 16-QAM OFDM signal with channel estimation using polynomial interpolation,” IET Signal Processing, vol. 10, no. 3, pp. 254-265.
• [4] L. E. Baum and T. Petrie,( 1966) “Statistical Inference for Probabilistic Functions of Finite State Markov Chains,” Ann. Math. Stat., vol. 37, no. 6, pp. 1554-1563.
• [5] J. H. Alkhateeb, J. Ren, J. Jiang and H.,( 2011) “Al-Muhtaseb Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking,” PATTERN RECOGN LETT, vol. 32, no. 8, pp. 1081-1088.
• [6] C. Champion and S.M. Houghton,( 2016) “Application of continuous state Hidden Markov Models to a classical problem in speech recognition,” COMPUT SPEECH LANG, vol. 36, no. 7, pp. 347-364.
• [7] C. Wu, L. Lee, D. Tao,( 2007) “An HMM prediction and throttling-based call admission control scheme for wireless multimedia networks,” J COMPUT MATH APPL, vol. 54, no. 3, pp. 364-378.
• [8] MD. Osman Gani, H. Sarwar and C.M. Rahman,( 2009) “Prediction of state of wireless network using Markov and Hidden Markov Model,” JNW, vol. 4, no. 10, pp. 976-984.
• [9] B. Brejova, D. G. Brown and T. Vinar, (2007) “The most probable annotation problem in HMMs and its application to bioinformatics,” J. Comput. Syst. Sci, vol. 73, no. 7, pp. 1060-1077.
• [10] N. Raman and S. J. Maybank,( 2016) “Activity recognition using a supervised non-parametric hierarchical HMM,” Neurocomputing, vol. 199, no. 30, pp. 163-177.
• [11] Md. Rafiul Hassan , Ramamohanararao K., Kamruzzaman J. Rahman M. and Maruf Hossain M.,( 2013) “A HMM-based adaptive fuzzy inference system for stock market forecasting,” Neurocomputing, vol. 104, pp. 10-25.
• [12] M. Kim, D. Kim and S. Lee, (2003) “Face recognition using the embedded HMM with second order block-specific observations,” Pattern Recognition, vol. 36, no. 11, pp. 2723-2735.
• [13] S. Lee, H. Lee and B. Youn,( 2012) “Modelling and analyzing technology innovation in the energy sector: patent-based HMM approach,” COMPUT IND ENG Engineering, vol. 63, no. 3, pp. 564- 577.
• [14] H. Uguz and A. Arslan,( 2010) “A new approach based on discrete Hidden Markov Model using Rocchio algorithm for the diagnosis of the brain diseases,” Digital Signal Processing, vol. 20, no. 3, pp. 923-934.
• [15] H. A. Tai, W. Ching and L. Y. Chan,( 2009) “Detection of machine failure: Hidden Markov Model approach,” COMPUT IND ENG, vol. 57, no. 2, pp. 608-619.
• [16] E. L. Sonnhammer, G. Von Heijne and A. Krogh, (1998) “A hidden Markov model for predicting transmembrane helices in protein sequences,” in Proc. Int. Conf. on Intelligent systems for Molecular Biology.
• [17] C.V., Verikoukis,( 2004) “An adaptive hidden markov model for indoor OFDM based wireless systems,” JTIT, vol. 2, pp. 61-65.
• [18] J. Poikonen, J. Ojaniemi, K. Nybom, D. Gomez Barquero and T. Jokela,( 2011) “A computationally efficient simulation chain for OFDM-based wireless systems,” IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.
• [19] C. Carlemalm, A. Logothetis and H.V. Poor, (2000) “Channel estimation and demodulation of asynchronous CDMA signals in frequency selection fading channels,” in 10th European Signal Processing.
• [20] E. Khan and D. T. M. Slock,( 2002) “Iterative receiver for synchronous CDMA using hidden Markov model,” in The 13th IEEE International Symposium on Indoor and Mobile Radio Communications.
• [21] A. Beverly, K. Sam Shanmugan, (1998) “Hidden Markov Models for burst errors in GSM and DECT channels,” in Global Telecommunications Conference.
• [22] E. N. Gilbert, (1960) “Capacity of a burst-noise channel,” Bell Syst. Tech. J., vol. 39, no. 9, pp. 1253-1265.
• [23] E. O. Elliott (1963) “Estimates of error rates for codes on burst-noise channels,” Bell Syst. Tech. J., vol. 42, no. 5, pp. 1977-1997.
• [24] B. D. Fritchma,( 1967) “A binary channel characterization using partitioned Markov chains,” IEEE Trans. Inf. Theory,IT, vol. 13, no. 2, pp. 221-227.
• [25] J. Garcia Frias and P. M. Crespo, (1997) “Hidden markov models for burst error characterization in indoor radio channels,” IEEE Transactions on Vehicular Technology, vol. 46, no. 4, pp. 1006-1020.
• [26] O. S. Salih, C. Wang, D. I. Lauren and H. Yejun,( 2009) “Hidden markov models for packet-level errors in bursty digital wireless channels,” in IEEE Loughborough Antennas and Propagation Conference.
• [27] S. G. Srinivasa, P. Lee and S. W. Mclavghlin,( 2007) “Post-Error correcting code modelling of burst channels using hidden markov models with applications to magnetic recording,” IEEE Trans. Magn, vol. 43, no. 22.
• [28] S. Sivaprakasam and K. S. Shanmugan,(1994) “A forward-only real time modified hidden markov modeling algorithm for tracking bursty digital channels,” in Fifth IEEE international workshop on computer-aided modeling analysis and design of communication link and networks.
• [29] W.Turin, M. M.,Sondhi, (1993) “Modeling error sources in digital channels,” IEEE J. Sel. Area. Co, vol. 11, no. 3, pp. 340-347.
• [30] W.Turin,( 1998) “Simulation of error source in digital channels,” IEEE J. Sel. Area., vol. 6, no. 1, pp. 85-93.
• [31] W.Turin,R., Nobelen, (1998) “Hidden markov modeling of fading channels,” IEEE J. Sel. Area. Co, vol. 16, no. 9, pp. 1809-1817.
• [32] S. Khudanpur and P. Narayan, (2002) “Order estimation for a special class of hidden markov sources and binary renewal processes,” IEEE Trans. Inf. Theory, vol. 48, no. 6, pp. 1704-1713.
• [33] C. C. Liu and P. Narayan,( 1994) “Order Estimation and Sequential Universal Data Compression of a Hidden Markov Source by the Method of Mixtures,” 18- IEEE Trans. Inf. Theory, vol. 40, no. 4, pp. 1167-1180.
• [34] R. Kwan and C. Leung,( 2005) “An HMM approach to adaptive modulation and coding with multicodes for fading channels,” in Canadian Conference on Electrical and Computer Engineering.
• [35] P. Ji, B. Liu, D. Towsley and J. Kurose,(2002) “Modeling frame-level errors in GSM wireless channels,” in Global Telecommunications Conference.
• [36] I. A. Akbar and W. H. Tranter,( 2007) “An improved estimator of the finite population mean in simple random sampling,” in IEEE SoutheastCon.
• [37] P. Tavde, A. Dubey and K.D. Kulat,(2012) “Performance analysis of OFDM system [case study of optimize IFFT size for M-PSK technique],” Int. j. eng. res. appl., vol. 2, no. 3, pp. 2550-2556.