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.
📌 3 Key Takeaways
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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Stochastic processes · hidden Markov model · WiMAX
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.
KORDNOORI, S., MOSTAFAEI, H., BEHZADI, M. H. (2019). EVALUATION OF THE ERROR PERFORMANCE OF THE IEEE802.16 STANDARD BASED ON A HIDDEN MARKOV MODEL. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 37 - Sayı: 4, 1480–1496. https://ajindex.com/articles/419140
A. Tripathi, V. Anand and A. K. Jain, (2016) “1st International Conference on Innovation and Challenges in Cyber Security
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
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
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
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
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
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
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
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
N. Raman and S. J. Maybank,( 2016) “Activity recognition using a supervised non-parametric hierarchical HMM,” Neurocomputing, vol. 199, no. 30, pp. 163-177
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
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
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
H. Uguz and A. Arslan,( 2010) “A new approach based on discrete Hidden Markov Model using Rocchio algorithm for the di