Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Shirin Kordnoori, Hamidreza Mostafaei, Shaghayegh Kordnoori, Mohammad Mohsen Ostadrahimi

Abstract


CDMA is an important and basic part of today’s communications technologies. This technology can be analyzed efficiently by reducing the time, computation burden, and cost by characterizing the physical layer with a Markov Model. Waveform level simulation is generally used for simulating different parts of a digital communication system. In this paper, we introduce two different mathematical methods to model digital communication channels. Hidden Markov and Semi Hidden Markov models’ applications have been investigated for evaluating the DS-CDMA link performance with different parameters. Hidden Markov Models have been a powerful mathematical tool that can be applied as models of discrete-time series in many fields successfully. A semi-hidden Markov model as a stochastic process is a modification of hidden Markov models with states that are no longer unobservable and less hidden. A principal characteristic of this mathematical model is statistical inertia, which admits the generation, and analysis of observation symbol contains frequent runs. The SHMMs cause a substantial reduction in the model parameter set. Therefore in most cases, these models are computationally more efficient models compared to HMMs. After 30 iterations for different Number of Interferers, all parameters have been estimated as the likelihood become constant by the Baum Welch algorithm. It has been demonstrated that by employing these two models for different Numbers of Interferers and Number of symbols, Error sequences can be generated, which are statistically the same as the sequences derived from the CDMA simulation. An excellent match confirms both models’ reliability to those of the underlying CDMA-based physical layer.

Keywords


Hidden Markov Model; Semi Hidden Markov Model; CDMA; Error Sequence; Baum Welch

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References


Sharma U, Maheshkar S, Mishra AN, Kaushik R. Visual Speech Recognition Using Optical Flow and Hidden Markov Model. Wireless Personal Communications 2019 jun;106(4):2129–2147. https://link.springer.com/article/10.1007/s11277-018-5930-z.

Siregar B, Tarigan AJ, Nasution S, Andayani U, Fahmi F. Speech Recognition with Hidden Markov Model and Multisensory Methods in Learning Applications Intended to Help Dyslexic Children. Journal of Physics: Conference Series 2019 jul;1235(1):1–9.

Lakin SM, Kuhnle A, Alipanahi B, Noyes NR, Dean C, Muggli M, et al. Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences. Communications Biology 2019 dec;2(1):1–11. https://doi.org/10.1038/s42003-019-0545-9.

Ghotkar A, Vidap P, Deo K. Dynamic Hand Gesture Recognition using Hidden Markov Model by Microsoft Kinect Sensor. International Journal of Computer Applications 2016 sep;150(5):5–9.

Thi NAN, Yang HJ, Kim SH, Kim SH. Combining Dynamic Time Warping and Single Hidden Layer Feedforward Neural Networks for Temporal Sign Language Recognition. International Journal of Contents 2011;7(1):14–22.

Acedo L. A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript. Mathematical and Computational Applications 2019 jan;24(1):14–39. https://www.mdpi.com/2297-8747/24/1/14/htmhttps://www.mdpi.com/2297-8747/24/1/14.

Lasfar M, Bouden H. A method of data mining using Hidden Markov Models (HMMs) for protein secondary structure prediction. In: Procedia Computer Science, vol. 127; 2018. p. 42–51.

Bhor P, Sodhi GS, Singh D. Hidden markov model for the heart rate variability detection. International Journal of Engineering and Advanced Technology 2019 jun;8(5):2494–2499.

Touloupou P, Finkenstädt B, Spencer SEF. Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models. Journal of Computational and Graphical Statistics 2020 apr;29(2):238–249. https://www.tandfonline.com/doi/abs/10.1080/10618600.2019.1654880.

Nootyaskool S, Choengtong W. Hidden Markov Models predict foreign exchange rate. In: 14th International Symposium on Communications and Information Technologies, ISCIT 2014 Institute of Electrical and Electronics Engineers Inc.; 2015. p. 99–101.

Mustafa MK, Allen T, Appiah K. A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Computing and Applications 2019;31(1):891–899.

Tarasov S. Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits. Systematic Biology 2019 sep;68(5):698–716.

Lin Z, Zamanighomi M, Daley T, Ma S, Wong WH. Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression. Statistical Science 2020;35(1):2–13.

Bieck R, Heuermann K, Pirlich M, Neumann J, Neumuth T. Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery. International Journal of Computer Assisted Radiology and Surgery 2020;15(12):2089–2100.

Ali SF, Hassan MT. Feature based techniques for a driver’s distraction detection using supervised learning algorithms based on fixed monocular video camera. KSII Transactions on Internet and Information Systems 2018;12(8):3820–3841.

Baum LE, Petrie T. Statistical Inference for Probabilistic Functions of Finite State Markov Chains. The Annals of Mathematical Statistics 1966 dec;37(6):1554–1563.

Fawer U, Aazhang B. A Multiuser Receiver for Code Division Multiple Access Communications over Multipath Channels. IEEE Transactions on Communications 1995;43(234):1556–1565.

Chen JD, Ueng FB, Lin PF. A low-complexity adaptive receiver for DS-CDMA systems in unknown code delay environment. International Journal of Communication Systems 2011 feb;24(2):225–238.

Ahmed J. Spectral Efficiency Comparison of Asynchronous MC-CDMA, MC DS-CDMA and MT-CDMA with Carrier Frequency Offset. Arabian Journal for Science and Engineering 2019 mar;44(3):1833–1841.

Halak B, Ma T, Wei X. A dynamic CDMA network for multicore systems. Microelectronics Journal 2014;45(4):424–434.

Shen Y, Xu Y. Multiple-Access Interference and Multipath Influence Mitigation for Multicarrier Code-Division MultipleAccess Signals. IEEE Access 2020;8(1):3408–3415.

Kim Y, Lee H, Ahn J, Chung J. Selection of CDMA and OFDM using machine learning in underwater wireless networks. ICT Express 2019 dec;5(4):215–218.

Ogbodo EU, Dorrell DG, Abu-Mahfouz AM. Performance measurements of communication access technologies and improved cognitive radio model for smart grid communication. Transactions on Emerging Telecommunications Technologies 2019 oct;30(10):e3653.

Shestakov VV, Manonina IV. Definition of Parameters of the Source of Error Model for Communication Systems with Mobile Objects. In: 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO 2020 Institute of Electrical and Electronics Engineers Inc.; 2020. p. 1–7.

Kordnoori S, Mostafaei H, Behzadi MH. PSO Optimized Hidden Markov Model Performance Analysis for IEEE 802.16/WiMAX Standard. Wireless Personal Communications 2019 oct;108(4):2461–2476.

Myint SH, Yu K, Sato T. Modeling and Analysis of Error Process in 5G Wireless Communication Using Two-State Markov Chain. IEEE Access 2019;7:26391–26401.

Kordnoori S, Mostafaei H, Behzadi M. An application of a semi-hidden Markov model in wireless communication systems. Mathematical Sciences 2019 mar;13(1):61–67.

Xu BL, Fu YF, Shi G, Yin XX, Miao L, Wang ZD, et al. Comparison of optical and concentration feature used for fNIRSbased BCI system using HMM. In: Applied Mechanics and Materials, vol. 385-386; 2013. p. 1443–1448.

Ranjan R, Mitra D. Order estimation of HMM Discrete Channel Model for OFDM systems. In: Proceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012; 2012. p. 41–44.

Srinivas S, Shanmugan KS. Form-HMM, a forward-only realtime modified hidden markov modeling algorithm for tracking bursty digital channels. In: Fifth IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks Institute of Electrical and Electronics Engineers (IEEE); 2005. p. 1–6.




DOI: http://dx.doi.org/10.12962/j20882033.v31i3.7016

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