Communication messages to and from mobile wireless users commonly transit combined wired and wireless subnets which are vulnerable to time variant mobile wireless channel conditions.  General cases include: 1) non-dispersive free space loss, 2) non-dispersive fading, 3) time dispersive distortion, 4) frequency dispersive distortion, and 5) dual time and frequency dispersive distortion. Cognitive processing architectures (CPA) can mitigation these problematic conditions with channel state recognition (CSR) algorithms which respond to time-variant distortion without the aid of training sequences or pilot tones.  They can provide efficient distortion state awareness to downstream cognitive processes; which apply near real time mitigation selections among SNR loss, flat-fading, inter-symbol interference (ISI), and/or inter-frequency interference (IFI) methods. This paper covers recent research by the authors to introduce channel state recognition (CSR) algorithms, a CSR testbed, and a reference waveform generator (RWG).  The CSR testbed provides an integrated environment for algorithm verification, and the RWG provides symbol streams with controlled channel states based on calibrated symbol and channel parameters.  Applying reference waveforms to CSR algorithms provides effective algorithm performance verification.  This paper also surveys published wireless channel multistate hidden Markov models (HMM) revealing mature generative and recognition HMM applications for modeling wireless channel parameters.  However, none have been found for recognition of channel dispersion and related conditions such as frequency selectivity and/or time selectivity. Therefore, the authors introduce a mobile wireless channel (MWC) distortion state model (DSM) and a distortion mitigation transform (DMT) linking time-variant non-dispersive, single, or dual-dispersive channel states with effective mitigation methods.  Additionally, the DSM is embedded in a distortion state recognition (DSR) HMM and the CSR testbed for performance verification.  Test results for the DSR algorithm demonstrate the utility of the DSM and the feasibility of CSR.  Accuracy performance results agree with published standard HMM recognition accuracy in terms of sensitivity and specificity.  DSR algorithm limitations are noted and provide direction for future CSR research efforts.

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Mobile Wireless Channel Dispersion State Recognition An Enabling Cognitive Radio Environmental Awareness Algorithm

Kenneth Brown*, Glenn Prescott**
Department of Applied Information Sciences, Johns Hopkins University, Maryland.
Electrical and Computer Engineering Department, University of Kansas
Periodicity:November - January'2012
DOI : https://doi.org/10.26634/jcs.1.1.1731

Abstract

Communication messages to and from mobile wireless users commonly transit combined wired and wireless subnets which are vulnerable to time variant mobile wireless channel conditions.  General cases include: 1) non-dispersive free space loss, 2) non-dispersive fading, 3) time dispersive distortion, 4) frequency dispersive distortion, and 5) dual time and frequency dispersive distortion. Cognitive processing architectures (CPA) can mitigation these problematic conditions with channel state recognition (CSR) algorithms which respond to time-variant distortion without the aid of training sequences or pilot tones.  They can provide efficient distortion state awareness to downstream cognitive processes; which apply near real time mitigation selections among SNR loss, flat-fading, inter-symbol interference (ISI), and/or inter-frequency interference (IFI) methods. This paper covers recent research by the authors to introduce channel state recognition (CSR) algorithms, a CSR testbed, and a reference waveform generator (RWG).  The CSR testbed provides an integrated environment for algorithm verification, and the RWG provides symbol streams with controlled channel states based on calibrated symbol and channel parameters.  Applying reference waveforms to CSR algorithms provides effective algorithm performance verification.  This paper also surveys published wireless channel multistate hidden Markov models (HMM) revealing mature generative and recognition HMM applications for modeling wireless channel parameters.  However, none have been found for recognition of channel dispersion and related conditions such as frequency selectivity and/or time selectivity. Therefore, the authors introduce a mobile wireless channel (MWC) distortion state model (DSM) and a distortion mitigation transform (DMT) linking time-variant non-dispersive, single, or dual-dispersive channel states with effective mitigation methods.  Additionally, the DSM is embedded in a distortion state recognition (DSR) HMM and the CSR testbed for performance verification.  Test results for the DSR algorithm demonstrate the utility of the DSM and the feasibility of CSR.  Accuracy performance results agree with published standard HMM recognition accuracy in terms of sensitivity and specificity.  DSR algorithm limitations are noted and provide direction for future CSR research efforts.

Keywords

 Channel State Awareness, Channel State Recognition; Cognitive Radio; Cognitive Processing Architecture, Dispersion State Model; Distortion Mitigation; Distortion Mitigation Transform, Distortion State Recognition, Environmental Awareness; Mitigation Processing; Mobile Wireless Channel State Model; Mobile Wireless Channel Dispersion State Model; Mobile Wireless Channel Environmental Awareness; Mobile Wireless Channel Situational Awareness; Mobile Wireless Channel State Recognition; Software Define

How to Cite this Article?

 Brown, K. D., and Prescott, G. (2012). Mobile Wireless Channel Dispersion State Recognition - An Enabling Cognitive Radio Environmental Awareness Algorithm. i-manager’s Journal on Communication Engineering and Systems, 1(1), 20-38. https://doi.org/10.26634/jcs.1.1.1731

References
[1]. Arauz, J., & Krishnamurthy, P. (2002). "A study of different partitioning schemes in first order Markovian models for Rayleigh fading channels,". s.l. : Wireless Personal Multimedia Communications, 2002. The 5th International Symposium on, Vol.1, pp. 277- 281 Vol.1, 27-30 Oct. 2002.
[2]. Arslan H. Ed. (2007); Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems; Springer.
[3]. Atallah, M.J. (Ed.) (1999). Algorithms and Theory of Computation Handbook; CRC Press.
[4]. Babich, F., & Lombardi, G (2000). "A Markov model for the mobile propagation channel,". no.1, s.l. : Vehicular Technology, IEEE Transactions on , Jan 2000, Vol. 49. pp.63- 73.
[5]. Babich, F., & Lombardi, G (1997). A measurement based Markov model for the indoor propagation channel. Phoenix, AZ : Vehicular Technology Conference, 1997 IEEE 47th , 1997. 0-7803-3659-3.
[6]. Babich, F., Kelly, O.E., & Lombardi, G. (1999). "A context-tree based model for quantized fading,". no.2, s.l. : Communications Letters, IEEE, pp.46-48, Feb 1999, Vol. vol.3. pp.46-48.
[7]. Babich, F., Kelly, O.E., & Lombardi, G. (2000). "Generalized Markov modeling for flat fading,". s.l. : Communications, IEEE Transactions on, Vol. 48, No.4, pp.547-551, Apr 2000.
[8]. Babich, F., Lombardi, G., & Valentinuzzi, E. (1999). "Variable order Markov modelling for LEO mobile satellite channels ,". no.8, s.l. : Electronics Letters , 15 Apr 1999, Vol. 35. pp.621-623.
[9]. Benvenuto, N., & Cherubini, G. (2002). Algorithms for Communications Systems & their Applications; John Wiley and Sons.
[10]. Buracchini, E. (2000). "The software radio concept," Communications Magazine, IEEE, vol.38, no.9, pp.138- 143, Sep 2000.
[11]. Burzigotti, P. (2008). "DVB-SH Analysis Using a Multi-State Land Mobile Satellite Channel Model,". et al. s.l. : Advanced Satellite Mobile Systems, 2008. ASMS 2008, 26-28 Aug. 2008, Vols. 4th , vol., no. pp.149-155.
[12]. Chockalingam, A., Milstein, L.B., & Zorzi, M. (1996). "Performance of a wireless media access protocol on a M a r k o v i a n f a d i n g c h a n n e l , " . s . l . : G l o b a l Telecommunications Conference, 1996. GLOBECOM '96. 'Communications: The Key to Global Prosperity ,, 18-22 Nov 1996. vol.3, no., pp.1769-1773 vol. 3.
[13]. Chung, Wei-ho & Yao, Kung. (2009). "Modified hidden semi-markov model for modelling the flat fading channel,". no.6, s.l. : Communications, IEEE Transactions on, June 2009, Vol. 57. pp.1806-1814.
[14]. Cormen, T. H., Leiserson, C. E., Rivest R. L., & Clifford S. (2009). Introduction to Algorithms, Third Edition; MIT Press.
[15]. Cuevas-Ruiz, J.L., & Delgado-Penin, J.A. (2004). "Channel model based on semi-Markovian processes. An approach for HAPS systems,". s.l. : Electronics, Communications and Computers, 2004. CONIELECOMP 2004, 16-18 Feb. 2004. 14th International Conference on , vol., no., pp. 52- 56.
[16]. Dongya, Shen, R. Jian, Y. Yihuai, G. Yong, C. Hongliang, & F. Shigang. (2005). "The six-state Markov model for land mobile satellite channels,". s.l. : Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2005. MAPE 2005. IEEE International Symposium on , 8-12 Aug. 2005. vol.2, no., pp. 1619- 1622 Vol. 2.
[17]. Du, Q., & Zhang, X. (2006). "Time-Sharing Based Rate Adaptation for Multicast Over Wireless Fading Channels in Mobile Wireless Networks,". s.l. : Information Sciences and Systems, 2006 40th Annual Conference on , 22-24 March 2006. vol., no., pp.1385-1390.
[18]. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological Sequence Analysis. New York: Cambridge University Press.
[19]. Fette B Ed. (2006.). Cognitive Radio Technology; Elsevier.
[20]. Fritchman, B. (1967). "A binar y channel characterization using partitioned Markov chains,". no.2, s.l. : Information Theory, IEEE Transactions on , Apr 1967, Vol. vol.13. pp. 221- 227.
[21]. Galvao, R. K. H., A. Izhac, S. Hadjiloucas, V. M. Becerra, & J.W. Bowen (2007). “MIMO Wiener Model Identification for Large Scale Fading of Wireless Mobile Communications Links”. June 2007, s.l. : IEEE Communication Letters, 2007, Vol. 11 no. 6.
[22]. Goldsmith, A.J., & Varaiya, P.P. (1996). "Capacity, mutual information, and coding for finite-state Markov channels,". no.3, s.l. : Information Theory, IEEE Transactions on , May 1996, Vol. vol.42. pp.868-886.
[23]. Han, SeonYeong & Abu-Ghazaleh (2009). "Estimated Measurement-Based Markov Models: Towards Flexible and Accurate Modeling of Wireless Channels,".. s.l.: "Wireless a n d M o b i l e C o m p u t i n g , N e t w o r k i n g a n d Communications, 2009. WIMOB 2009. IEEE International Conference on , 12-14 Oct. 2009. vol., no., pp.331-337.
[24]. Harada H., & Prasad R. (2002). Simulation and Software Radio for Mobile Communications; Artech House; 2002.
[25]. Huang, Xiaozhou & Wu, Hsiao-Chun. (2007). “Robust and Efficient Intercarrier Interference Mitigation for OFDM Systems in Time-Varying Fading Channels”. 5, s.l. : Vehicular Technology, IEEE Transactions on, 2007, Vol. 56 . 0018- 9545.
[26]. Inaty, E. (2006). "A Finite State Markov Chain-Based Umbrella Cell Channel Model for Fast Mobile Users,". s.l. : Wireless Communications, Networking and Mobile Computing, 2006. WiCOM 2006.International Conference on, 22-24 Sept. 2006. vol., no., pp.1-5.
[27]. Jain, A., R. Upadhyay, P. D. Vyavahare, & L.D. Arya (2007). "Stochastic Modeling and Performance Evaluation of Fading Channel for Wireless Network Design,". s.l. : Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on , vol.2, no., pp.893-898, 21-23 May 2007.
[28]. Jakes, W.C. Ed. (1974). Microwave Mobile Communications; IEEE Press reissue; AT&T; 1974.
[29]. Jeruchim, M.C.; Balaban, P.; & Shanmugan, K.S. (2000). Simulation of Communication Systems: Modeling, Methodology, and Techniques, 2nd Edition; Kluwer Academic/Plenum Publishers.
[30]. Kenington, P.B. (2005). RF and Baseband Techniques for Software Defined Radio; Artech House.
[31]. Kennedy, R.S. (1969). Fading Dispersive Channels; Wiley & Sons.
[32]. Kossiakoff, Alexander Smart, & William N.Sweet (2003). Systems Engineering: Principles and Practice. s.l. : John Wiley and Sons.
[33]. Kumwilaisak, W., & Kuo, C.-C.J. (2002). "Adaptive variable length Markov chain for non-stationary fading channel modeling,". s.l. : Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE, vol.3, no., pp. 2046- 2050 vol.3, 17-21 Nov. 2002.
[34]. Kundu, S.R., Basu, K., & Das, S.K. (2005). "Finite state Markov model for effective bandwidth calculation in wireless packet networks,". s.l. : Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 2005. WIOPT 2005. Third International Symposium on , vol., no., pp. 351- 357, 3-7, April 2005.
[35]. Lee, W.C.Y. (1982). Mobile Communications Engineering; McGraw Hill Book Company.
[36]. Lopez-Guerrero, M., & Gomez, J. (2009). "A mobilitybased channel model for wireless systems with adaptive modulation,". s.l. : Electrical and Computer Engineering, 2009. CCECE '09. Canadian Conference on, vol., no., pp.750-755, 3-6 May 2009.
[37]. Maier, Mark W., & Rechtin, Eberhardt (2000). The Art of Systems Architecting, 2nd Edition. s.l. : CRC Press.
[38]. Mathworks. (2011 03). hmmestimate function. Retrieved from MATLAB ® :Statistics toolbox technical documentation: http://www.mathworks.com/help/techdo /stats/hmmestimate.html.
[39].Mathworks. (2011 03). hmmtrain function. Retrieved f r om M AT L A B ® : S t a t i s t i c s t o o l b o x t e c h n i c a l documentation: http://www.mathworks.com /help/ techdoc/stats/hmmtrain.html.
[40]. Mathworks. (2011, 03). hmmdecode function. Retrieved from MATLAB®:Statistics toolbox technical documentation: http://www.mathworks.com/help/ techdoc/stats/hmmdecode.html.
[41]. MATLAB ®. (2011 03). Registered Trademark. Natick, MA, U.S.A.: Mathworks Inc.
[42]. Mitola J. III (2006). Cognitive Radio Architecture, The Engineering Foundations of Radio XML; Wiley and Sons; 2006.
[43]. Moon, T.K., & Stirling, W.C. (2000).; Mathematical Methods and Algorithms for Signal Processing; Prentice Hall; 2000.
[44]. Olama, M.M., Djouadi, S.M., & Charalambous, C.D. (2006). “Time varying channel modeling for ad-hoc mobile wireless networks ”. Las Vegas, NV : Wireless Communications and Networking Conference, 2006. WCNC 2006. IEEE , 2006. 1525-3511.
[45]. Pahlavan, K., & Levesque, A.H. (1995). Wireless Information Networks; John Wiley and Sons.
[46]. Parsons, J.D. (2000). The Mobile Radio Propagation Channel 2nd edition; John Wiley & Sons.
[47]. Pimentel, C., Falk, T.H., & Lisboa, L. (2004). "Finite-state Markov modeling of correlated Rician-fading channels,". s.l. : Vehicular Technology, IEEE Transactions on, vol.53, no.5, pp. 1491- 1501, Sept. 2004.
[48]. Poikonen, J. Bilbao (2009). “A finite-state simulation model for OFDM over frequency-selective fast fading channels”.: Broadband Multimedia Systems and Broadcasting, 2009. BMSB '09. IEEE International Symposium on, 2009. 978-1-4244-2590-7.
[49]. Proakis, J.G. (1989). Digital Communications 2nd Edition; McGraw Hill.
[50]. Rabiner, L.R. (1989). “A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”. No 2, s.l. : IEEE, 1989, Vol. 77. 8825949.
[51]. Rabiner, L.R., & Juang, B.H. (January, 1986). “An Introduction to Hidden Markov Models”. IEEE ASSP Magazine.
[52]. Rappaport, T.S. (2002). Wireless Communications: Principles and Practice, 2nd Edition; Prentice Hall.
[53]. Reed, J.H. (2002). Software Radio, A Modern Approach to Radio Engineering; Prentice Hall; 2002.
[54]. Richter, A., Salmi, J. Koivunen, & V. Cannes (2006). “An Algorithm for Estimation and Tracking of Distributed Diffuse Scattering in Mobile Radio Channels”. Signal Processing Advances in Wireless Communications, 2006. SPAWC '06. IEEE 7th Workshop on , 2006. 2-5 July 2006 ; pp. 1 - 5.
[55]. Riediger, M., & Shwedyk, E. (2003). "Communication receivers based on Markov models of the fading channel," . s.l. : Communications, IEEE Proceedings, vol.150, no.4, pp. 275- 279, 12 Aug. 2003.
[56]. Sanchez-Salas, D.A., & Cuevas-Ruiz, J.L. (2007). "Nstates Channel Model using Markov Chains,". s.l. : Electronics, Robotics and Automotive Mechanics Conference, 2007. CERMA 2007, vol., no., pp.342-347, 25-28, Sept. 2007.
[57]. Sklar, B. (2001). Digital Communications: Fundamentals and Applications 2nd Edition; Prentice Hall.
[58]. Steffan, H. (2002). Adaptive generative radio channel models. The Hague , Netherlands : Personal, Indoor and Mobile Radio Communications, 1994. Wireless Networks - Catching the Mobile Future., 5th IEEE International Symposium on, 2002. 06 August 2002.
[59]. Su, Tao, Ling, Hao & Vogel, W.J. (1998). "Markov modeling of slow fading in wireless mobile channels at 1.9 GHz,". s.l. : Antennas and Propagation, IEEE Transactions on, vol.46, no.6, pp.947-948, Jun 1998.
[60]. Swarts, F., & Ferreira, H.C. (1993). Markov characterization of channels with soft decision outputs. May 1993, s.l. : Communications, IEEE Transactions on , 1993, Vol. 41 Issue:5 . 0090-6778 .
[61]. Tan, C.C., & Beaulieu, N.C (2000). "On first-order Markov modeling for the Rayleigh fading channel,".. s.l. : Communications, IEEE Transactions on, vol.48, no.12, pp.2032-2040, Dec 2000.
[62]. Tranter, W.H., Shanmugan, K.S., Rappaport, T.S., & Kosbar K.L. (2004). Pinciples of Communication Systems Simulation with Wireless Applications; Prentice Hall.
[63]. Wakana, H. (1997). "Propagation model for simulating shadowing and multipath fading in land-mobile satellite channel,". s.l. : Electronics Letters, vol.33, no.23, pp.1925- 1926, 6 Nov 1997.
[64]. Wang, H.S., & Moayeri, N. (1993). "Modeling, capacity, and joint source/channel coding for Rayleigh fading channels,". s.l. : Vehicular Technology Conference, 1993 IEEE 43rd , vol., no., pp.473-479, 18-20 May 1993.
[65]. Wang, Hong Shen & Chang, Pao-Chi. (1996). "On verifying the first-order Markovian assumption for a Rayleigh fading channel model,". s.l. : Vehicular Technology, IEEE Transactions on , vol.45, no.2, pp.353-357, May 1996.
[66]. Wieser, V., & Psenak, V. Brno (2007). “Mobile Radio Channel State Prediction for Power Control in WCDMA Mobile Network ”. Radioelektronika, 2007. 17th International Conference , 2007. pp. 1-4 , 24-25 April 2007.
[67]. Xie, Yongjun, & Fang, Yuguang. (2000). "A general statistical channel model for mobile satellite systems ,". s.l. : Vehicular Technology, IEEE Transactions on, vol.49, no.3, pp.744-752, May 2000.
[68]. Yang, J., & Tin, & N. Khandani, A. K. (2002). “Adaptive modulation and coding in 3G wireless systems.” s.l. : Vehicular Technology Conference, 2002. Proceedings. VTC 2002-Fall. 2002 IEEE 56th, 2002. pp. 544- 548 vol.1.
[69]. Yuen, Ching-Wan & Yue, On-Ching (2006). "Channel State Dependent Packet Discard Policy for 3G Networks,". s.l. : Vehicular Technology Conference, 2006. VTC 2006- Spring. IEEE 63rd , vol.1, no., pp.405-409, 7-10 May 2006.
[70]. Zhang, Qinqing & Kassam, S.A. (1993). "Finite-state Markov model for Rayleigh fading channels,". s.l. : Communications, IEEE Transactions on , vol.47, no.11, pp.1688-1692, Nov 1999.
[71]. Ziemer, R.E.; & Tranter, W.H. (2002). Principles of Communications: Systems, Modulations, and Noise (5th Edition); John Wiley and Sons.
[72]. Ziemer, R.E., Tranter, W.H., & Fannin, D.R. (1983). Signals and Systems: Continuous and Discrete; Macmillan Publishing.
[73]. Zorzi, Michele, Rao, R.R., & Milstein, L.B. (1995). “On the accuracy of a first-order Markov model for data transmission on fading channels”. Tokyo : Universal Personal Communications. 1995. Record., 1995 Fourth IEEE International Conference on , 1995. pp.211-215, 6-10 Nov 1995.
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