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

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