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In the research of time varying communication channels, signal amplitude attenuation is considered to be the main cause of channel varying memories. The Gilbert-Elliot channel has been used to model this kind of memories for a long time with a decision-feedback decoder or an equivalent genie-aided decoder being its optimal decoders.
The orthodoxy in the time-varying channel is that, the mutual information (MI) rate of the Uniform Symmetric Variable Noise Finite State Markov Channel (USVN-FSMC) is maximized by the channel input of maximum entropy, i.e., independent and identically distributed (i.i.d.) and uniform. The optimal signal detection is performed by a decision-feedback decoder (DFD). However this decoder is not reliable;...
This paper looks into the modeling of information transmission over cognitive interrogator-sensor networks (CISNs), which represent a novel and important class of sensor networks deployed for surveillance, tracking, and imaging applications. The crux of the problem is to develop a channel model that allows for the evaluation of the sensing channel capacity and error rate performance in CISNs, for...
This paper looks into the modeling of information transmission over cognitive interrogator-sensor networks (CISNs), which represent an import class of sensor networks deployed for surveillance, tracking, and imaging applications. The crux of the problem is to develop a channel model that allows for evaluation of the sensing channel capacity and error rate performance, where the sensing link is overlaid...
In this paper we demonstrate the application of Hidden Markov Models (HMM) for localization and tracking in ultra wide band (UWB) radar networks. To improve localization, a Voronoi region based approach is utilized to form a HMM for detection and tracking of mobile target. The observations used for the HMM localization are obtained from the power delay profile of the received signals. In UWB systems...
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