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In this paper, we develop a statistical approach based on Markov chain Monte Carlo (MCMC) techniques for joint data detection and channel estimation over time-varying frequency-selective channels. The proposed detector, that we call MCMC with list channel estimates (MCMC-LCE), adopts the Gibbs sampler to find a list of mostly likely transmitted sequences and matching channel estimates/impulse responses...
In this paper, we develop a novel statistical detection algorithm following similar principles to that of expectation maximization (EM) algorithm. Our goal is to develop an iterative algorithm for joint channel estimation and data detection in channels that have a long memory and are fast varying in time. At each iteration, starting with an estimate of the channel, we combine a Markov Chain Monte...
In this paper, we develop a novel approach for joint channel estimation and Markov Chain Monte Carlo (MCMC) detection for time-varying frequency-selective channels. First, we propose a sequential channel estimation (SCE) MCMC algorithm that combines an MCMC algorithm for data detection, and an adaptive least mean square (LMS) algorithm for channel tracking, in a sequential fashion. Then we develop...
In this paper, we develop a novel list channel refinement (LCR) based Markov Chain Monte Carlo (MCMC) detector for time-varying frequency-selective channels. Information fed back from the channel decoder is exploited for both data detection and channel estimation. The LCR-MCMC detector adopts parallel Gibbs samplers to find a list of mostly likely transmitted sequences as well as matching channel...
In this work, we develop novel statistical detectors to combat intersymbol interference for frequency selective channels based on Markov Chain Monte Carlo (MCMC) techniques. While the optimal maximum a posteriori (MAP) detector has a complexity that grows exponentially with the constellation size and the memory of the channel, the MCMC detector can achieve near optimal performance with a complexity...
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