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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...
This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multiple-input and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1...
Statistical detectors that are based on Markov chain Monte Carlo (MCMC) simulators have emerged as promising low-complexity solutions to both multiple-input multiple-output (MIMO) and code division multiple access (CDMA) communication systems. While these types of detectors achieve unprecedented near capacity performance, i.e., when operated in low signal-to-noise ratio (SNR) regime, they exhibit...
In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm...
In this paper, we present an overview of recent work on the applications of Markov Chain Monte Carlo (MCMC) techniques to both multiple-input and multiple-output (MIMO) detection and channel equalization. In the setting of MIMO detection, we have shown that, even for very large antenna systems with high spectral efficiencies of 24 bits/channel use (8 transmit and 8 receive antennas with 64 QAM modulation),...
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