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Complexity-Reduced Suboptimal Equalization with Monte Carlo Based MIMO Detectors | IEEE Conference Publication | IEEE Xplore

Complexity-Reduced Suboptimal Equalization with Monte Carlo Based MIMO Detectors


Abstract:

Optimal detection in multiple-input multiple-output (MIMO) frequency-selective systems is known to have exponential complexity in the number of transmitter antennas and c...Show More

Abstract:

Optimal detection in multiple-input multiple-output (MIMO) frequency-selective systems is known to have exponential complexity in the number of transmitter antennas and channel length resulting from intersymbol interference. Several studies focus on suboptimal detectors, proposing trade-offs between computational complexity and bit error rate. In this paper, we model the detection problem using factor graphs and apply the sum-product algorithm to derive the optimal detector. Then we propose a novel suboptimal particle filter detector, based on sequential Monte Carlo, followed by a Markov chain Monte Carlo step to further enhance performance. The proposed algorithm exchanges the exponential complexity in channel length for a linear complexity in the number of particles and achieves better bit error rate than the linear minimum mean square error (LMMSE) detector.
Date of Conference: 02-06 September 2019
Date Added to IEEE Xplore: 18 November 2019
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Conference Location: A Coruna, Spain

References

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