Complexity-Reduced Suboptimal Equalization with Monte Carlo Based MIMO Detectors. Fernandes, G. C. G. & Bruno, M. G. S. In *2019 27th European Signal Processing Conference (EUSIPCO)*, pages 1-5, Sep., 2019.

Paper doi abstract bibtex

Paper doi abstract bibtex

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.

@InProceedings{8902499, author = {G. C. G. Fernandes and M. G. S. Bruno}, booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)}, title = {Complexity-Reduced Suboptimal Equalization with Monte Carlo Based MIMO Detectors}, year = {2019}, pages = {1-5}, 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.}, keywords = {computational complexity;equalisers;error statistics;graph theory;intersymbol interference;Markov processes;MIMO communication;Monte Carlo methods;particle filtering (numerical methods);signal detection;sequential Monte Carlo;Markov chain Monte Carlo step;exponential complexity;channel length;linear complexity;bit error rate;square error detector;complexity-reduced suboptimal equalization;MIMO detectors;optimal detection;multiple-input multiple-output frequency-selective systems;transmitter antennas;intersymbol interference;computational complexity;detection problem;factor graphs;sum-product algorithm;optimal detector;novel suboptimal particle filter detector;Monte Carlo based MIMO detectors;suboptimal particle filter detector;linear minimum mean square error detector;LMMSE detector;Detectors;Monte Carlo methods;Mathematical model;MIMO communication;Transmitting antennas;Complexity theory;Bit error rate;Equalization;MIMO detection;particle filter;Markov chain Monte Carlo;factor graphs}, doi = {10.23919/EUSIPCO.2019.8902499}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532906.pdf}, }

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