Complexity-Reduced Suboptimal Equalization with Monte Carlo Based MIMO Detectors
Guilherme C. G. Fernandes, Marcelo G. S. Bruno · 2019
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.