TY - GEN
T1 - Detection by Sampling
T2 - 30th European Signal Processing Conference, EUSIPCO 2022
AU - Zilberstein, Nicolas
AU - Dick, Chris
AU - Doost-Mohammady, Rahman
AU - Sabharwal, Ashutosh
AU - Segarra, Santiago
N1 - Funding Information:
This work was partially supported by Nvidia. Email: {nzilberstein, doost, ashu, segarra}@rice.edu, cdick@nvidia.com.
Publisher Copyright:
© 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a prespecified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.
AB - Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a prespecified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.
KW - Langevin dynamics
KW - Markov chain Monte Carlo
KW - Massive MIMO detection
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M3 - Conference contribution
AN - SCOPUS:85141011149
T3 - European Signal Processing Conference
SP - 1651
EP - 1655
BT - 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PB - European Signal Processing Conference, EUSIPCO
Y2 - 29 August 2022 through 2 September 2022
ER -