TY - GEN
T1 - Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics
AU - Zilberstein, Nicolas
AU - Dick, Chris
AU - Doost-Mohammady, Rahman
AU - Sabharwal, Ashutosh
AU - Segarra, Santiago
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the underdamped Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed overdamped Langevin-based MIMO detector.
AB - We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the underdamped Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed overdamped Langevin-based MIMO detector.
KW - Markov chain Monte Carlo
KW - Massive MIMO detection
KW - diffusion process
KW - underdamped Langevin dynamics
UR - https://www.scopus.com/pages/publications/86000385916
UR - https://www.scopus.com/inward/citedby.url?scp=86000385916&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10096783
DO - 10.1109/ICASSP49357.2023.10096783
M3 - Conference contribution
AN - SCOPUS:86000385916
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -