Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Abstract
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.
BibTeX
@inproceedings{icassp2023_acceleratedmassi,
title = {Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics},
author = {Nicolas Zilberstein and Chris Dick and Rahman Doost-Mohammady and Ashutosh Sabharwal and Santiago Segarra},
booktitle = {ICASSP 2023},
year = {2023}
}