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We propose a multiple-input multiple-output (MIMO) detector based on anannealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Ourdetector achieves state-of-the-art performance in terms of symbol error rate(SER) while keeping the computational complexity in check. Indeed, our methodcan be easily tuned to strike the right balance between computationalcomplexity and performance as required by the application at hand. This balanceis achieved by tuning hyperparameters that control the length of the simulatedLangevin dynamic. Through numerical experiments, we demonstrate that ourdetector yields lower SER than competing approaches (including learning-basedones) with a lower running time compared to a previously proposed\emph{overdamped} Langevin-based MIMO detector.

Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics