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Accurately detecting symbols transmitted over multiple-input multiple-output(MIMO) wireless channels is crucial in realizing the benefits of MIMOtechniques. However, optimal MIMO detection is associated with a complexitythat grows exponentially with the MIMO dimensions and quickly becomesimpractical. Recently, stochastic sampling-based Bayesian inference techniques,such as Markov chain Monte Carlo (MCMC), have been combined with the gradientdescent (GD) method to provide a promising framework for MIMO detection. Inthis work, we propose to efficiently approach optimal detection by exploringthe discrete search space via MCMC random walk accelerated by Nesterov'sgradient method. Nesterov's GD guides MCMC to make efficient searches withoutthe computationally expensive matrix inversion and line search. Our proposedmethod operates using multiple GDs per random walk, achieving sufficientdescent towards important regions of the search space before adding randomperturbations, guaranteeing high sampling efficiency. To provide augmentedexploration, extra samples are derived through the trajectory of Nesterov's GDby simple operations, effectively supplementing the sample list for statisticalinference and boosting the overall MIMO detection performance. Furthermore, wedesign an early stopping tactic to terminate unnecessary further searches,remarkably reducing the complexity. Simulation results and complexity analysisreveal that the proposed method achieves exceptional performance in bothuncoded and coded MIMO systems, adapts to realistic channel models, and scaleswell to large MIMO dimensions.

Gradient-Based Markov Chain Monte Carlo for MIMO Detection