Large Scale MIMO Detection Using MCMC Approach With Blockwise Sampling
In this paper, a low complexity approach for the large scale (underdetermined) multiple input multiple output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein's algorithm is employed in each sub system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD) From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC based approach under low correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.
IEEE Transactions on Communications