Achieving Near MAP Performance With an Excited Markov Chain Monte Carlo MIMO Detector

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors J. C. Hedstrom, C. H. Yuen, R. Chen, B. Farhang-Boroujeny
Journal/Conference Name I
Paper Category
Paper Abstract We introduce a revised derivation of the bitwise Markov Chain Monte Carlo (MCMC) multiple-input multipleoutput (MIMO) detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling factors. Another common problem with MCMC algorithms is the unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations are used on a slowly converging example, the output log likelihood ratios can be unstable and overconfident. Therefore, we develop a method to identify rare slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in bit error rate plots. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximuma-posteriori performance even with challenging, realistic, and highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8 × 8 MIMO 256 quadrature amplitude modulation.
Date of publication 2017
Code Programming Language Cuda
Comment

Copyright Researcher 2022