  We introduce a revised derivation of the bitwise Markov Chain Monte Carlo
(MCMC) multiple-input multiple-output (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 is used on a slowly
converging example, the output LLRs 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 BER 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
maximum-a-posteriori (MAP) performance even with challenging, realistic,
highly-correlated channels at the maximum MIMO sizes and modulation rates
supported by the 802.11ac WiFi specification, 8x8 MIMO 256 QAM.
