Trained Parameter-Based Path Sampling for Low Complexity Soft MIMO Detection

Abstract

In this paper, we consider the topic of multiple-input multiple-output (MIMO) detection for coded systems. Tree search based detectors are well-behaved but have high complexity, while low-cost random sampling based detectors may suffer from distribution mismatch and performance loss. To overcome these issues, we propose a data-driven algorithm, called trained parameter based path sampling (TPbPS), to optimize parameters of the sampling distribution for random sampling based detectors. A more robust approach is designed to avoid sampling repetition and missing by derandomizing the path sampling, where paths are determined by the trained sampling distribution as well as fixed and uniformly-distributed numbers. The error probability is derived for the proposed TPbPS, and it shows that the full diversity can be achieved by elaborately designing a set of sampling path for each quantized noise level. Further, a soft-TPbPS algorithm of computing reliable soft outputs for channel decoders is proposed, via applying end-to-end Bayesian optimization to learn parameters of sampling distribution that goes straight at the objective of optimizing the block error rate performance. Combining these techniques yields enhanced soft MIMO detection designs that non-trivially advance the state-of-the-art, and provide significant performance or complexity gains over traditional methods.