A Tensor-Train Framework for Bayesian Inference in High-Dimensional Systems: Applications to MIMO Detection and Channel Decoding

ArXi:2604.05890v1 Announce Type: cross Bayesian inference in high-dimensional discrete-input additive noise models is a fundamental challenge in communication systems, as the of the required joint a posteriori probability (APP) mass function grows exponentially with the number of unknown variables. In this work, we propose a tensor-train (TT) framework for tractable, near-optimal Bayesian inference in discrete-input additive noise models.