Semi-Supervised ML-Based Joint Channel Estimation and Decoding for m-MIMO With Gaussian Inference Learning

This letter proposes the use of quasi-orthogonal space-time block codes (QOSTBC) to enhance link quality and reliability in massive multiple-input multiple-output (m-MIMO) systems subject to independent fading in dynamic channels. It has been shown, however, that the computational complexity of classical decoding algorithms, such as maximum likelihood, can hinder the adoption of QOSTBC codes in systems with many antennas and high-order modulation schemes. Complex-valued neural networks (CVNNs) offer a promising alternative for joint decoding and channel estimation with competitive computational complexity. This letter presents an extension of our previously proposed CVNN with supervised training, which incorporates two semi-supervised learning techniques: hard inference learning (HIL) and Gaussian inference learning (GIL). By leveraging non-pilot-aided data, HIL and GIL enable the CVNNs to self-learn from useful information, increasing their tracking ability and robustness in dynamic channels.