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

• Published in: IEEE wireless communications letters Vol. 12; no. 12; p. 1
• Main Authors: Soares, Jonathan Aguiar, Mayer, Kayol Soares, Arantes, Dalton Soares
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Block codes; Channel estimation; Channels; complex-valued neural networks; Complexity; Decoding; Heuristic algorithms; Inference; inference learning; Machine learning; Massive-MIMO; Maximum likelihood decoding; MIMO communication; MIMO-OFDM; Neural networks; OFDM; Quality of service; Semi-supervised learning; Symbols; Training
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2023.3309479

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 work 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.