Learning Modified Gated Recurrent Units for Information Feedback in Unfolded Turbo VEP MU-MIMO Receivers

Multi-User Multiple-Input Multiple-Output (MU-MIMO) communication systems have gained significant attention due to their potential to enhance spectral efficiency and improve overall system performance. Soft-Input Soft-Output (SISO) detectors and decoders for iterative algorithms are commonly employed for the estimation of transmitted bits in such system. Message passing SISO detectors such as Vector Expectation Propagation (VEP) have demonstrated significant superiority over linear detectors such as Linear Minimum Mean Square Error (LMMSE) at the cost of higher computational complexity. SISO receivers rely on different hyper-parameters that can be optimized. In this article, we propose a modification of Gated Recurrent Unit (GRU), a type of neural network architecture from the Recurrent Neural Networks (RNN) field, suitable to learn hyperparameters of unfolded SISO iterative algorithms for multi-user MIMO communications. Our proposed algorithm outperforms both non-learned and state-of-the-art unfolded algorithms with a fair complexity increase.