Deep learning (DL) is attracting considerable attention in the design of communication
systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large …

In this paper, we propose deep-unfolded versions of the recently proposed superiorized
adaptive projected subgradient method for MIMO detection. The proposed methods require …

This paper addresses the problem of symbol detection in time-varying Massive Multiple-
Input Multiple-Output (M-MIMO) systems. While conventional detection techniques either …

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has
been deemed as a promising technique for future wireless communications. However, most …

In this paper, we investigate the use of model-based neural networks for Massive Multiple-
Input Multiple-Output (MMIMO) detection. Recently, a new M-MIMO detection architecture …

Multiple-user, multiple-input, multiple-output (MU-MIMO) systems supporting a large number
of concurrent streams have the potential to substantially improve the connectivity and …

The model-driven deep learning method has been verified to be effective for signal detection
in the massive multi-input multi-output (MIMO) system. In previous work, this kind of methods …

In this work, we consider the use of model-driven deep learning techniques for massive
multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO …

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection
approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed …

In this paper, we reveal that artificial neural network (ANN) assisted multiple-input multiple-
output (MIMO) signal detection can be modeled as ANN-assisted lossy vector quantization …