Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a
promising technology to tremendously elevate the efficiency of multiple-input multiple-output …

Recent interest in artificial intelligence (AI) for wireless communication networks has grown
significantly for multiple-input multiple-output (MIMO) detection. AI techniques, particularly …

The great success of deep learning (DL) has inspired researchers to develop more accurate
and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL …

In recent times, artificial intelligence (AI) has gained considerable attention as a highly
promising technology for enhancing the performance of multiple-input multiple-output …

In this paper, we introduce a framework for a systematic acceleration of deep neural network
(DNN) design for MIMO detection. A monotonically non-increasing function is used to scale …

Signal detection plays a significant role at the receiver of current multiple-input multiple-
output (MIMO) communication systems. In this paper, we propose a deep learning aided …

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 recent times, artificial intelligence (AI) has gained considerable attention as a highly
promising technology for enhancing the performance of multiple-input multiple-output …

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 …

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 …