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

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 …

Applications of deep learning have not been adequately studied in communication systems,
though it is a tremendously popular approach in many other fields. This study focuses on the …

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

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 work, a deep learning-based symbol detection method is developed for multi-user
multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold …

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 …

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 …

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution
(LTE) and 5G. The information detection problem at the receiver side is in general difficult …

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