In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-
input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) …

Achieving adequate precision in deep learning based communications often requires large
network architectures, which results into unacceptable time delay and power consumption …

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 …

The revolution of Artificial Intelligence (AI) transforms the Multiple Input Multiple Output
(MIMO) technology into Massive MIMO (Ma-MIMO) technology. However, despite the …

This paper investigates the performance of deep neural network (DNN)-based signal
detection in multiple input, multiple output (MIMO), communication systems. MIMO …

In this letter, we present a novel deep-learning-based network for MIMO symbol detection,
referred to as a momentum-induced detection network, MomentNet. Inspired by the …

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 …

The multiple-input multiple-output over orthogonal delay-Doppler division multiplexing
(MIMO-ODDM) has recently attracted great interest as a promising solution for high-mobility …

This paper introduces a framework for systematic complexity scaling of deep neural network
(DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their …

To support the and development and application of the fifth-generation (5G) communication
and internet of things (IoT) networks, high data-rate wireless transmission is required. To …