The rapid development of information and wireless communication technologies together
with the large increase in the number of end-users have made the radio spectrum more …

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of
attention in both academia and industry. Detection techniques have a significant impact on …

In this paper, we devise an efficient and flexible management for mobile edge computing
(MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the …

In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet
of things (IoT), where multiple users have some computational tasks assisted by multiple …

Recent development toward innovative applications and technologies like self-driving,
augmented reality, smart cities, and various other applications leads to excessive growth in …

In this paper, we propose an ultra-reliable multiuser multiple-input multiple-output (MU-
MIMO) detector based on deep learning for the fifth-generation and beyond the fifth …

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly
detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) …

To overcome the influence of channel estimation error on signal detection, this paper
presents a model-driven deep learning method for joint channel estimation and signal …

Intelligent signal processing for wireless communications is a vital task in modern wireless
systems, but it faces new challenges because of network heterogeneity, diverse service …

This paper investigates the optimal signal detection problem with a particular interest in
large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can …