Next-generation cellular technologies, commonly referred to as the sixth generation (6G),
are envisioned to support a higher system capacity, better performance, and network …

Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …

Imagine a coverage area where each mobile device is communicating with a preferred set of
wireless access points (among many) that are selected based on its needs and cooperate to …

Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for
the next generation of wireless communication networks because it has the potential to …

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In
particular, the MIMO detector is specially designed by unfolding an iterative algorithm and …

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency
space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid …

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-
precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO …

In this letter, we propose a deep learning (DL)-based channel estimation scheme for the
massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop …

The users at cell edge of a massive multiple-input-multiple-output (MIMO) system suffer from
severe pilot contamination (PC), which leads to poor quality of service (QoS). To enhance …

We address the joint device activity detection and channel estimation (JACE) problem in a
massive MIMO connectivity scenario in which a large number of mobile devices are …