Summary form only: Abstracts of articles presented in this issue of the publication.

The global wireless industry works like clockwork. Every decade, the global community ratifies a new generation of cellular standards relying on advances from the past decades; a similar rhythm drives Wi-Fi standardization. In all standards, cellular and Wi-Fi, a core design principle is that wireless nodescan either transmit or receive in a given frequency band. However, a node cannot simultaneously

Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips

Hybrid dynamical systems, i.e., systems that have both continuous and discrete states, are ubiquitous in engineering but are difficult to work with due to their discontinuous transitions. For example, a robot leg is able to exert very little control effort, while it is in the air compared to when it is on the ground. When the leg hits the ground, the penetrating velocity instantaneously collapses to

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial

The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental theories, models, hardware architectures, and application systems toward more general artificial intelligence (AI) by learning from the information processing mechanisms or structures/functions of biological nervous systems. It is regarded as one of the most promising research directions for future intelligent

Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task. Previous work in MTL has mainly focused on

Time-division duplex (TDD) and frequency-division duplex (FDD) are mainly used in commercial new radio (NR) deployments, where the time- or frequency-domain resources are split between downlink (DL) and uplink (UL). Full duplex (FD) will enable 5G-advanced and 6G systems to go beyond TDD and FDD operation into a new duplexing mode that leverages the benefits of both TDD/FDD deployments. It achieves

Error correction coding (i.e., channel coding) is a key ingredient of any digital communications system. In mobile wireless communications, channel codes have evolved from simple convolutional codes in Global System for Mobile Communications (GSM) (2G), parallel concatenated (turbo) codes in Universal Mobile Telecommunications Service (UMTS) (3G), and long-term evolution (LTE) (4G), to carefully designed

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing