Federated learning (FL) has garnered significant attention in the Internet-of-things (IoT) domain due to its ability to facilitate collaborative learning among distributed privacy-sensitive devices without compromising their local data. However, FL's development and application are hindered by the heterogeneity, vulnerability, and limited computing and wireless communication resources of IoT systems

On August 16, 2024, the U.S. Federal Communications Commission (FCC) released a Notice of Proposed Rulemaking (NPRM) and Declaratory Ruling seeking to revise the rules for the Citizens Broadband Radio Service (CBRS) band, 3.55-3.7 GHz [1]. While CBRS was developed specifically for the U.S. spectrum regulatory regime, it has inspired similar use of shared, locally licensed spectrum in other countries

This article presents the construction of WiSense, a human activity recognition dataset based on WiFi signals. With the application of machine learning in the field of WiFi sensing, the availability of datasets becomes a vexing problem because collecting WiFi data is a time-consuming and costly process. WiSense consists of data from ten volunteers performing six different activities in two rooms. We

In our continued efforts to reduce backlogs, we are delighted to present 34 articles from our Open Call submissions in this issue. These articles form Part 3 of the original series titled: A Potpourri on Wireless Advances.

Due to device-specific defects introduced during the hardware manufacturing process, the radio frequency fingerprint (RFF) can be extracted to identify wireless devices and further avoid spoofing attacks. Many effective RFF identification methods have been proposed based on either machine learning or deep learning. However, from the perspective of communication security, if the RFF of the transmitter

Integrated sensing and communication (ISAC) has attracted growing interest in sixth-generation (6G) and beyond wireless networks. The primary challenges faced by highly efficient ISAC include limited sensing and communication (S&C) coverage, constrained integration gain between S&C under weak channel correlations, and unknown performance boundaries. Intelligent reflecting/ refracting surfaces (IRSs)

An adaptive standardized protocol is essential for addressing inter-slice resource contention and conflict in network slicing. Traditional protocol standardization is a cumbersome task that yields hardcoded predefined protocols, resulting in increased costs and delayed rollout. Going beyond these limitations, this article proposes a novel multi-agent deep reinforcement learning (MADRL) communication

Physical layer security (PLS) is a lightweight, keyless, and information-theoretical security approach, which helps to enable confidential communications in wireless networks. However, the application of PLS in 5G and beyond wireless communications is rare due to its secrecy capacity and coverage limits. To make the PLS more appealing in 5G and beyond systems, new techniques emerged to address the

This article delves into advancements in resource allocation techniques tailored for systems utilizing reconfigurable intelligent surfaces (RIS), with a primary focus on achieving low-complexit and resilient solutions. The investigation of low-complexity approaches for RIS holds significant relevance, primarily owing to the intricate characteristics inherent in RIS-based systems and the need for deploying

The 6G wireless system is expected to deliver faster and more reliable wireless connections via cutting-edge radio technologies. However, a pivot to these radio technologies is the effective management of large-scale antenna arrays, which aims to construct valid spatial streams to maximize system throughput. In this paper, the authors explored generative artificial intelligence to accurately model

The 3GPP Release 17 has provisioned the integration of Non-Terrestrial Networks (NTN) with 5G terrestrial network (TN) infrastructure. NTN refers to a family of wireless communication systems, that mainly include low Earth orbit (LEO), medium Earth orbit (MEO) and geostationary Earth orbit (GEO) satellite constellations, high-altitude platforms (HAPS), and Unmanned Aerial Vehicles (UAVs) commonly known

As generative artificial intelligence (GAl) models continue to evolve, their generative capabilities are increasingly enhanced, and being used exten-sively in content generation. Furthermore, GAl also excels in data modeling and analysis, benefiting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing

Emerging intelligent reflective surfaces (IRSs) significantly improve system performance, but also pose a significant risk for physical layer security (PLS). Unlike the extensive research on legitimate IRS-enhanced communications, in this article we present an adversarial I RS-based, fully-passive jammer (FPJ). We describe typical application scenarios for disco IRS (DIRS)-based FPJ, where an illegitimate

Extremely large-scale multiple-input-multiple-out-put (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover

Recently, we have been witnessing the remarkable progress and widespread adoption of sensing technologies in autonomous driving, robotics, and metaverse. Considering the rapid advancement of computer vision (CV) technology to analyze the sensing information, we anticipate a proliferation of wireless applications exploiting the sensing and CV technologies in 6G. In this article, we provide a holistic