Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.

3D Cooperative Localization in UAV Systems: CRLB Analysis and Security Solutions

This paper presents a robust and secure framework for achieving accurate and reliable cooperative localization in multiple unmanned aerial vehicle (UAV) systems. The Cramer-Rao low bound (CRLB) for the three-dimensional (3D) cooperative localization network is derived, with particular attention given to the non-uniform spatial distribution of anchor nodes. Challenges of mobility and security threats are addressed, corresponding solutions are brought forth and numerically assessed . The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detection (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in cooperative localization systems, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying malicious data, enhancing the security and resilience of the cooperative localization.

Hierarchical Cell-Free Massive MIMO for High Capacity with Simple Implementation

Cell-free massive multi-input multi-output (MIMO) has recently gained much attention for its potential in shaping the landscape of sixth-generation (6G) wireless systems. This paper proposes a hierarchical network architecture tailored for cell-free massive MIMO, seamlessly integrating co-located and distributed antennas. A central base station (CBS), equipped with an antenna array, positions itself near the center of the coverage area, complemented by distributed access points spanning the periphery. The proposed architecture remarkably outperforms conventional cell-free networks, demonstrating superior sum throughput while maintaining a comparable worst-case per-user spectral efficiency. Meanwhile, the implementation cost associated with the fronthaul network is substantially diminished.

A Short Overview of 6G V2X Communication Standards

We are on the verge of a new age of linked autonomous cars with unheard-of user experiences, dramatically improved air quality and road safety, extremely varied transportation settings, and a plethora of cutting-edge apps. A substantially improved Vehicle-to-Everything (V2X) communication network that can simultaneously support massive hyper-fast, ultra-reliable, and low-latency information exchange is necessary to achieve this ambitious goal. These needs of the upcoming V2X are expected to be satisfied by the Sixth Generation (6G) communication system. In this article, we start by introducing the history of V2X communications by giving details on the current, developing, and future developments. We compare the applications of communication technologies such as Wi-Fi, LTE, 5G, and 6G. we focus on the new technologies for 6G V2X which are brain-vehicle interface, blocked-based V2X, and Machine Learning (ML). To achieve this, we provide a summary of the most recent ML developments in 6G vehicle networks. we discuss the security challenges of 6G V2X. We address the strengths, open challenges, development, and improving areas of further study in this field.

Coverage Hole Elimination System in Industrial Environment

The paper proposes a framework to identify and avoid the coverage hole in an indoor industry environment. We assume an edge cloud co-located controller that followers the Automated Guided Vehicle (AGV) movement on a factory floor over a wireless channel. The coverage holes are caused due to blockage, path-loss, and fading effects. An AGV in the coverage hole may lose connectivity to the edge-cloud and become unstable. To avoid connectivity loss, we proposed a framework that identifies the position of coverage hole using a Support- Vector Machine (SVM) classifier model and constructs a binary coverage hole map incorporating the AGV trajectory re-planning to avoid the identified coverage hole. The AGV's re-planned trajectory is optimized and selected to avoid coverage hole the shortest coverage-hole-free trajectory. We further investigated the look-ahead time's impact on the AGV's re-planned trajectory performance. The results reveal that an AGV's re-planned trajectory can be shorter and further optimized if the coverage hole position is known ahead of time

Terahertz-Enpowered Communications and Sensing in 6G Systems: Opportunities and Challenges

The current focus of academia and the telecommunications industry has been shifted to the development of the six-generation (6G) cellular technology, also formally referred to as IMT-2030. Unprecedented applications that 6G aims to accommodate demand extreme communications performance and, in addition, disruptive capabilities such as network sensing. Recently, there has been a surge of interest in terahertz (THz) frequencies as it offers not only massive spectral resources for communication but also distinct advantages in sensing, positioning, and imaging. The aim of this paper is to provide a brief outlook on opportunities opened by this under-exploited band and challenges that must be addressed to materialize the potential of THz-based communications and sensing in 6G systems.

Opportunistic Reflection in Reconfigurable Intelligent Surface-Assisted Wireless Networks

This paper focuses on multiple-access protocol design in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs). By extending the existing approaches in single-user or single-RIS cases, we present two benchmark schemes for this multi-user multi-RIS scenario. Inspecting their shortcomings, a simple but efficient method coined opportunistic multi-user reflection (OMUR) is proposed. The key idea is to opportunistically select the best user as the anchor for optimizing the RISs, and non-orthogonally transmitting all users' signals simultaneously. A simplified version of OMUR exploiting random phase shifts is also proposed to avoid the complexity of RIS channel estimation.

A Simple Multiple-Access Design for Reconfigurable Intelligent Surface-Aided Systems

This paper focuses on the design of transmission methods and reflection optimization for a wireless system assisted by a single or multiple reconfigurable intelligent surfaces (RISs). The existing techniques are either too complex to implement in practical systems or too inefficient to achieve high performance. To overcome the shortcomings of the existing schemes, we propose a simple but efficient approach based on \textit{opportunistic reflection} and \textit{non-orthogonal transmission}. The key idea is opportunistically selecting the best user that can reap the maximal gain from the optimally reflected signals via RIS. That is to say, only the channel state information of the best user is used for RIS reflection optimization, which can in turn lower complexity substantially. In addition, the second user is selected to superpose its signal on that of the primary user, where the benefits of non-orthogonal transmission, i.e., high system capacity and improved user fairness, are obtained. Additionally, a simplified variant exploiting random phase shifts is proposed to avoid the high overhead of RIS channel estimation.

A Reliable and Resilient Framework for Multi-UAV Mutual Localization

This paper presents a robust and secure framework for achieving accurate and reliable mutual localization in multiple unmanned aerial vehicle (UAV) systems. Challenges of accurate localization and security threats are addressed and corresponding solutions are brought forth and accessed in our paper with numerical simulations. The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detectio (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in the mutual localization system, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying UAVs with malicious data, enhancing the security and resilience of the mutual localization

Considerations on the EMF Exposure Relating to the Next Generation Non-Terrestrial Networks

The emerging fifth generation (5G) and the upcoming sixth generation (6G) communication technologies introduce the use of space- and airborne networks in their architectures under the scope of non-terrestrial networks (NTNs). With this integration of satellite and aerial platform networks, better coverage, network flexibility and easier deployment can be achieved. Correspondingly, satellite broadband internet providers have launched an increasing number of small satellites operating in low earth orbit (LEO). These recent developments imply an increased electromagnetic field (EMF) exposure to humans and the environment. In this work, we provide a short overview of the state of consumer-grade satellite networks including broadband satellites and future NTN services. We also consider the regulatory state governing their operation within the context of EMF exposure. Finally, we highlight the aspects that are relevant to the assessment of EMF exposure in relation to NTNs.