Covertness in the Near Field: Maximizing the Covert Region with FDA

Covert communication in wireless networks ensures that transmissions remain undetectable to adversaries, making it a potential enabler for privacy and security in sensitive applications. However, to meet the high performance and connectivity demands of sixth-generation (6G) networks, future wireless systems will require larger antenna arrays, higher operating frequencies, and advanced antenna architectures. This shift changes the propagation model from far-field planar-wave to near-field spherical-wave which necessitates a redesign of existing covert communication systems. Unlike far-field beamforming, which relies only on direction, near-field beamforming depends on both distance and direction, providing additional degrees of freedom for system design. In this paper, we aim to utilize those freedoms by proposing near-field Frequency Diverse Array (FDA)-based transmission strategies that manipulate the beampattern in both distance and angle, thereby establishing a non-covert region around the legitimate user. Our approach takes advantage of near-field properties and FDA technology to significantly reduce the area vulnerable to detection by adversaries while maintaining covert communication with the legitimate receiver. Numerical simulations show that our methods outperform conventional phased arrays by shrinking the non-covert region and allowing the covert region to expand as the number of antennas increases.

Robust Communication Design in RIS-Assisted THz Channels

Terahertz (THz) communication offers the necessary bandwidth to meet the high data rate demands of next-generation wireless systems. However, it faces significant challenges, including severe path loss, dynamic blockages, and beam misalignment, which jeopardize communication reliability. Given that many 6G use cases require both high data rates and strong reliability, robust transmission schemes that achieve high throughput under these challenging conditions are essential for the effective use of high-frequency bands. In this context, we propose a novel mixed-criticality superposition coding scheme for reconfigurable intelligent surface (RIS)-assisted THz systems. This scheme leverages both the strong but intermittent direct line-of-sight link and the more reliable, yet weaker, RIS path to ensure robust delivery of high-criticality data while maintaining high overall throughput. We model a mixed-criticality queuing system and optimize transmit power to meet reliability and queue stability constraints. Simulation results show that our approach significantly reduces queuing delays for critical data while sustaining high overall throughput, outperforming conventional time-sharing methods. Additionally, we examine the impact of blockage, beam misalignment, and beamwidth adaptation on system performance. These results demonstrate that our scheme effectively balances reliability and throughput under challenging conditions, while also underscoring the need for robust beamforming techniques to mitigate the impact of misalignment in RIS-assisted channels.

Realization of Reconfigurable Intelligent Surfaces with Space-Time Coded Metasurfaces

This paper presents experimental realization of a reconfigurable intelligent surface (RIS) using space-time coding metasurfaces to enable concurrent beam steering and data modulation. The proposed approach harnesses the capabilities of metasurfaces, allowing precise temporal control over individual unit cells of the RIS. We show that by employing proper binary codes manipulating the state of unit cells, the RIS can act as a digital data modulator with beam steering capability. We describe the experimental setup and computational tools, followed by validation through harmonic generation and investigation of beam steering and data modulation. Additionally, four digital modulation schemes are evaluated. By implementing customized binary codes, constellations under varying conditions are compared, showcasing the potential for real-world applications. This study offers new insights into the practical implementation of RIS for advanced wireless communication systems.

A Structural Analysis of the User Behavior Dynamics for Environmentally Sustainable ICT

The sector of information and communication technology (ICT) can contribute to the fulfillment of the Paris agreement and the sustainable development goals (SDGs) through the introduction of sustainability strategies. For environmental sustainability, such strategies should contain efficiency, sufficiency, and consistency measures. To propose such, a structural analysis of ICT is undertaken in this manuscript. Thereby, key mechanisms and dynamics behind the usage of ICT and the corresponding energy and resource use are analyzed by describing ICT as a complex system. The system contains data centers, communication networks, smartphone hardware, apps, and the behavior of the users as sub-systems, between which various Morinian interactions are present. Energy and non-energy resources can be seen as inputs of the system, while e-waste is an output. Based on the system description, we propose multiple measures for efficiency, sufficiency and consistency to reduce greenhouse gas emissions and other environmental impacts.

Optimizing Energy Efficiency with RSMA: Balancing Low and High QoS Requirements

Future wireless systems are expected to deliver significantly higher quality-of-service (QoS) albeit with fewer energy resources for diverse, already existing and also novel wireless applications. The optimal resource allocation for a system in this regard could be investigated by reducing the overall power available at the expense of reduced QoS for the inefficient users. In other words, we maximize the system energy efficiency by achieving power saving through a minimal back-off in terms of QoS. In this paper, we investigate the energy efficiency vs. delivered QoS trade-off for the rate-splitting multiple access (RSMA) assisted downlink system. We first determine the user grouping with a normalised channel similarity metric so as to allow a large number of users with non-zero achievable private message rates. Through the private message removal (PMR) of these users, we aim to investigate the QoS vs. energy efficiency trade-off. Numerical results indicate a peak of ~$10\%$ increase in the network energy efficiency for the proposed normalised channel similarity metric based user grouping with scheduled PMR.

On Neural-Network Representation of Wireless Self-Interference for Inband Full-Duplex Communications

Neural network modeling is a key technology of science and research and a platform for deployment of algorithms to systems. In wireless communications, system modeling plays a pivotal role for interference cancellation with specifically high requirements of accuracy regarding the elimination of self-interference in full-duplex relays. This paper hence investigates the potential of identification and representation of the self-interference channel by neural network architectures. The approach is promising for its ability to cope with nonlinear representations, but the variability of channel characteristics is a first obstacle in straightforward application of data-driven neural networks. We therefore propose architectures with a touch of "adaptivity" to accomplish a successful training. For reproducibility of results and further investigations with possibly stronger models and enhanced performance, we document and share our data.

RIS-Aided Bistatic Radar for Rapid NLOS Sensing in the Teraharetz Band

In this paper, we investigate a non-lineof-sight (NLOS) sensing problem at terahertz frequencies. To be able to observe the targets shadowed by a blockage, we propose a method using reconfigurable intelligent surfaces (RIS). We employ a bistatic radar system and scan the obstructed area with RIS using hierarchical codebooks (HCB). Moreover, we propose an iterative maximum likelihood estimation (MLE) scheme to yield the optimum sensing accuracy, converging to Cramer-Rao lower bound (CRLB). We take band-specific effects such as diffraction and beam squint into account and show that these effects are relevant factors affecting localization performance in RIS-employed radar setups. The results show that under NLOS conditions, the system can still localize all the targets with very good accuracy using the RIS. The initial estimates obtained by the HCBs can provide centimeter-level accuracy, and when the optimal performance is needed, at the cost of a few extra transmissions, the proposed iterative MLE method improves the accuracy to sub-millimeter accuracy, yielding the position error bound.

Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection

The convergence of communication and computation, along with the integration of machine learning and artificial intelligence, stand as key empowering pillars for the sixth-generation of communication systems (6G). This paper considers a network of one base station serving a number of devices simultaneously using spatial multiplexing. The paper then presents an innovative deep learning-based approach to simultaneously manage the transmit and computing powers, alongside computation allocation, amidst uncertainties in both channel and computing states information. More specifically, the paper aims at proposing a robust solution that minimizes the worst-case delay across the served devices subject to computation and power constraints. The paper uses a deep neural network (DNN)-based solution that maps estimated channels and computation requirements to optimized resource allocations. During training, uncertainty samples are injected after the DNN output to jointly account for both communication and computation estimation errors. The DNN is then trained via backpropagation using the robust utility, thus implicitly learning the uncertainty distributions. Our results validate the enhanced robust delay performance of the joint uncertainty injection versus the classical DNN approach, especially in high channel and computational uncertainty regimes.

Dynamic Rate Splitting Grouping for Antifragile Responses to Wireless Network Disruptions

The reliance on wireless network architectures for applications demanding high reliability and fault tolerance is growing. These architectures heavily depend on wireless channels, making them susceptible to impairments and blockages. Ensuring functionality, particularly for safety-critical applications, demands robust countermeasures at the physical layer. In response, this work proposes the utilization of a dynamic Rate Splitting (RS) grouping approach as a resilience mechanism during blockages. RS effectively manages interference within networks but faces challenges during outages and blockages, where system performance can deteriorate due to the lowest decoding rate dictating the common rate and increased interference from fewer available channel links. As a strategic countermeasure, RS is leveraged to mitigate the impact of blockages, maintaining system efficiency and performance amidst disruptions. In fact, the introduction of new RS groups enables the exploration of novel solutions to the resource allocation problem, potentially outperforming those adopted before the occurrence of a blockage. As it turns out, by employing the dynamic RS grouping, the network exhibits an antifragile recovery response, showcasing the network's ability to not only recover from disruptions but also surpass its initial performance.

Show Me the Way: Real-Time Tracking of Wireless Mobile Users with UWB-Enabled RIS

The integration of Reconfigurable Intelligent Surfaces (RIS) in 6G wireless networks offers unprecedented control over communication environments. However, identifying optimal configurations within practical constraints remains a significant challenge. This becomes especially pronounced, when the user is mobile and the configurations need to be deployed in real time. Leveraging Ultra-Wideband (UWB) as localization technique, we capture and analyze real-time movements of a user within the RIS-enabled indoor environment. Given this information about the system's geometry, a model-based optimization is utilized, which enables real-time beam steering of the RIS towards the user. However, practical limitations of UWB modules lead to fluctuating UWB estimates, causing the RIS beam to occasionally miss the tracked user. The methodologies proposed in this work aim to increase the compatibility between these two systems. To this end, we provide two key solutions: beam splitting for obtaining more robust RIS configurations and UWB estimation correction for reducing the variations in the UWB data. Through comprehensive theoretical and experimental evaluations in both stationary and mobile scenarios, the effectiveness of the proposed techniques is demonstrated. When combined, the proposed methods improve worst-case tracking performance by a significant 17.5dB compared to the conventional approach.