WiFi sensing relies heavily on blackbox machine learning [Machine Learning (ML)] models due to the large feature space and complexity. Despite achieving very high accuracies in complex scenarios, the blackbox nature of these ML-based sensing techniques is commonly criticized. This is in fact a major source of mistrust as these models provide very little explanation supporting their decision, while often handling critical applications (e.g., elderly monitoring). In this paper, we investigate explainable artificial intelligence [eXplainable Artificial Intelligence (XAI)] techniques to shed light on the decisions and behaviors of such blackbox models. Specifically, we propose eXSense, a workflow designed based on state-of-the-art XAI techniques to analyze the behavior of blackbox models both locally and globally. To demonstrate its potential, we conduct an extensive analysis on two case studies from the recent sensing literature. Finally, leveraging the insights obtained from our analysis, we propose and evaluate changes to these models, thus enhancing their efficiency and reliability. This includes reducing the feature space by at least 80% with no/minimal loss (≤q 1%≤1%) to the model accuracy.

Reconfigurable intelligent surfaces (RISs) have emerged as a key technology for dynamically reshaping wireless propagation, enhancing coverage and mitigating blockages to enable more pervasive network connectivity. However, implementing RISs at high frequencies remains challenging due to the cost and power demands of semiconductor-based components. To address these critical limitations, liquid crystals (LCs) technology has been identified as a promising low-cost and low-power alternative, giving rise to LC-RIS. The central challenge of this technology, however, lies in its limited responsiveness, as the slow molecular dynamics of LCs lead to long phase-shift reconfiguration times that restrict practicality. This paper presents LiquiRIS, a novel framework that enables substantially faster phase shifting in LC-RIS. By explicitly incorporating the physical dynamics of LC molecules into the phase-shift configuration process, LIQUIRIS intelligently selects phase transitions that minimize the overall reconfiguration time. As a result, LIQUIRIS achieves up to 71.61% reduction in overall reconfiguration time compared to conventional schemes, significantly improving the feasibility of LC-RIS deployment. The proposed framework is further validated through experiments on a mmWave LC-RIS prototype.

Millimeter-wave (mmWave) communication systems utilize phased-array antennas to generate highly directional beams, effectively reducing the signal footprint. Nonetheless, eavesdropping, particularly within the main-lobe, remains a significant concern. This paper introduces BeamSec, a novel beam hopping approach to maximize absolute secrecy rates with no information about the channel state information (CSI) or location of the eavesdroppers. Methodologically, BeamSec identifies diverse beam-pairs between transceivers by analyzing signal characteristics, such as angle of departure (AoD) and angle of arrival (AoA). To prevent the secure message from being eavesdropped, BeamSec splits and jointly encodes data among selected beams. Moreover, BeamSec optimizes secrecy by adapting time allocation across selected beams under different levels of channel knowledge, namely (i) full/-partial radio frequency (RF) maps constructed based on the empirical data of legitimate users, (ii) knowledge of the room floor plan, and (iii) only the instantaneous knowledge of the legitimate transmitter (TX)-receiver (RX) channel. Furthermore, we experimentally validate the efficiency of the proposed schemes using an 802.11ad-compatible 60 GHz phased-array testbed. Specifically, BeamSec demonstrates a non-zero absolute secrecy rate even for the simplistic uniform time allocation approach. Radio map (partial channel knowledge) and known room geometry (instantaneous TX/RX) based schemes provide further improvement of 124.8% and 58.13%, respectively, as compared to uniform time allocation.

The rapid growth of space-based services has established Low Earth Orbit (LEO) satellite networks as a promising option for global broadband connectivity. Next-generation LEO networks leverage inter-satellite links (ISLs) to provide faster and more reliable communications compared to traditional bent-pipe architectures, even in remote regions. However, the high mobility of satellites, dynamic traffic patterns, and potential link failures pose significant challenges for efficient and resilient routing. To address these challenges, we model the LEO satellite network as a time-varying graph comprising a constellation of satellites and ground stations. Our objective is to minimize a weighted sum of average delay and packet drop rate. Each satellite independently decides how to distribute its incoming traffic to neighboring nodes in real time. Given the infeasibility of finding optimal solutions at scale, due to the exponential growth of routing options and uncertainties in link capacities, we propose SKYLINK, a novel fully distributed learning strategy for link management in LEO satellite networks. SKYLINK enables each satellite to adapt to the time-varying network conditions, ensuring real-time responsiveness, scalability to millions of users, and resilience to network failures, while maintaining low communication overhead and computational complexity. To support the evaluation of SKYLINK at global scale, we develop a new simulator for large-scale LEO satellite networks. For 25.4 million users, SKYLINK reduces the weighted sum of average delay and drop rate by 29% compared to the bent-pipe approach, and by 92% compared to Dijkstra. It lowers drop rates by 95% relative to k-shortest paths, 99% relative to Dijkstra, and 74% compared to the bent-pipe baseline, while achieving up to 46% higher throughput. At the same time, SKYLINK maintains constant computational complexity with respect to constellation size.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing Key Performance Indicators (KPIs) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing the average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in latency.

In this paper, we share our experience in designing, prototyping, and empirically characterizing RF Switch-based Reconfigurable Intelligent Surfaces (RIS). Our RIS design consists of arrays of patch antennas, delay lines, and programmable radio-frequency (RF) switches, enabling 3D beamforming passively, without active RF components. Our design introduces two key innovations: (i) a modular structure that provides scalability for sustainable deployments, and (ii) the support for 3-bit phase shifters, enabling high-spatial resolution codebooks. We realized this design through PCB technology and affordable electronic components, then rigorously validated our prototype in a controlled setting. With this paper, we make a comprehensive characterization of our RIS publicly available via a large dataset, to promote further empirical-driven research on this topic. Finally, we present a cost analysis of our design, which underscores the importance of sustainable practices in shaping the future of wireless technologies.

In this article, we present two datasets that we make publicly available for research. The data is collected in a testbed comprised of a custom-made reconfigurable intelligent surface (RIS) prototype and two regular orthogonal frequency-division multiplexing (OFDM) transceivers within an anechoic chamber. First, we discuss the details of the testbed and equipment used, including insights about the design and implementation of our RIS prototype. We further present the methodology we employ to gather measurement samples, which consists of letting the RIS electronically steer the signal reflections from an OFDM transmitter toward a specific location. To this end, we evaluate a suitably designed configuration codebook and collect measurement samples of the received power with an OFDM receiver. Finally, we present the resulting datasets, their format, and examples of exploiting this data for research purposes.

Liquid crystal (LC) technology offers a cost-effective, scalable, energy-efficient, and continuous phase tunable realization of extremely large reconfigurable intelligent surfaces (RISs). However, LC response time to achieve a desired differential phase is significantly higher compared to competing silicon-based technologies (RF switches, PIN diodes, etc). The slow response time can be the performance bottleneck for applications where frequent reconfiguration of the RIS (e.g., to serve different users) is needed. In this paper, we develop an RIS phase-shift design that is aware of the transition behavior and aims to minimize the time to switch among multiple RIS configurations each serving a mobile user in a time-division multiple-access (TDMA) protocol. Our simulation results confirm that the proposed algorithm significantly reduces the time required for the users to achieve a threshold signal quality. This leads to a considerable improvement in the achievable throughput for applications, where the length of the TDMA time intervals is comparable with the RIS reconfiguration time.

— Wi-Fi sensing applications have achieved remarkable results over the last decade, offering accurate device-free localization and gesture recognition capabilities. Indeed, Wi-Fi sensing has quickly become a critical field of research for future communication systems under the paradigm known as joint communication and sensing. However, device-free wireless sensing can also be exploited for malign purposes against unaware victims, and the omnipresence of Wi-Fi transceivers poses a significant threat to people’s privacy. Therefore, it is essential to develop functional solutions that can effectively thwart wireless sensing. All the current attempts to hinder illegitimate wireless sensing rely on specialized hardware deployed in the environment, but their cost and complexity can undermine widespread deployment. In this paper, we explore the possibility of using native capabilities of Wi-Fi systems, namely beamforming, to thwart wireless sensing. To this end, we propose for the first time a solution that enables complete control over the beamforming in commercial Wi-Fi devices. On top of that, we build BeamDancer, which randomizes beamforming vectors to inhibit channel fingerprinting. We empirically demonstrate the effectiveness of the proposed solution against three different wireless sensing techniques, both data-driven and model-based, while preserving almost entirely the legitimate Wi-Fi traffic at the same time.

The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose BeamSec, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper's location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beampairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beampairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that BeamSec outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

Reconfigurable Intelligent Surfaces (RISs) are expected to be massively deployed in future beyond-5th generation wireless networks, thanks to their ability to programmatically alter the propagation environment, inherent low-cost and low-maintenance nature. Indeed, they are envisioned to be implemented on the facades of buildings or on moving objects. However, such an innovative characteristic may potentially turn into an involuntary negative behavior that needs to be addressed: an undesired signal scattering. In particular, RIS elements may be prone to experience failures due to lack of proper maintenance or external environmental factors. While the resulting Signal-to-Noise-Ratio (SNR) at the intended User Equipment (UE) may not be significantly degraded, we demonstrate the potential risks in terms of unwanted spreading of the transmit signal to non-intended UEs. In this regard, we consider the problem of mitigating such undesired effectby proposing two simple yet effective algorithms, which are based on maximizing the Signal-to-Leakage-and-Noise-Ratio (SLNR) over a predefined two-dimensional (2D) area and are applicable in the case of perfect channel-state-information (CSI) and partial CSI, respectively. Numerical and full-wave simulations demonstrate the added gains compared to leakage-unaware and reference schemes.

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.