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.

Wi-Fi sensing has emerged as a versatile tool for tasks such as localization, gesture recognition, and vital-sign monitoring, enabling applications from smart environments to personalized healthcare. However, sensing accuracy often significantly degrades when pretrained models are deployed across different commodity receivers. We present the first systematic comparison of Channel State Information (CSI) across diverse Commercial Off-The-Shelf Wi-Fi sensing platforms. Using a unified experimental setup delivering precisely precoded signals simultaneously to multiple receivers, we isolate receiver-specific variability. We find that dominant cross-device differences arise from Automatic Gain Control and consistent subcarrier non-linearities. We propose a simple gain-alignment preprocessing step, recovering most of the lost accuracy (up to 75%) in cross-device Human Activity Recognition model deployments. Without preprocessing, model accuracy sharply drops—effectively breaking practical deployments. Additional analyses reveal measurable inherent differences in receiver faithfulness, sensitivity and noise. While these receiver-induced differences do not significantly affect robust sensing tasks such as Human Activity Recognition, they become relevant in scenarios demanding high precision (e.g., single-shot time of flight). Our findings demonstrate that cross-device variability in CSI is real but manageable, and we provide tools and guidelines for robust, hardware-agnostic Wi-Fi sensing.

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.

WiFi sensing relies heavily on blackbox 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 (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 ($\leq 1\%$) to the model accuracy.

liquid crystal (LC) technology is a promising hardware solution for realizing extremely large reconfigurable intelligent surfaces (RISs) due to its advantages in cost-effectiveness, scalability, energy efficiency, and continuous phase shift tunability. However, the slow response time of the liquid crystal (LC)-RIS phase shifters, especially in comparison to the silicon-based alternatives like radio frequency switches and positive-intrinsic-negative (PIN) diodes, limits the performance. This limitation becomes particularly relevant in time-division multiple-access (TDMA) applications where RIS must sequentially serve users in different locations, as the phase-shifting response time of LC-RIS phase shifters can constrain system performance. This paper addresses the slow phase-shifting limitation of LC by developing a physics-based model for the time response of an LC unit cell and proposing a novel phase-shift design framework to reduce the transition time. Specifically, exploiting the fact that LC-RIS at millimeter wave (mmWave) bands have a large number of elements, we optimize the LC phase shifts based on user locations, eliminating the need for full channel state information (CSI) and minimizing reconfiguration overhead. Moreover, instead of focusing on a single point, the RIS phase shifters are designed to optimize coverage over an area. This enhances communication reliability for mobile users and mitigates performance degradation due to user location estimation errors. The proposed RIS phase-shift design minimizes the transition time between configurations, a critical requirement for TDMA schemes. Our analysis reveals that the impact of RIS reconfiguration time on system throughput becomes particularly significant when TDMA intervals are comparable to the reconfiguration time. In such scenarios, optimizing the phase-shift design helps mitigate performance degradation while ensuring specific quality of service requirements. Moreover, the proposed algorithm has been tested through experimental evaluations, which demonstrate that it also performs effectively in practice.

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 map, 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.

This work investigates a practical reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where a subset of RIS elements fail to function properly and reflect incident signals randomly towards unintended directions with attenuation, thereby degrading system performance. To date, no study has addressed such impairments caused by faulty RIS elements in ISAC systems. This work aims to fill the gap. First, to quantify the impact of faulty elements on ISAC performance, we derive the misspecified Cramér-Rao bound (MCRB) for sensing parameter estimation and signal-to-interference-and-noise ratio (SINR) for communication quality. Then, to mitigate the performance loss caused by faulty elements, we jointly design the remaining functional RIS phase shifts and transmit beamforming to minimize the MCRB, subject to the communication SINR and transmit power constraints. The resulting optimization problem is highly non-convex due to the intricate structure of the MCRB expression and constant-modulus constraint imposed on RIS. To address this, we reformulate it into a more tractable form and propose a block coordinate descent (BCD) algorithm that incorporates majorization-minimization (MM), successive convex approximation (SCA), and penalization techniques. Simulation results demonstrate that our proposed approach reduces the performance loss by 21.25% on average compared to the baseline where the presence of faulty elements is ignored. Furthermore, the performance gain becomes more evident as the number of faulty elements increases.

The reflecting antenna elements in most reconfigurable intelligent surfaces (RISs) use semiconductor-based (e.g., positive-intrinsic-negative (PIN) diodes and varactors) phase shifters. Although effective, a drawback of this technology is the high power consumption and cost, which become particularly prohibitive in millimeter-wave (mmWave)/sub-Terahertz range. With the advances in Liquid Crystals (LCs) in microwave engineering, we have observed a new trend in using LC for realizing phase shifter networks of RISs. LC-RISs are expected to significantly reduce the fabrication costs and power consumption. However, the nematic LC molecules are sensitive to temperature variations. Therefore, implementing LC-RIS in geographical regions with varying temperatures requires temperature-resilient designs. The mentioned temperature variation issue becomes more significant at higher temperatures as the phase shifter range reduces in warmer conditions, whereas it expands in cooler ones. In this paper, we study the impact of temperature on the operation of LC-RISs and develop a temperature-resilient phase shift design. Specifically, we formulate a max-min signal-to-interference-plus-noise ratio optimization for a multi-user downlink mmWave network that accounts for the impact of temperature in the LC-RIS phase shifts. The simulation results demonstrate a significant improvement for the considered set of parameters when using our algorithm compared to the baseline approach, which neglects the temperature effects.

Liquid crystal (LC) technology enables low-power and costeffective solutions for implementing the reconfigurable intelligent surface (RIS). However, the phase-shift response of LC-RISs is temperaturedependent, which, if unaddressed, can degrade the performance. This issue is particularly critical in applications such as secure communications, where variations in phase-shift response may lead to significant information leakage. In this paper, we consider secure communication through an LC-RIS and developed a temperature-aware algorithm adapting the RIS phase shifts to thermal conditions. Our simulation results demonstrate that the proposed algorithm significantly improves the secure data rate compared to scenarios where temperature variations are not accounted for.

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.

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.

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.

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.

Mobility management is a major challenge for millimeter-wave (mmWave) cellular networks. In particular, directional beamforming in mmWave devices renders high-speed mobility support very complex. This complexity, however, is not limited to system design but also the performance estimation and evaluation. Hence, some have turned their attention to stochastic modeling of mmWave vehicular communication to derive closed-form expressions that can characterize the coverage and rate behavior of the network. In this article, we model and analyze the beam management for mmWave vehicular networks. To the best of our knowledge, this is the first work that goes beyond coverage and rate analysis. Specifically, we focus on a multi-lane divided highway scenario in which base stations and vehicles are present on both sides of the highway. In addition to providing analytical expressions for the average number of beam switching and handover events, we provide design insights for the operators to fine-tune their network through more informed choice of system parameters, including the number of resources dedicated to channel feedback and beam alignment operations.

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.

In this poster, we present the Reconfigurable Intelligent Surface (RIS) that we designed, built, and tested. At first, the RIS technology is briefly discussed, subsequently, our prototype details are explained, and finally, we conclude by showing the obtained test results. Our RIS design comprises arrays of patch antennas, delay lines, and programmable radio-frequency (RF) switches that enable almost-passive 3D beamforming, i.e., without active RF components.

Industry 4.0 relies heavily on wireless technologies. Energy efficiency and device cost have played a significant role in the initial design of such wireless systems for industry automation. However, high reliability, high throughput, and low latency are also key for certain sectors such as the manufacturing industry. In this sense, existing wireless solutions for industrial settings are limited. Emerging technologies such as millimeter-wave (mmWave) communication are highly promising to address this bottleneck. Still, the propagation characteristics at such high frequencies in harsh industrial settings are not well understood. Related work in this area is limited to isolated measurements in specific scenarios. In this work, we carry out an extensive measurement campaign in highly representative industrial environments. Most importantly, we derive the statistical link-level distributions of the channel parameters of widely accepted mmWave channel model of IEEE 802.11 ad that fit these environments. This model can be beneficial to understand the performance of mmWave systems in typical industrial settings. Beyond analyzing and discussing the insights, with this article we also share our extensive dataset with the community.

IoT devices penetrate different aspects of our life including critical services, such as health monitoring, public safety, and autonomous driving. Such safety-critical IoT systems often consist of a large number of devices and need to withstand a vast range of known Denial-of-Service (DoS) network attacks to ensure a reliable operation while offering low-latency information dissemination. As the first solution to jointly achieve these goals, we propose LIDOR, a secure and lightweight multihop communication protocol designed to withstand all known variants of packet dropping attacks. Specifically, LIDOR relies on an end-to-end feedback mechanism to detect and react on unreliable links and draws solely on efficient symmetric-key cryptographic mechanisms to protect packets in transit. We show the overhead of LIDOR analytically and provide the proof of convergence for LIDOR which makes LIDOR resilient even to strong and hard-to-detect wormhole-supported grayhole attacks. In addition, we evaluate the performance via testbed experiments. The results indicate that LIDOR improves the reliability under DoS attacks by up to 91% and reduces network overhead by 32% compared to a state-of-the-art benchmark scheme.

This paper proposes a new device-to-device edge computing and networks (D2D-ECN) framework which facilitates low-latency execution of real-time Internet-of Things applications through computation offloading with minimal overhead. Our framework accounts for key challenges of D2D-ECN in terms of the efficiency of the resource management and the resulting security concerns caused by lacking trustworthy between task owners and resource providers. In particular, we propose to use a blockchain-empowered framework for implementing resource trading and task assigment as the smart contracts. However, the existing Proof-of-Work (PoW) is impractical for the resource-constrained IoT devices due to high computational complexity of the mining process. Thus, we present a reputation-based consensus mechanism called proof-of-reputation (PoR), where the device with the highest reputation score is responsible for packaging the resource transactions and reputation records in the blockchain. Furthermore, we evaluate the reputation score of each device according to the current computation performance and history reputation. Security, feasibility analysis and numerical results show that our proposed computation offloading scheme can be deployed in the decentralized D2D-ECN system safely and effectively.

The complexity of the mode selection and resource allocation (MS&RA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MS&RA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MS&RA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because:$(i)$they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or$(ii)$they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least$1.8\times $higher throughput, and the highest flexibility against variability or link failures in the system.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios, and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely FML, with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates received data to learn from and adapt to its environment. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 minutes of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes such as blockage and traffic.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

Experimental setups for cellular communications have always been a rare commodity in academia. The advent of software-defined radios (SDRs) paved the way for researchers to prototype their ideas on real hardware. However, existing SDR platforms and their associated reference design codes mostly provide basic cellular functionality with limitations such as low numbers of users and computational capacity. In this demo, we demonstrate the first SDR-based testbed for inband D2D communications using LabVIEW Communications and the USRP hardware platform. Furthermore, we implement a light-weight quality-aware scheduler which adaptively switches communication links from D2D to cellular and vice versa.

We introduce a channel-opportunistic architecture that enhances the user experience in terms of throughput, fairness, and energy efficiency. Our proposed architecture leverages D2D communication and it is built on top of the forthcoming D2D features of 5G networks. In particular, we focus on outband D2D where cellular users are allowed to exploit both cellular (i.e., LTE-A) and WLAN (i.e., WiFi Direct) technologies to establish a D2D connection. In this architecture, cellular users form clusters, in which only the user with the best channel condition communicates with the base station on behalf of the entire cluster. Within the cluster, the unlicensed spectrum is utilized to relay traffic. In this article, we provide analytical models for the proposed system and study the impact of several payoff distribution methods commonly adopted in the literature on coalitional game theory. We then introduce an operator-controlled relay protocol based on the D2D features of LTE-A and WiFi Direct, and demonstrate the feasibility and the advantages of D2D-assisted cellular communication with our SDR prototype.

Device-to-Device (D2D) communications represent a paradigm shift in cellular networks. In particular, analytical results on D2D performance for offloading and relay are very promising, but no experimental evidence validates these results to date. This paper is the first to provide an experimental analysis of outband D2D relay schemes. Moreover, we design D2D opportunistic relay with QoS enforcement (DORE), a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in the presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for relaying in a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

Device-to-Device communications represent a paradigm shift in cellular networks. Analytical results on D2D performance are very promising, but there is no experimental evidence that validates these results to date. This paper is the first to provide an experimental analysis of outband D2D schemes. Moreover, we design DORE, a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

Opportunistic schedulers such as MaxRate and Proportional Fair are known for trading off between throughput and fairness of users in cellular networks. In this paper, we propose a novel solution that integrates opportunistic scheduling design principles and cooperative D2D communication capabilities in order to maximize fairness without sacrificing throughput. Specifically, we develop a mathematical approach and design a smart tie-breaking scheme which maximizes the fairness achieved by the MaxRate scheduler. However, our approach could be applied to improve fairness of any scheduler. In addition, we show that users that cooperatively form D2D clusters benefit from both higher throughput and fairness. Our scheduling scheme is simple to implement, scales linearly with the number of clusters, and is able to double the throughput of Equal Time schedulers and to outperform by 20% or more Proportional Fair schedulers, while providing a user fairness index comparable to or better than Proportional Fair.

The significant growth in mobile data traffic and the ever- increasing user's demand for high-speed, always connected networks continue challenging network providers and lead research towards solutions to enable faster, scalable and more flexible networks. In this paper we present the CROWD approach, a networking framework providing mechanisms to tackle the high densification and heterogeneity of wireless networks. The goal of CROWD is to design protocols and algorithms for very dense and heterogeneous wireless networks, which we call DenseNets. The mechanisms we propose include energy efficiency, MAC enhancements, connectivity management and backhaul configuration to contribute to the next generation of networks considering density as a resource instead of as an obstacle.

In this paper, we propose Floating Band D2D, an adaptive framework to exploit the full potential of Device-to-Device (D2D) transmission modes. We show that inband and outband D2D modes exhibit different pros and cons in terms of complexity, interference, and spectral efficiency. Moreover, none of these modes is suitable as a one-size-fits-all solution for today's cellular networks, due to diverse network requirements and variable users' behavior. Therefore, we unveil the need for going beyond traditional single-band mode-selection schemes. Specifically, we model and formulate a general and adaptive multi-band mode selection problem, namely Floating Band D2D. The problem is NP-hard, so we propose simple yet effective heuristics. Our results show the superiority of the Floating Band D2D framework, which dramatically increases network utility and achieves near complete fairness.

Opportunistic scheduling was initially proposed to exploit user channel diversity for network capacity enhancement. However, the achievable gain of opportunistic schedulers is generally restrained due to fairness considerations which impose a tradeoff between fairness and throughput. In this paper, we show via analysis and numerical simulations that opportunistic scheduling not only increases network throughput dramatically, but also increases energy efficiency and can be fair to the users when they cooperate, in particular by using D2D communications. We propose to leverage smartphone's dual-radio interface capabilities to form clusters among mobile users. We design simple, scalable and energy-efficient D2D-assisted opportunistic strategies, which would incentivize mobile users to form clusters. We use a coalitional game theory approach to analyze the cluster formation mechanism, and show that proportional fair-based intra-cluster payoff distribution brings significant incentive to all mobile users regardless of their channel quality.