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.

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.

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric (radio frequency) fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In this paper, we prove analytically and experimentally that these solutions are highly vulnerable to impersonation attacks. We also demonstrate that such a unique device-specific signature can be abused to track devices, thus violating privacy. We propose RF-Veil, a radiometric fingerprinting solution that is not only robust against impersonation attacks but also effective in protecting privacy by obfuscating the radiometric fingerprint of the transmitter for non-legitimate receivers. Specifically, we introduce a randomized pattern of phase errors to the transmitted signal such that only the intended receiver can extract the original fingerprint of the transmitter. In a series of experiments and analyses, we expose the vulnerability of adopting naive randomization to statistical attacks and introduce countermeasures. Finally, we show the efficacy of RF-Veil experimentally in protecting user privacy and enhancing security. More importantly, our proposed solution allows communicating with other devices, which do not employ RF-Veil.

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 × 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 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).

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.

The directionality of millimeter-Wave (mm-Wave) communication results in challenging network dynamics and thus complex system design. A key problem with such networks is human blockage, which is highly detrimental since absorption at mm-Wave frequencies is extremely high. This poses a significant challenge for the state-of-the-art technologies in 5G networks such as Device-to-Device (D2D) communication. Essentially, the aforementioned dynamics hinder direct communication between devices. Existing protocols in the mm-Wave band such as IEEE 802.11ad address this problem using relays. However, the complexity relay discovery in these protocols grows linearly with the number of users, Hence, these approaches are infeasible for crowded areas such as malls or busy pedestrian streets. In this paper, we present a lightweight relaying mechanism called Opp-Relay that builds on the existing D2D features of the 3GPP standard to opportunistically discover an mm-Wave enabled relay. Specifically, we provide an algorithm to compute the optimal beamwidth for opportunistic discovery of a relay in dense and dynamic network environments. We validate our approach in practice using our experimental testbed operating in the 60 GHz band. Our experiments demonstrate that choosing a suitable beamwidth to discover and communicate with a relay node is crucial. Moreover, we show that our relaying mechanism significantly reduces the complexity of relay discovery.

At vehicular speeds, the contact time during which a mobile node is in range of a fixed road side unit (RSU) is short. While this is not an issue if the RSU only needs to deliver textual information such as traffic updates, short contact times become problematic when transmitting a large amount of information. For instance, an RSU may need to deliver high volumes of local navigation data for an augmented reality application, or video material regarding tourist information of a nearby town. Millimeter-wave (mm-Wave) communication is highly promising for such scenarios since it provides order-of-magnitude larger throughput than the existing technologies operating at lower frequencies. However, the contact time in mm-Wave vehicular scenarios becomes even shorter due to the directional nature of the communication. This raises a fundamental question: can the high throughput of mm-Wave make up for the reduction in the contact time? In this paper, we analyze this trade-off and design a first-of-its-kind practical mm-Wave vehicular testbed to evaluate the resulting performance. Specifically, we consider alternative locations for the RSU other than at the side of the road, such as on top of a bridge or inside a roundabout. Moreover, we leverage that the road implicitly determines the direction in which the RSU expects a car to be located. This allows us to use fixed beam-steering at both the car and the RSU, thus avoiding costly beam-training. We validate our approach in real-world vehicular scenarios with actual traffic in a mid-sized town in Spain. The results show that our fixed beam-steering approach enables the RSU to transmit large amounts of data in a very short amount of time for a wide range of speeds. This allows us to provide detailed insights into the aforementioned fundamental question regarding the use of mm-Wave in vehicular scenarios.

The characteristics of two key communication technologies in 5G, namely, D2D and mmWave, are complementary. While D2D facilitates the communication of nearby mobile nodes, mmWave provides very high throughput short-range links by using carrier frequencies beyond 30 GHz, and reduces interference by using directional communication. This directly addresses two critical issues in cellular networks, namely, the increasing number of users and the high throughput requirements. In this article, we explore the above symbiosis of D2D and mmWave. More precisely, we integrate mmWave communications into the 3GPP framework for D2D communication, that is, ProSe. To this end, we design the message exchange among entities in the ProSe architecture to support the discovery, establishment, and maintenance of mmWave links. Further, we evaluate the performance of an mmWave D2D system for the case of a picocell operating in the 60 GHz band. We experimentally analyze the benefits of combining D2D and 60 GHz communication. Our results show that this combination improves performance in terms of throughput by up to 2.3 times.