Evaluating cellular systems, from 5th generation (5G) New Radio (NR) and 5G-Advanced to 6th generation (6G), is challenging because the performance emerges from the tight coupling of propagation, beam management, scheduling, and higher-layer interactions. System-level simulation is therefore indispensable, yet the vast majority of studies rely on the statistical 3rd Generation Partnership Project (3GPP) channel models. These are well suited to capture average behavior across many statistical realizations, but cannot reproduce site-specific phenomena such as comer diffraction, street-canyon blockage, or deterministic line-of-sight conditions and angle-of- departure/arrival relationships that drive directional links.This paper extends 5G-LENA, an NR module for the system-level Network Simulator 3 (ns-3), with a trace-based channel model that processes the Multipath Components (MPCs) obtained from external ray-tracers (e.g., Sionna Ray Tracer (RT)) or measurement campaigns. Our module constructs frequency-domain channel matrices, and feeds them to the existing Physical (PHY)/Medium Access Control (MAC) stack without any further modifications. The result is a geometry-based channel model that remains fully compatible with the standard 3GPP implementation in 5G-LENA, while delivering site-specific geometric fidelity. This new module provides a key building block toward Digital Twin (DT) capabilities by offering realistic site-specific channel modeling, unlocking studies that require site awareness, including beam management, blockage mitigation, and environment-aware sensing. We demonstrate its capabilities for precise beam-steering validation and end-to-end metric analysis. In both cases, the trace-driven engine exposes performance inflections that the statistical model does not exhibit, confirming its value for high- fidelity system-level cellular networks research and as a step toward DT applications.

5G networks provide low-latency, high throughput, and massive connectivity, yet the control plane remains exposed to several security threats. Among the most common and impactful threats are Denial-of-Service (DoS) attacks, with Radio Resource Control (RRC) signaling storms being particularly effective and difficult to mitigate. In this attack, a malicious User Equipment (UE) aims to exhaust Next Generation Node Base (gNB) resources, preventing legitimate UEs from establishing a connection. Existing defenses are typically limited to detection, only evaluated through numerical simulations, and cannot discern between high-load network conditions and attacks. Most of them also assume static setups and do not take mobility into account. In this paper, we first evaluate the feasibility of the signaling storm attack by using the OpenAirInterface(OAI) 5G protocol stack. Then, we propose StormShield, a signaling storm attack detection and mitigation technique implemented as an xApp on an O-RAN Near-Real-Time (near-RT) RAN Intelligent Controller (RIC). It fingerprints and blocks Malicious UEs (MUEs) before gNB resources are exhausted. We prototyped our solution on an Over-The-Air (OTA) testbed with OAI, NVIDIA Aerial, and two different gNB setups. The first one leverages an USRP X410 Software-defined Radio (SDR) with 8.1 functional split; the second a commercial Foxconn Radio Unit (RU) with 7.2 functional split. Our experimental evaluation demonstrates that StormShield effectively prevents gNB resource exhaustion, identifying and blocking MUEs with an average detection accuracy of 97.6% within 106.5 ms from the beginning of the attack.

Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretation, and network optimization. However, their deployment in practice must balance inference cost, latency, and reliability. In this work, we study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline. In it, an efficient edge model handles routine queries, a more capable cloud model addresses complex cases, and human experts are involved only when necessary. We define a misalignment-cost constrained optimization problem, aiming to minimize average processing cost, while guaranteeing alignment of automated answers with expert judgments. We propose a statistically rigorous threshold selection method based on multiple hypothesis testing (MHT) for a query processing mechanism based on knowledge and confidence tests. The approach provides finite-sample guarantees on misalignment risk. Experiments on the TeleQnA dataset –a telecom-specific benchmark – demonstrate that the proposed method achieves superior cost-efficiency compared to conventional cascaded baselines, while ensuring reliability at prescribed confidence levels.

5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this article, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

Modern cellular networks adopt a software-based and disaggregated approach to support diverse requirements and mission-critical reliability needs. While softwarization introduces flexibility, it also increases the complexity of the network architectures, which calls for robust automation frameworks that can deliver efficient and fully-autonomous configuration, scalability, and multi-vendor integration. This paper presents AutoRAN, an automated, intent-driven framework for zero-touch provisioning of open, programmable cellular networks. Leveraging cloud-native principles, AutoRAN employs virtualization, declarative infrastructure-as-code templates, and disaggregated micro-services to abstract physical resources and protocol stacks. Its orchestration engine integrates Large Language Models (LLMs) to translate high-level intents into machine-readable configurations, enabling closed-loop control via telemetry-driven observability. Implemented on a multi-architecture OpenShift cluster with heterogeneous compute (x86/ARM CPUs, NVIDIA GPUs) and multi-vendor Radio Access Network (RAN) hardware (Foxconn, NI), AutoRAN automates deployment of O-RANcompliant stacks-including OpenAirInterface, NVIDIA ARC RAN, Open5GS core, and O-RAN Software Community (OSC) RIC components-using Continuous Integration and Continuous Delivery/Deployment (CI/CD) pipelines. Experimental results demonstrate that AutoRAN is capable of deploying an end-toend Private 5G network in less than 60 seconds with 1.6 Gbps throughput, validating its ability to streamline configuration, accelerate testing, and reduce manual intervention with similar performance than non cloud-based implementations. With its novel LLM-assisted intent translation mechanism, and performanceoptimized automation workflow for multi-vendor environments, AutoRAN has the potential of advancing the robustness of nextgeneration cellular supply chains through reproducible, intentbased provisioning across public and private deployments.

The future mobile network schedulers have the complex mission of distributing radio resources among various applications with different requirements. The radio access network (RAN) slicing enables the creation of different logical networks by using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources among the slices to fulfill their requirements. Several recent studies have proposed advances in machine learning-based RRS. However, these works often evaluate their models under limited scenarios and with minimal slice diversity, raising concerns about their real-world applicability. The generalization capabilities of these models remain uncertain without rigorous testing across diverse network conditions and slice configurations, which may hinder their effectiveness upon deployment in operational networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a RAN slicing context. The proposed method protects high-priority slices when the available radio resources are insufficient, using transfer learning to reduce the number of required training steps. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines.

Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced upon acceptance of this paper and a tutorial for energy efficiency testing in ns-O-RAN.

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems through the near-real-time dynamic activation and deactivation of Base Station (BS) Radio Frequency (RF) frontends using Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimization (PPO) and Deep Q-Network (DQN). These algorithms run on the RAN Intelligent Controllers (RICs), part of the Open RAN architecture, and are designed to make optimal network-level decisions based on historical data without compromising stability and performance. We leverage a rich set of Key Performance Measurements (KPMs), serving as state for the DRL, to create a comprehensive representation of the RAN, alongside a set of actions that correspond to some control exercised on the RF frontend. We extend ns-O-RAN, an open-source, realistic simulator for 5G and Open RAN built on ns-3, to conduct an extensive data collection campaign. This enables us to train the agents offline with over 300,000 data points and subsequently evaluate the performance of the trained models. Results show that DRL agents improve energy efficiency by adapting to network conditions while minimally impacting the user experience. Additionally, we explore the trade-off between throughput and energy consumption offered by different DRL agent designs.

Mission-critical applications (MCA) like smart grid management, first-aid response, and tactical coordination in military search and rescue operations refer to applications that can pose a risk to human lives or cause extensive and catastrophic losses. The deployment and management of these applications need careful consideration to meet the stringent performance demand of resource-constrained environments. One way to achieve such performance and dependability demand is to adopt Message Oriented Middlewares (MOMs) (e.g., Apache Kafka, RabbitMQ) as they enable real-time data analytics and informed decision making. Despite their extensive usage in legacy business intelligent applications, little is known about their suitability for mission-critical applications. This paper fills that gap by first deploying and testing mission-critical applications on Apache Kafka and RabbitMQ. Then, we measure the performance, security, and reliability of the chosen MOMs to support MCA. The evaluation results confirm that Apache Kafka outperforms RabbitMQ, making it a potential candidate to deploy MCA. Specifically, Kafka requires 13x less bandwidth than RabbitMQ, which could be further reduced by 85% using effective parameter tuning. Our findings pave the way for MOMs to be adopted in MCAs to meet their stringent performance and dependability demands.

The traditional black-box and monolithic approach to Radio Access Networks (RANs) has heavily limited flexibility and innovation. The Open RAN paradigm, and the architecture proposed by the O-RAN ALLIANCE, aim to address these limitations via openness, virtualization and network intelligence. In this work, first we propose a novel, programmable scheduler design for Open RAN Distributed Units (DUs) that can guarantee minimum throughput levels to User Equipments (UEs) via configurable weights. Then, we propose an O-RAN xApp that reconfigures the scheduler’s weights dynamically based on the joint Complementary Cumulative Distribution Function (CCDF) of reported throughput values. We demonstrate the effectiveness of our approach by considering the problem of asset tracking in 5G-powered Industrial Internet of Things (IIoT) where uplink video transmissions from a set of cameras are used to detect and track assets via computer vision algorithms. We implement our programmable scheduler on the OpenAirInterface (OAI) 5G protocol stack, and test the effectiveness of our xApp control by deploying it on the O-RAN Software Community (OSC) near-RT RAN Intelligent Controller (RIC) and controlling a 5G RAN instantiated on the Colosseum Open RAN digital twin. Our experimental results demonstrate that our approach enhances the success percentage of meeting throughput requirements by 33% compared to a reference scheduler. Moreover, in the asset tracking use case, we show that the xApp improves the detection accuracy, i.e., the F1 score, by up to 37.04%.

Ultra-dense fifth generation (5G) and beyond networks leverage spectrum sharing and frequency reuse to enhance throughput, but face unpredictable in-band uplink (UL) interference challenges that significantly degrade Signal to Interference plus Noise Ratio (SINR) at affected Next Generation Node Bases (gNBs). This is particularly problematic at cell edges, where overlapping regions force User Equipments (UEs) to increase transmit power, and in directional millimeter wave systems, where beamforming sidelobes can create unexpected interference. The resulting signal degradation disrupts protocol operations, including scheduling and resource allocation, by distorting quality indicators like Reference Signal Received Power (RSRP) and Received Signal Strength Indicator (RSSI), and can compromise critical functions such as channel state reporting and Hybrid Automatic Repeat Request (HARQ) acknowledgments.

The application of small-factor, 5G-enabled Unmanned Aerial Vehicles (UAVs) has recently gained significant interest in various aerial and Industry 4.0 applications. However, ensuring reliable, high-throughput, and low-latency 5G communication in aerial applications remains a critical and underexplored problem. This paper presents the 5th generation (5G) Aero, a compact UAV optimized for 5G connectivity, aimed at fulfilling stringent 3rd Generation Partnership Project (3GPP) requirements. We conduct a set of experiments in an indoor environment, evaluating the UAV’s ability to establish high-throughput, low-latency communications in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions. Our findings demonstrate that the 5G Aero meets the required 3GPP standards for Command and Control (C2) packets latency in both LoS and NLoS, and video latency in LoS communications and it maintains acceptable latency levels for video transmission in NLoS conditions. Additionally, we show that the 5G module installed on the UAV introduces a negligible 1% decrease in flight time, showing that 5G technologies can be integrated into commercial off-the-shelf UAVs with minimal impact on battery lifetime. This paper contributes to the literature by demonstrating the practical capabilities of current 5G networks to support advanced UAV operations in telecommunications, offering insights into potential enhancements and optimizations for UAV performance in 5G networks.

Although the integration between Unmanned Aerial Vehicles (UAVs) and Radio Access Network (RAN) applications is envisioned to enable a variety of new use cases and services, several practical aspects related to autonomous operations over cellular systems are still largely unexplored due to difficulties in testing and validating such integration in the real world. In this paper, we bridge the gap between simulation and real-world applications by introducing a new framework that combines real-world robotic controllers and 5th generation (5G) cellular stacks with channel and flight simulation. We consider a holistic approach where we use ArduPilot as the flight controller and OpenAirInterface (OAI) and srsRAN as the 5G cellular stacks to provide a unified solution for developing and experimenting with UAV s for cellular applications. We utilize ArduPilot Software-in-the-Loop (SITL) to simulate and control the mobility of UAVs, while OAI-RFSim and srsRAN are used to model channel conditions. Our framework is particularly useful for developing data-driven solutions that require (i) a large amount of data collected under realistic operational conditions to learn effective control policies; and (ii) a sandbox and safe testing environment that enables exploration of the action space. By addressing a UAV coverage problem and developing a greedy heuristic, we demonstrate how our framework can be used to create and test algorithms in a simulated environment, showcasing its potential as a bridge to real-world applications.

The development of 6G wireless technologies is rapidly advancing, with the 3rd Generation Partnership Project (3GPP) entering the pre-standardization phase and aiming to deliver the first specifications by 2028. This paper explores the OpenAirInterface (OAI) project, an open-source initiative that plays a crucial role in the evolution of 5G and future 6G networks. OAI provides a comprehensive implementation of 3GPP and O-RAN compliant networks, including Radio Access Network (RAN), Core Network (CN), and software-defined User Equipment (UE) components. This paper details the history and evolution of OAI, its licensing model, and the various projects under its umbrella, such as RAN, the CN, and the Operations, Administration and Maintenance (OAM) projects. It also highlights the development methodology, Continuous Integration/Continuous Delivery (CI/CD) processes, and end-to-end systems powered by OAI. Furthermore, the paper discusses the potential of OAI for 6G research, focusing on spectrum, reflective intelligent surfaces, and Artificial Intelligence (AI)/Machine Learning (ML) integration. The open-source approach of OAI is emphasized as essential for tackling the challenges of 6G, fostering community collaboration, and driving innovation in next-generation wireless technologies.

Open Radio Access Networks (RANs) leverage disaggregated and programmable RAN functions and open interfaces to enable closed-loop, data-driven radio resource management. This is performed through custom intelligent applications on the RAN Intelligent Controllers (RICs), optimizing RAN policy scheduling, network slicing, user session management, and medium access control, among others. In this context, we have proposed dApps as a key extension of the O-RAN architecture into the real-time and user-plane domains. Deployed directly on RAN nodes, dApps access data otherwise unavailable to RICs due to privacy or timing constraints, enabling the execution of control actions within shorter time intervals. In this paper, we propose for the first time a reference architecture for dApps, defining their life cycle from deployment by the Service Management and Orchestration (SMO) to real-time control loop interactions with the RAN nodes where they are hosted. We introduce a new dApp interface, E3, along with an Application Protocol (AP) that supports structured message exchanges and extensible communication for various service models. By bridging E3 with the existing O-RAN E2 interface, we enable dApps, xApps, and rApps to coexist and coordinate. These applications can then collaborate on complex use cases and employ hierarchical control to resolve shared resource conflicts. Finally, we present and open-source a dApp framework based on OpenAirInterface (OAI). We benchmark its performance in two real-time control use cases, i.e., spectrum sharing and positioning in a 5th generation (5G) Next Generation Node Base (gNB) scenario. Our experimental results show that standardized real-time control loops via dApps are feasible, achieving average control latency below 450 microseconds and allowing optimal use of shared spectral resources.

The development of Open Radio Access Network (RAN) cellular systems is being propelled by the integration of Artificial Intelligence (AI) techniques. While AI can enhance network performance, it expands the attack surface of the RAN. For instance, the need for datasets to train AI algorithms and the use of open interface to retrieve data in real time paves the way to data tampering during both training and inference phases. In this work, we propose MalO-RAN, a framework to evaluate the impact of data poisoning on O-RAN intelligent applications. We focus on AI-based xApps taking control decisions via Deep Reinforcement Learning (DRL), and investigate backdoor attacks, where tampered data is added to training datasets to include a backdoor in the final model that can be used by the attacker to trigger potentially harmful or inefficient pre-defined control decisions. We leverage an extensive O-RAN dataset collected on the Colosseum network emulator and show how an attacker may tamper with the training of AI models embedded in xApps, with the goal of favoring specific tenants after the application deployment on the network. We experimentally evaluate the impact of the SleeperNets and TrojDRL attacks and show that backdoor attacks achieve up to a 0.9 attack success rate. Moreover, we demonstrate the impact of these attacks on a live O-RAN deployment implemented on Colosseum, where we instantiate the xApps poisoned with MalO-RAN on an O-RAN-compliant Near-real-time RAN Intelligent Controller (RIC). Results show that these attacks cause an average network performance degradation of 87%.

Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (“fingerprint”) the device itself based on variations introduced in the transmitted waveform. Key impediments in developing robust and scalable RFFDL techniques that are practical in dynamic and mobile environments are the non-stationary behavior of the wireless channel and other impairments introduced by the propagation conditions. To date, the existing RFFDL-based techniques have only been able to demonstrate a desirable performance when the training and testing environment remains the same, which makes the solutions impractical.SignCRFbrings to the RFFDL landscape what it has been missing so far: a scalable, channel-agnostic data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments that isindependent of the dynamic nature of the environment or channel irregularities caused by mobility.SignCRFconsists of: (i) a classifier developed in a base-environment with minimum channel dynamics, and finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator that is carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific “signature”; and (iii) a Max Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance ofSignCRFfor multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices, the largest datasets available in the literature). We assess the scalability ofSignCRFacross various testbed scales by validating our system using small, medium, and large-scale testbeds, with sizes of 5, 20, and 100 devices, respectively. We demonstrate thatSignCRFcan significantly improve the RFFDL performance by achieving as high as 100% correct authentication for WiFi devices and 80% correctly authenticated LoRa devices, a 5x and 8x improvement when compared to the state-of-the-art respectively. Furthermore, we show thatSignCRFis resilient to adversarial actions by reducing the device recognition accuracy from 73% to 6%, which translates into zero mis-authentication of adversary radios that try to impersonate legitimate devices, which has not been achieved by any prior RFFDL techniques.

The O-RAN ALLIANCE is defining architectures, interfaces, operations, and security requirements for cellular networks based on Open Radio Access Network (RAN) principles. In this context, O-RAN introduced the RAN Intelligent Controllers (RICs) to enable dynamic control of cellular networks via data-driven applications referred to as rApps and xApps. RICs enable for the first time truly intelligent and self-organizing cellular networks. However, enabling the execution of many Artificial Intelligence (AI) algorithms making autonomous control decisions to fulfill diverse (and possibly conflicting) goals poses unprecedented challenges. For instance, the execution of one xApp aiming at maximizing throughput and one aiming at minimizing energy consumption would inevitably result in diametrically opposed resource allocation strategies. Therefore, conflict management becomes a crucial component of any functional intelligent O-RAN system. This article studies the problem of conflict mitigation in O-RAN and proposes PACIFISTA, a framework to detect, characterize, and mitigate conflicts generated by O-RAN applications that control RAN parameters. PACIFISTA leverages a profiling pipeline to tests O-RAN applications in a sandbox environment, and combines hierarchical graphs with statistical models to detect the existence of conflicts and evaluate their severity. Experiments on Colosseum and OpenRAN Gym demonstrate PACIFISTA's ability to predict conflicts and provide valuable information before potentially conflicting xApps are deployed in production systems. We use PACIFISTA to demonstrate that users can experience a 16% throughput loss even in the case of xApps with similar goals, and that applications with conflicting goals might cause severe instability and result in up to 30% performance degradation. We also show that PACIFISTA can help operators to identify conflicting applications and maintain performance degradation below a tolerable threshold.

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access Network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven intelligent control loops. Recent work has showed how Deep Reinforcement Learning (DRL) is effective in dynamically controlling O-RAN systems. However, how to design these solutions in a way that manages heterogeneous optimization goals and prevents unfair resource allocation is still an open challenge, with the logic within DRL agents often considered as a opaque system. In this paper, we introduce PandORA, a framework to automatically design and train DRL agents for Open RAN applications, package them as xApps and evaluate them in the Colosseum wireless network emulator. We benchmark 23 xApps that embed DRL agents trained using different architectures, reward design, action spaces, and decision-making timescales, and with the ability to hierarchically control different network parameters. We test these agents on the Colosseum testbed under diverse traffic and channel conditions, in static and mobile setups. Our experimental results indicate how suitable fine-tuning of the RAN control timers, as well as proper selection of reward designs and DRL architectures can boost network performance according to the network conditions and demand. Notably, finer decision-making granularities can improve Massive Machine-Type Communications (mMTC)’s performance by$\sim\! 56\%$and even increase Enhanced Mobile Broadband (eMBB) Throughput by$\sim\! 99\%$.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) nearreal-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multimodal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacolog-ical interventions. To address this challenge, we advocate for predictive systems that provide timely alerts to individuals at risk, enabling them to take precautionary actions. Our framework employs advanced DL techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors, thereby enhancing prediction accuracy. The algorithms are optimized for real-time processing on edge devices, mitigating privacy concerns and minimizing data transmission overhead inherent in cloud-based solutions, ultimately preserving battery energy. Additionally, our system predicts the countdown time to seizures (with 15-minute intervals up to an hour prior to the onset), offering critical lead time for preventive actions. Our multimodal model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.

Next-generation wireless networks target high network availability, ubiquitous coverage, and extremely high data rates for mobile users. This requires exploring new frequency bands, e.g., mmWaves, moving toward ultra-dense deployments in urban locations, and providing ad hoc, resilient connectivity in rural scenarios. The design of the backhaul network plays a key role in advancing how the access part of the wireless system supports next-generation use cases. Wireless backhauling, such as the newly introduced Integrated Access and Backhaul (IAB) concept in 5G, provides a promising solution, also leveraging the mmWave technology and steerable beams to mitigate interference and scalability issues. At the same time, however, managing and optimizing a complex wireless backhaul introduces additional challenges for the operation of cellular systems. This paper presents a strategy for the optimal creation of the backhaul network considering various constraints related to network topology, robustness, and flow management. We evaluate its feasibility and efficiency using synthetic and realistic network scenarios based on 3D modeling of buildings and ray tracing. We implement and prototype our solution as a dynamic IAB control framework based on the Open Radio Access Network (RAN) architecture, and demonstrate its functionality in Colosseum, a large-scale wireless network emulator with hardware in the loop.

The wireless spectrum’s increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-real-time links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT’s wireless environment. We evaluate TwiNet’s performance in two distinct use cases: (i) enhancing Safe Adaptive Data Rate (SADR) systems by assessing risky traffic configurations of UEs, resulting in approximately 15% improved network performance compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, where the DL pipeline achieves up to 97% accuracy by training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

Network virtualization, software-defined infrastructure, and orchestration are pivotal elements in contemporary networks, yielding new vectors for optimization and novel capabilities. In line with these principles, O-RAN presents an avenue to bypass vendor lock-in, circumvent vertical configurations, enable network programmability, and facilitate integrated artificial intelligence (AI) support. Moreover, modern container orchestration frameworks (e.g., Kubernetes, Red Hat OpenShift) simplify the way cellular base stations, as well as the newly introduced RAN Intelligent Controllers (RICs), are deployed, managed, and orchestrated. While this enables cost reduction via infrastructure sharing, it also makes it more challenging to meet O-RAN control latency requirements, especially during peak resource utilization. For instance, the Near-real-time RIC is in charge of executing applications (xApps) that must take control decisions within one second, and we show that container platforms available today fail in guaranteeing such timing constraints. To address this problem, we propose ScalO-RAN, a control framework rooted in optimization and designed as an O-RAN rApp that allocates and scales AI-based O-RAN applications (xApps, rApps, dApps) to: (i) abide by application-specific latency requirements, and (ii) monetize the shared infrastructure while reducing energy consumption. We prototype ScalO-RAN on an OpenShift cluster with base stations, RIC, and a set of AI-based xApps deployed as micro-services. We evaluate ScalO-RAN both numerically and experimentally. Our results show that ScalO-RAN can optimally allocate and distribute O-RAN applications within available computing nodes to accommodate even stringent latency requirements. More importantly, we show that scaling O-RAN applications is primarily a time-constrained problem rather than a resource-constrained one, where scaling policies must account for stringent inference time of AI applications, and not only how many resources they consume.

Fifth generation (5G) and beyond cellular networks are vulnerable to security threats, primarily due to the lack of integrity protection in the Radio Resource Control (RRC) layer. In order to address this problem, we propose a real-time anomaly detection framework that leverages the concept of distributed applications in 5G Open RAN networks. Specifically, we identify Physical Layer (PHY) features that can generate a reliable fingerprint, infer in a novel way the time of arrival of uplink packets lacking integrity protection, and handle cross-layer features. By identifying legitimate message sources and detecting suspicious activities through an Artificial Intelligence (AI) design, we demonstrate that Open RAN-based applications that run at the edge can be designed to provide additional security to the network. Our solution is first validated in extensive emulation environments achieving over 85% accuracy in predicting potential attacks on unseen test scenarios. We then integrate our approach into a real-world prototype with a large channel emulator to assess its real-time performance and costs. Our solution meets the low-latency real-time constraints of 2 ms, making it well-suited for real-world deployments.

Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable latency for real-time operations. In addition, spectrogram-based approaches do not achieve sufficient granularity level as they are based on object detection performed on pixels and are based on rectangular bounding boxes. For this reason, we propose a completely novel approach based on semantic spectrum segmentation, where multiple signals are simultaneously classified and localized in both time and frequency at the I/Q level. Conversely from the state-of-the-art computer vision algorithm, we add non-local blocks to combine the spatial features of signals, and thus achieve better performance. In addition, we propose a novel data generation approach where a limited set of easy-to-collect real-world wireless signals are "stitched together" to generate large-scale, wideband, and diverse datasets. Experimental results obtained on multiple testbeds (including the Arena testbed) using multiple antennas, multiple sampling frequencies, and multiple radios over the course of 3 days show that our approach classifies and localizes signals with a mean intersection over union (IOU) of 96.70% across 5 wireless protocols while performing in real-time with a latency of 2.6 ms. Moreover, we demonstrate that our approach based on non-local blocks achieves 7% more accuracy when segmenting the most challenging signals with respect to the state-of-the-art U-Net algorithm. We will release our 17 GB dataset and code.

Digital Twins (DTs) have become predominant for wireless spectrum emulation thanks to their realistic virtual mod-e~ing to test and optimize wireless network performance, enabling efficient spectrum management. In this work, we propose and demonstrate a bidirectional, near-real-time communication link between real-world wireless spectrum scenarios and DT replicas by utilizing the MQTT protocol. Results show that our link can achieve data transfer times with an average latency of 14 ms, which is suitable for real-time communication, allowing for our DT to be within the required latency threshold for applications such as voice communication, video streaming, and Internet of Thin2:8 (loT) control.

Digital twins are now a staple of wireless networks design and evolution. Creating an accurate digital copy of a real system offers numerous opportunities to study and analyze its performance and issues. It also allows designing and testing new solutions in a risk-free environment, and applying them back to the real system after validation. A candidate technology that will heavily rely on digital twins for design and deployment is 6G, which promises robust and ubiquitous networks for eXtended Reality (XR) and immersive communications solutions. In this paper, we present BostonTwin, a dataset that merges a high-fidelity 3D model of the city of Boston, MA, with the existing geospatial data on cellular base stations deployments, in a ray-tracing-ready format. Thus, BostonTwin enables not only the instantaneous rendering and programmatic access to the building models, but it also allows for an accurate representation of the electromagnetic propagation environment in the real-world city of Boston. The level of detail and accuracy of this characterization is crucial to designing 6G networks that can support the strict requirements of sensitive and high-bandwidth applications, such as XR and immersive communication.

While the availability of large datasets has been instrumental to advance fields like computer vision and natural language processing, this has not been the case in mobile networking. Indeed, mobile traffic data is often unavailable due to privacy or regulatory concerns. This problem becomes especially relevant in Open Radio Access Network (RAN), where artificial intelligence can potentially drive optimization and control of the RAN, but still lags behind due to the lack of training datasets. While substantial work has focused on developing testbeds that can accurately reflect production environments, the same level of effort has not been put into twinning the traffic that traverse such networks.

Network slicing allows Telecom Operators (TOs) to support service provisioning with diverse Service Level Agreements (SLAs). The combination of network slicing and Open Radio Access Network (RAN) enables TOs to provide more customized network services and higher commercial benefits. However, in the current Open RAN community, an open-source end-to-end slicing solution for 5G is still missing. To bridge this gap, we developed ORANSlice, an open-source network slicing-enabled Open RAN system integrated with popular open-source RAN frameworks. ORANSlice features programmable, 3GPP-compliant RAN slicing and scheduling functionalities. It supports RAN slicing control and optimization via xApps on the near-real-time RAN Intelligent Controller (RIC) thanks to an extension of the E2 interface between RIC and RAN, and service models for slicing. We deploy and test ORANSlice on different O-RAN testbeds and demonstrate its capabilities on different use cases, including slice prioritization and minimum radio resource guarantee.

RAN Intelligent Controllers (RICs) are programmable platforms that enable data-driven closed-loop control in the O-RAN architecture. They collect telemetry and data from the RAN, process it in custom applications, and enforce control or new configurations on the RAN. Such custom applications in the Near-Real-Time (RT) RIC are called xApps, and enable a variety of use cases related to radio resource management. Despite numerous open-source and commercial projects focused on the Near-RT RIC, developing and testing xApps that are interoperable across multiple RAN implementations is a time-consuming and technically challenging process. This is primarily caused by the complexity of the protocol of the E2 interface, which enables communication between the RIC and the RAN while providing a high degree of flexibility, with multiple Service Models (SMs) providing plug-and-play functionalities such as data reporting and RAN control. In this paper, we propose xDevSM, an open-source flexible framework for O-RAN service models, aimed at simplifying xApp development for the O-RAN Software Community (OSC) Near-RT RIC. xDevSM reduces the complexity of the xApp development process, allowing developers to focus on the control logic of their xApps and moving the logic of the E2 service models behind simple Application Programming Interfaces (APIs). We demonstrate the effectiveness of this framework by deploying and testing xApps across various RAN software platforms, including OpenAirInterface and srsRAN. This framework significantly facilitates the development and validation of solutions and algorithms on O-RAN networks, including the testing of data-driven solutions across multiple RAN implementations.

Softwarized and programmable Radio Access Networks (RANs) come with virtualized and disaggregated components, increasing the supply chain robustness and the flexibility and dynamism of the network deployments. This is a key tenet of Open RAN, with open interfaces across disaggregated components specified by the O-RAN ALLIANCE. It is mandatory, however, to validate that all components are compliant with the specifications and can successfully interoperate, without performance gaps with traditional, monolithic appliances. Open Testing & Integration Centers (OTICs) are entities that can verify such interoperability and adherence to the standard through rigorous testing. However, how to design, instrument, and deploy an OTIC which can offer testing for multiple tenants, heterogeneous devices, and is ready to support automated testing is still an open challenge. In this paper, we introduce a blueprint for a programmable OTIC testing infrastructure, based on the design and deployment of the Open6G OTIC at Northeastern University, Boston, and provide insights on technical challenges and solutions for O-RAN testing at scale.

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE’s mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

Wireless Sensor Networks (WSNs) are pivotal in various applications, including precision agriculture, ecological surveillance, and the Internet of Things (IoT). However, energy limitations of battery-powered nodes are a critical challenge, necessitating optimization of energy efficiency for maximal network lifetime. Existing strategies like duty cycling and Wake-up Radio (WuR) technology have been employed to mitigate energy consumption and latency, but they present challenges in scenarios with sparse deployments and short communication ranges. This paper introduces and evaluates the performance of Unmanned Aerial Vehicle (UAV)-assisted mobile data collection for WuR-enabled WSNs through physical and simulated experiments. We propose two one-hop UAV-based data collection strategies: a naïve strategy, which follows a predetermined fixed path, and an adaptive strategy, which optimizes the collection route based on recorded metadata. Our evaluation includes multiple experiment categories, measuring collection reliability, collection cycle duration, successful data collection time (latency), and node awake time to infer network lifetime. Results indicate that the adaptive strategy outperforms the naïve strategy across all metrics. Furthermore, WuR-based scenarios demonstrate lower latency and considerably lower node awake time compared to duty cycle-based scenarios, leading to several orders of magnitude longer network lifetime. Remarkably, our results suggest that the use of WuR technology alone achieves unprecedented network lifetimes, regardless of whether data collection paths are optimized. This underscores the significance of WuR as the technology of choice for all energy critical WSN applications.

The Terahertz (THz) band (0.1–10 THz) is projected to enable broadband wireless communications of the future, and many envision deep learning as a solution to improve the performance of THz communication systems and networks. However, there are few available datasets of true THz signals that could enable testing and training of deep learning algorithms for the research community. In this paper, we provide an extensive dataset of 120,000 data frames for the research community. All signals were transmitted at 165 GHz but with varying bandwidths (5 GHz, 10 GHz, and 20 GHz), modulations (4PSK, 8PSK, 16QAM, and 64QAM), and transmit amplitudes (75 mV and 600 mV), resulting in twenty-four distinct bandwidth-modulation-power combinations each with 5,000 unique captures. The signals were captured after down conversion at an intermediate frequency of 10 GHz. This dataset enables the research community to experimentally explore solutions relating to ultrabroadband deep and machine learning applications.

The Open Radio Access Network (Open RAN) vision is based on the three principles of (i) open interfaces; (ii) cloudification; and (iii) automation through closed-loop control. It is a network architecture paradigm embodied and augmented through technical reference specifications of the 3GPP and the O-RAN Alliance. At the centre of Open RAN are open, programmable, and virtualized components, connected to each other through open interfaces that enable closed-loop, data-driven, and intelligent control. For instance, the O-RAN Alliance introduced two RAN Intelligent Controllers (or RICs) that connect through open interfaces to the disaggregated components of the RAN, and implement control loops that run at different time scales.

Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracingnovel cellular architecturesby transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

Obtaining access to exclusive spectrum, cell sites, Radio Access Network (RAN) equipment, and edge infrastructure imposes major capital expenses to mobile network operators. A neutral host infrastructure, by which a third-party company provides RAN services to mobile operators through network virtualization and slicing techniques, is seen as a promising solution to decrease these costs. Currently, however, neutral host providers lack automated and virtualized pipelines for onboarding new tenants and to provide elastic and on-demand allocation of resources matching operators' requirements. To address this gap, this paper presents NeutRAN, a zero-touch framework based on the O-RAN architecture to support applications on neutral hosts and automatic operator onboarding. NeutRAN builds upon two key components: (i) an optimization engine to guarantee coverage and to meet quality of service requirements while accounting for the limited amount of shared spectrum and RAN nodes, and (ii) a fully virtualized and automated infrastructure that converts the output of the optimization engine into deployable micro-services to be executed at RAN nodes and cell sites. NeutRAN was prototyped on an OpenShift cluster and on a programmable testbed with 4 base stations and 10 users from 3 different tenants. We evaluate its benefits, comparing it to a traditional license based RAN where each tenant has dedicated physical and spectrum resources. We show that NeutRAN can deploy a fully operational neutral host-based cellular network in around 10 seconds. Experimental results× and the per-user average throughput by 1.73× in networks with shared spectrum blocks of 30 MHz. NeutRAN provides a 1.77× cumulative throughput gain even when it can only operate on a shared spectrum block of 10 MHz (one third of the spectrum used in license-based RANs).

The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence via innovative data-driven algorithms. How to practically realize this vision, however, is largely an open problem. Specifically, for a given intent, it is still unclear how to select which data-driven models should be deployed and where, which parameters to control, and how to feed them appropriate inputs. In this paper, we take a decisive step forward by presenting OrchestRAN, a network intelligence orchestration framework for next generation systems that embraces and builds upon the Open Radio Access Network (RAN) paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-Real-time (RT) RAN Intelligent Controller (RIC) as an rApp and allows Network Operators (NOs) to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-RT, e.g., for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location (e.g., in the cloud, or at the edge) to achieve intents specified by the NOs while meeting the desired timing requirements and avoiding conflicts between different data-driven algorithms controlling the same parameters set. We show that the intelligence orchestration problem in Open RAN is NP-hard. To support real-world applications, we also propose three complexity reduction techniques to obtain low-complexity solutions that, when combined, can compute a solution in 0.1 s for large network instances. We prototype OrchestRAN and test it at scale on Colosseum, the world's largest wireless network emulator with hardware in the loop. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.

The next generation of cellular networks will be characterized by openness, intelligence, virtualization, and distributed computing. The Open Radio Access Network (Open RAN) framework represents a significant leap toward realizing these ideals, with prototype deployments taking place in both academic and industrial domains. While it holds the potential to disrupt the established vendor lock-ins, Open RAN's disaggregated nature raises critical security concerns. Safeguarding data and securing interfaces must be integral to Open RAN's design, demanding meticulous analysis of cost/benefit tradeoffs. In this paper, we embark on the first comprehensive investigation into the impact of encryption on two pivotal Open RAN interfaces: the E2 interface, connecting the base station with a near-real-time RAN Intelligent Controller, and the Open Fronthaul, connecting the Radio Unit to the Distributed Unit. Our study leverages a full-stack O-RAN ALLIANCE compliant implementation within the Colosseum network emulator and a production-ready Open RAN and 5G-compliant private cellular network. This research contributes quantitative insights into the latency introduced and throughput reduction stemming from using various encryption protocols. Furthermore, we present four fundamental principles for constructing security by design within Open RAN systems, offering a roadmap for navigating the intricate landscape of Open RAN security.

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user-level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducingns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps, automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative$Q$-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate, or minimize the impact of errors of emulation models, in this work, we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world?s largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average similarity of up to 0.987 in throughput and 0.982 in Signal to Interference plus Noise Ratio (SINR).

Future wireless networks and sensing systems will benefit from access to large chunks of spectrum above 100 GHz, to achieve terabit-per-second data rates in 6th Generation (6G) cellular systems and improve accuracy and reach of Earth exploration and sensing and radio astronomy applications. These are extremely sensitive to interference from artificial signals, thus the spectrum above 100 GHz features several bands which are protected from active transmissions under current spectrum regulations. To provide more agile access to the spectrum for both services, active and passive users will have to coexist without harming passive sensing operations. In this paper, we provide the first, fundamental analysis of Radio Frequency Interference (RFI) that large-scale terrestrial deployments introduce in different satellite sensing systems now orbiting the Earth. We develop a geometry-based analysis and extend it into a data-driven model which accounts for realistic propagation, building obstruction, ground reflection, for network topology with up to 105nodes in more than 85 km2. We show that the presence of harmful RFI depends on several factors, including network load, density and topology, satellite orientation, and building density. The results and methodology provide the foundation for the development of coexistence solutions and spectrum policy towards 6G.

Energy consumption represents a major part of the operating expenses of mobile network operators. With the densification foreseen with 5G and beyond, energy optimization has become a problem of crucial importance. While energy optimization is widely studied in the literature, there are limited insights and algorithms for energy-saving techniques for Integrated Access and Backhaul (IAB), a self-backhauling architecture that ease deployment of dense cellular networks reducing the number of fiber drops. This paper proposes a novel optimization model for dynamic joint routing and energy optimization in IAB networks. We leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to minimize the number of active IAB nodes while maintaining a minimum capacity per User Equipment (UE). The proposed approach formulates the problem as a binary nonlinear program, which is transformed into an equivalent binary linear program and solved using the Gurobi solver. The approach is evaluated on a scenario built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed optimization model reduces the RAN energy consumption by 47%, while guaranteeing a minimum capacity for each UE.

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access Network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven control loops. Despite recent work demonstrating the effectiveness of Deep Reinforcement Learning (DRL) in controlling O-RAN systems, how to design these solutions in a way that does not create conflicts and unfair resource allocation policies is still an open challenge. In this paper, we perform a comparative analysis where we dissect the impact of different DRL-based xApp designs on network performance. Specifically, we benchmark 12 different xApps that embed DRL agents trained using different reward functions, with different action spaces and with the ability to hierarchically control different network parameters. We prototype and evaluate these xApps on Colosseum, the world's largest O-RAN-compliant wireless network emulator with hardware-in-the-loop. We share the lessons learned and discuss our experimental results, which demonstrate how certain design choices deliver the highest performance while others might result in a competitive behavior between different classes of traffic with similar objectives.

Wide-ranging applications for underwater acoustic communication are attracting attention. However, the challenges of underwater acoustic communications make it necessary to advance communication techniques significantly. Underwater networks with wider coverage areas and more reliable communication links may be possible with the distributed deployment of several reception nodes. Using Single Input Multiple Output (SIMO) systems, power, and bandwidth resources can be utilized effectively without compromising spectral efficiency. In this work, we propose a communication system that employs software- defined distributed SIMO networking to exploit the spatial diversity of multiple receiver nodes for high data rates and reliable communication. To take advantage of spatial diversity, we develop and implement a software-defined architecture for the maximal-ratio combining receiver. Then, we conduct a thorough experimental evaluation in ocean environments for various subcarrier bandwidths and constellations using three distributed receivers. Our results show that our proposed distributed SIMO system, for both 1x2 and 1x3 settings, outperforms individual receiver modems in terms of bit error rate (BER) performance by 95.20% and 99.57%, respectively.

Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we focus our interest on detecting narrowband interference caused by signals that, despite occupying a small portion of the spectrum only, can cause significant harm to wireless systems. For example, in the case of interference with pilots and other signals that are used to equalize the effect of the channel or attain synchronization. Due to the small sizes of these signals, detection can be difficult due to their low energy footprint, while greatly impacting (or denying completely in some cases) network communications. We present a novel narrowband interference detection solution that utilizes convolutional neural networks (CNNs) to detect and locate these signals with high accuracy. To demonstrate the effectiveness of our solution, we have built a prototype that has been tested and validated on a real-world over-the-air large-scale wireless testbed. Our experimental results show that our solution is capable of detecting narrowband jamming attacks with an accuracy of up to 99%. Moreover, it is also able to detect multiple attacks affecting several frequencies at the same time even in the case of previously unseen attack patterns. Not only can our solution achieve a detection accuracy between 92% and 99%, but it does so by only adding an inference latency of 0.093ms.

Underwater acoustic communication and networking have been instrumental in enabling various commercial and military applications such as underwater surveillance, environmental monitoring, data-driven intelligent aquaculture, and unmanned underwater vehicles. However, the development of an underwater communication and networking system poses significant challenges due to spatially and temporally varying underwater acoustic channels. These channels are subject to severe multipath propagation and the Doppler effect, which can impose frequency-dependent distortions on underwater acoustic signals through frequency shifts and spreading. In high-frequency underwater acoustic communication systems, the Doppler effect is amplified, making its estimation and compensation even more challenging. In this paper, we present a detailed set of field experiments characterizing the Doppler effect in a high-frequency underwater acoustic channel. We also introduce a multicarrier communication system incorporating a Doppler estimation and compensation method. Through extensive field experiments, we quantify the performance of the Doppler estimation and compensation technique over high-frequency channels.

Jamming attacks have plagued wireless communication systems and will continue to do so going forward with technological advances. These attacks fall under the category of Electronic Warfare (EW), a continuously growing area in both attack and defense of the electromagnetic spectrum, with one subcategory being electronic attacks (EA). Jamming attacks fall under this specific subcategory of EW as they comprise adversarial signals that attempt to disrupt, deny, degrade, destroy, or deceive legitimate signals in the electromagnetic spectrum. While jamming is not going away, recent research advances have started to get the upper hand against these attacks by leveraging new methods and techniques, such as machine learning. However, testing such jamming solutions on a wide and realistic scale is a daunting task due to strict regulations on spectrum emissions. In this paper, we introduce eSWORD (emulation (of) Signal Warfare On Radio-frequency Devices), the first large-scale framework that allows users to safely conduct real-time and controlled jamming experiments with hardware-in-the-loop. This is done by integrating METEOR, an electronic warfare (EW) threat-emulating software developed by the MITRE Corporation, into the Colosseum wireless network emulator that enables large-scale experiments with up to 49 software-defined radio nodes. We compare the performance of eSWORD with that of real-world jamming systems by using an over-the-air wireless testbed (considering safe measures when conducting experiments). Our experimental results demonstrate that eSWORD achieves up to 98% accuracy in following throughput, signal-to-interference-plus-noise ratio, and link status patterns when compared to real-world jamming experiments, testifying to the high accuracy of the emulated eSWORD setup.

The electromagnetic spectrum plays a fundamental role in the development of the digital society. It enables wireless communications (either between humans or machines) and sensing (for example, for Earth exploration, radio astronomy, imaging, and radars). While each of these uses benefits from a larger bandwidth, the spectrum is a finite resource. This introduces competing interests among the different stakeholders of the spectrum, which have led—so far—to rigid policies and spectrum allocations. Recently, the spectrum crunch in the sub-6-GHz bands has prompted communication technologies to move to higher carrier frequencies, where future sixth-generation (6G) wireless networks can exploit theoretically very large bandwidths. However, the spectrum above 100 GHz features several narrow, yet numerous subbands that are exclusively allocated for passive sensing applications, e.g., for climate and weather monitoring. This prevents the allocation of large contiguous bands to active users of the spectrum, either being communications (which need tens of gigahertz of bandwidth to target terabit-per-second links) or radars. This article explores how spectrum policy and spectrum technologies can evolve to enablesharingamong different stakeholders in the above 100-GHz spectrum, without introducing harmful interference or disrupting either security applications or fundamental science exploration. This portion of the spectrum presents new challenges and opportunities for the design of spectrum sharing schemes, including higher spreading and absorption losses, extremely directional antenna technologies, and ultrahigh data-rate communications, among others. This article provides a tutorial on current regulations above 100 GHz and highlights how sharing is central to allowing each stakeholder to make the most out of this spectrum. It then defines—through detailed simulations based on standard International Telecommunications Union (ITU) channel and antenna models—scenarios in which active users may introduce harmful interference to passive sensing. Based on this evaluation, it reviews a number of promising techniques that can enable active/passive sharing above 100 GHz. The critical review and tutorial on policy and technologies of this article have the potential to kickstart future research and regulations that promote safe coexistence between active and passive users above 100 GHz, further benefiting the development of digital technologies and scientific exploration.

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator’s standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone’s location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average$19\%$network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

Cellular networks are undergoing a radical transformation toward disaggregated, fully virtualized, and programmable architectures with increasingly heterogeneous devices and applications. In this context, the open architecture standardized by the O-RAN Alliance enables algorithmic and hardware-independent Radio Access Network (RAN) adaptation through closed-loop control. O-RAN introduces Machine Learning (ML)-based network control and automation algorithms as so-calledxAppsrunning on RAN Intelligent Controllers . However, in spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by discussing insights and practical solutions for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. To this end, we introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a “wireless data factory.” Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. The lessons learned from the ColO-RAN implementation and the extensive results from our first-of-its-kind large-scale evaluation highlight the importance of experimental frameworks for the development of end-to-end intelligent RAN control pipelines, from data analysis to the design and testing of DRL agents. They also provide insights on the challenges and benefits of DRL-based adaptive control, and on the trade-offs associated to training on a live RAN. ColO-RAN and the collected large-scale dataset are publicly available to the research community.

Carrierless Amplitude and Phase (CAP) modulation has been widely used in the emerging visible light communication (VLC) paradigm. Compared to other modulation schemes, CAP is in fact known to be comparatively easy to implement, to have lower computational complexity, high spectral efficiency, and superior noise performance. However, as of today, how to select optimal parameters in CAP links for a desired target bit error rate (BER) performance and spectral efficiency requirements is still an open problem, because of the absence of closed-form expressions for the bit error probability under different signal-to-noise ratio (SNR) conditions. In this article, we present a comprehensive analysis on the impact of different CAP parameters on the bit error probability under different noise levels. We first describe the system model and derive a theoretical expression for the bit error rate of a CAP-modulated communication system. Then, we present detailed simulation and experimental results for different CAP parameters. The derived theoretical expression is also validated through simulations and experiments for each different CAP parameter. Then, with this expression, we show how to select optimal link parameters for different noise levels, by maximizing the spectral efficiency while keeping the BER lower than a predefined threshold.

Underwater vehicles (UVs) are becoming essential for a vast range of novel commercial, scientific and military applications. These include seabed exploration, monitoring of critical infrastructure and resources, and coastal surveillance. However, usage of UVs is currently beset by limitations preventing them to carry critical equipment, such as agile wireless communication systems, which would facilitate and enable those applications. To overcome these limitations, in this paper we present the design blueprint and evaluation of a wireless UV, obtained by integrating a custom software-defined underwater acoustic networking platform integrated to a UV. We first describe the integration of the platform with a commercially available UV. We then present results from experimental campaigns at sea using our wireless UV to generate datasets for studying the Doppler effect due to mobility. Our results indicate the effectiveness of our design for ease of deployment and dataset generation.

Today’s radio access networks (RANs) are monolithic entities which often operate statically on a given set of parameters for the entirety of their operations. To implement realistic and effective spectrum sharing policies, RANs will need to seamlessly and intelligently change their operational parameters. In stark contrast with existing paradigms, the new O-RAN architectures for 5G-and-beyond networks (NextG) separate the logic that controls the RAN from its hardware substrate, allowing unprecedented real-time fine-grained control of RAN components. In this context, we propose the Channel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliant framework that allows (i) sensing the spectrum to infer the presence of interference and (ii) reacting in real time by switching the distributed unit (DU) and radio unit (RU) operational parameters according to a specified spectrum access policy. ChARM is based on neural networks operating directly on unprocessed I/Q waveforms to determine the current spectrum context. ChARM does not require any modification to the existing 3GPP standards. It is designed to operate within the O-RAN specifications, and can be used in conjunction with other spectrum sharing mechanisms (e.g., LTE-U, LTE-LAA or MulteFire). We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi-Fi in unlicensed bands, where a controller operating over a RAN Intelligent Controller (RIC) senses the spectrum and switches cell frequency to avoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with. To collect standard-compliant Wi-Fi data, we extended the Colosseum testbed using system-on-chip (SoC) boards running a modified version of the OpenWiFi architecture. Experimental results show that ChARM achieves accuracy of up to 96% on Colosseum and 85% on an over-the-air testbed, demonstrating the capacity of ChARMto exploit the considered spectrum channels.

The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence via innovative data-driven algorithms. How to practically realize this vision, however, is largely an open problem. For a given network optimization/automation objective, it is currently unknown how to select which data-driven models should be deployed and where, which parameters to control, and how to feed them appropriate inputs. In this paper, we take a decisive step forward by presenting and prototyping OrchestRAN, a novel orchestration framework for next generation systems that embraces and builds upon the Open Radio Access Network (RAN) paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-Real-time (RT) RAN Intelligent Controller (RIC) and allows Network Operators (NOs) to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-RT, e.g., for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location (e.g., in the cloud, or at the edge) to achieve intents specified by the NOs while meeting the desired timing requirements and avoiding conflicts between different data-driven algorithms controlling the same parameters set. We show that the intelligence orchestration problem in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype OrchestRAN and test it at scale on Colosseum, the world’s largest wireless network emulator with hardware in the loop. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.

Millimeter wave (mmWave) communications have the potential to dramatically increase the throughput of 5G-and-beyond wireless networks. However, the challenging propagation conditions typical of higher frequencies require expensive base station densification to guarantee reliable Radio Access Networks (RANs). Integrated Access and Backhaul (IAB), a solution where wireless access and backhaul use the same waveform, spectrum, and protocol stack, has been proposed and standardized as a highly effective means of decreasing these costs. While IAB is considered a key enabler for high-frequency RANs, experimental research in this context is hampered by the lack of accessible testing platforms. In this demonstration, we showcase IABEST, a large-scale end-to-end IAB testbed based on open-source software and compatible with off-the-shelf hardware. We show how to deploy IABEST capabilities at scale on Colosseum, a publicly available massive channel emulator. Finally, we show how IABEST can support researchers in data collection and algorithm testing from the highest levels of network abstraction down to scheduling decisions.

With more than 65M people affected by epilepsy worldwide, early prediction and response to seizure onsets have become more important than ever. Cutting-edge research in implantable medical devices (IMDs) has shown that deep neural networks (DNNs) applied to intracranial electroencephalogram (iEEG) data can predict seizures up to an hour before onset. However, offloading of iEEG data to the edge/cloud is highly prohibitive, due to the sheer size of the generated data. Existing work either focuses on the DNN training phase only, or does not consider the severe energy/space limitations of IMDs. Moreover, the technical aspects of patient personalization, which allows for patient-specific hyper-parameter tuning, still remain unaddressed. In this paper, we propose a platform called AiEEG for in vivo early seizure prediction, whose DNN hardware circuitry can be reconfigured remotely without surgery. We prototype AiEEG on a system on chip (SoC) platform and demonstrate its end-to-end capabilities in seizure prediction with a population of 30 epileptic patients, with iEEG signals coming from a real-world dataset. Extensive experimental results shows that (i) our embedded and personalized DNN has an area under the curve (AUC) averaging at 0.97 and as low as zero false positives per hour (FPH) for over half the patients, an improvement of about 3.5x with respect to a non-personalized prediction method and the best for a dataset of this size when compared to the state-of-the-art; (ii) our AiEEG platform consumes 4.2x less energy than a cloud-based approach, leading to a 4x battery lifetime improvement; (iii) we are able to remotely fine-tune the DNN through partial reconfiguration as needed in about 10s.

Open Radio Access Network (RAN) architectures will enable interoperability, openness, and programmatic data-driven control in next generation cellular networks. However, developing scalable and efficient data-driven algorithms that can generalize across diverse deployments and optimize RAN performance is a complex feat, largely unaddressed as of today. Specifically, the ability to design efficient data-driven algorithms for network control and inference requires at a minimum (i) access to large, rich, and heterogeneous datasets; (ii) testing at scale in controlled but realistic environments, and (iii) software pipelines to automate data collection and experimentation. To facilitate these tasks, in this paper we propose OpenRAN Gym, a practical, open, experimental toolbox that provides end-to-end design, data collection, and testing workflows for intelligent control in next generation Open RAN systems. OpenRAN Gym builds on software frameworks for the collection of large datasets and RAN control, and on a lightweight O-RAN environment for experimental wireless platforms. We first provide an overview of OpenRAN Gym and then describe how it can be used to collect data, to design and train artificial intelligence and machine learning-based O-RAN applications (xApps), and to test xApps on a softwarized RAN. Then, we provide an example of two xApps designed with OpenRAN Gym and used to control a large-scale network with 7 base stations and 42 users deployed on the Colosseum testbed. OpenRAN Gym and its software components are open source and publicly-available to the research community.

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.

This article describes HIRO-NET, an Heterogeneous Intelligent Robotic Network. HIRO-NET is an emergency infrastructure-less network that aims to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy (BLE) and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster-stricken area. HIRO-NET's main goal is to enable users in the disaster area to exchange text messages to share critical information and request help from first responders. The mesh network discovery problem is analyzed and a network protocol specifically designed to facilitate the exploration process is presented. We show how HIRO-NET robots successfully discover, bridge and interconnect local mesh networks. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g., Twitter, Gmail) to the whole network. Results suggest that a temporary emergency network to cover a metropolitan area can be created in tens of minutes.

The Internet of Underwater Things (IoUT) will enable new military, scientific, and commercial applications at sea. However, powering of electronic devices in deep water still remains one of the main challenges, since these systems are typically powered by traditional batteries. This article presents the design of the first batteryless underwater sensor node that can be wirelessly recharged through ultrasonic waves from longer distances than allowed by current technologies. First, the architecture of an underwater platform capable of extracting electrical energy from ultrasonic waves is introduced. We then illustrate how to interface this system with an underwater digital communication unit. We discuss the design of a prototype where the storage unit is realized with a batch of supercapacitors. We show through experiments that the harvested energy is sufficient to provide the sensor node with the power necessary to perform a sensing operation and power a modem for ultrasonic communications. In the article, we evaluate the system power transfer efficiency. Given the reduced attenuation of ultrasonic waves in water, we show that our approach can cover longer distances with less transmission power than alternative solutions. Last, we experimentally evaluate the overall operating efficiency of the system.

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, al-lowing it to run optimization and machine learning-based frame-works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network-based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con-ditions and traffic patterns. QCell is prototyped on Colosseum, the world's largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user's throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.

Fifth-generation (5G) systems will extensively employ radio access network (RAN) softwarization. This key innovation enables the instantiation of "virtual cellular networks" running on different slices of the shared physical infrastructure. In this paper, we propose the concept of Private Cellular Connectivity as a Service (PCCaaS), where infrastructure providers deploy covert network slices known only to a subset of users. We then present SteaLTE as the first realization of a PCCaaS-enabling system for cellular networks. At its core, SteaLTE utilizes wireless steganography to disguise data as noise to adversarial receivers. Differently from previous work, however, it takes a full-stack approach to steganography, contributing an LTE-compliant stegano-graphic protocol stack for PCCaaS-based communications, and packet schedulers and operations to embed covert data streams on top of traditional cellular traffic (primary traffic). SteaLTE balances undetectability and performance by mimicking channel impairments so that covert data waveforms are almost indistinguishable from noise. We evaluate the performance of SteaLTE on an indoor LTE-compliant testbed under different traffic profiles, distance and mobility patterns. We further test it on the outdoor PAWR POWDER platform over long-range cellular links. Results show that in most experiments SteaLTE imposes little loss of primary traffic throughput in presence of covert data transmissions (<; 6%), making it suitable for undetectable PCCaaS networking.

Due to the sheer scale of the Internet of Things (IoT) and 5G, the wireless spectrum is becoming severely congested. For this reason, wireless devices will need to continuously adapt to current spectrum conditions by changing their communication parameters in real-time. Therefore, wireless signal classification (WSC) will become a compelling necessity to decode fast-changing signals from dynamic transmitters. Thanks to its capability of classifying complex phenomena without explicit mathematical modeling, deep learning (DL) has been demonstrated to be a key enabler of WSC. Although DL can achieve a very high accuracy under certain conditions, recent research has unveiled that the wireless channel can disrupt the features learned by the DL model during training, thus drastically reducing the classification performance in real-world live settings. Since retraining classifiers is cumbersome after deployment, existing work has leveraged the usage of carefully-tailored Finite Impulse Response (FIR) filters that, when applied at the transmitter's side, can restore the features that are lost because of the the channel actions, i.e., waveform synthesis. However, these approaches compute FIRs using offline optimization strategies, which limits their efficacy in highly-dynamic channel settings. In this paper, we improve the state of the art by proposing Chares, a Deep Reinforcement Learning (DRL)-based framework for channel-resilient adaptive waveform synthesis. Chares adapts to new and unseen channel conditions by optimally computing through DRL the FIRs in real time. Chares is a DRL agent whose architecture is based upon the Twin Delayed Deep Deterministic Policy Gradients (TD3), which requires minimal feedback from the receiver and explores a continuous action space for best performance. Chares has been extensively evaluated on two well-known datasets with an extensive number of channels. We have also evaluated the real-time latency of Chares with an implementation on field-programmable gate array (FPGA). Results show that Chares increases the accuracy up to 4.1x when no waveform synthesis is performed, by 1.9x with respect to existing work, and can compute new actions within 41 μs.

Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper, we propose DeepSense, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver's baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform's hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected "in the wild" with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms. For reproducibility and benchmarking purposes, we pledge to share the code and the datasets used in this paper to the community.

Practical experimentation and prototyping are core steps in the development of any wireless technology. Often times, however, this crucial step is confined to small laboratory setups that do not capture the scale of commercial deployments and do not ensure result reproducibility and replicability, or it is skipped altogether for lack of suitable hardware and testing facilities. Recent years have seen the development of publicly-available testing platforms for wireless experimentation at scale. Examples include the testbeds of the PAWR program and Colosseum, the world's largest wireless network emulator. With its 256 software-defined radios, 24 racks of powerful compute servers and first-of-its-kind channel emulator, Colosseum allows users to prototype wireless solutions at scale, and guarantees reproducibility and replicability of results. This tutorial provides an overview of the Colosseum platform. We describe the architecture and components of the testbed as a whole, and we then showcase how to run practical experiments in diverse scenarios with heterogeneous wireless technologies (e.g., Wi-Fi and cellular). We also emphasize how Colosseum experiments can be ported to different testing platforms, facilitating full-cycle experimental wireless research: design, experiments and tests at scale in a fully controlled and observable environment and testing in the field. The tutorial concludes with considerations on the flexible future of Colosseum, focusing on its planned extension to emulate larger scenarios and channels at higher frequency bands (mmWave).

The Long Range (LoRa) protocol for low-power wide-area networks (LPWANs) is a strong candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low cost, impressive sensitivity (-137dBm), and massive scalability potential. As tens of thousands of tiny LoRa devices are deployed over large geographic areas, a key component to the success of LoRa will be the development of reliable and robust authentication mechanisms. To this end, Radio Frequency Fingerprinting (RFFP) through deep learning (DL) has been heralded as an effective zero-power supplement or alternative to energy-hungry cryptography. Existing work on LoRa RFFP has mostly focused on small-scale testbeds and low-dimensional learning techniques; however, many challenges remain. Key among them are authentication techniques robust to a wide variety of channel variations over time and supporting a vast population of devices.

Highly directional millimeter wave (mmWave) radios need to perform beam management to establish and maintain reliable links. To achieve this objective, existing solutions mostly rely on explicit coordination between the transmitter (TX) and the receiver (RX), which significantly reduces the airtime available for communication and further complicates the network protocol design. This paper advances the state of the art by presenting DeepBeam, a framework for beam management that does not require pilot sequences from the TX, nor any beam sweeping or synchronization from the RX. This is achieved by inferring (i) the Angle of Arrival (AoA) of the beam and (ii) the actual beam being used by the transmitter through waveform-level deep learning on ongoing transmissions between the TX to other receivers. In this way, the RX can associate Signal-to-Noise-Ratio (SNR) levels to beams without explicit coordination with the TX. This is possible because different beam patterns introduce different "impairments" to the waveform, which can be subsequently learned by a convolutional neural network (CNN). To demonstrate the generality of DeepBeam, we conduct an extensive experimental data collection campaign where we collect more than 4 TB of mmWave waveforms with (i) 4 phased array antennas at 60.48 GHz, (ii) 2 codebooks containing 24 one-dimensional beams and 12 two-dimensional beams; (iii) 3 receiver gains; (iv) 3 different AoAs; (v) multiple TX and RX locations. Moreover, we collect waveform data with two custom-designed mmWave software-defined radios with fully-digital beamforming architectures at 58 GHz. We also implement our learning models in FPGA to evaluate latency performance. Results show that DeepBeam (i) achieves accuracy of up to 96%, 84% and 77% with a 5-beam, 12-beam and 24-beam codebook, respectively; (ii) reduces latency by up to 7x with respect to the 5G NR initial beam sweep in a default configuration and with a 12-beam codebook. The waveform dataset and the full DeepBeam code repository are publicly available.

The cellular networking ecosystem is being radically transformed by openness, softwarization, and virtualization principles, which will steer NextG networks toward solutions running on "white box" infrastructures. Telco operators will be able to truly bring intelligence to the network, dynamically deploying and adapting its elements at run time according to current conditions and traffic demands. Deploying intelligent solutions for softwarized NextG networks, however, requires extensive prototyping and testing procedures, currently largely unavailable. To this aim, this paper introduces SCOPE, an open and softwarized prototyping platform for NextG systems. SCOPE is made up of: (i) A ready-to-use, portable open-source container for instantiating softwarized and programmable cellular network elements (e.g., base stations and users); (ii) an emulation module for diverse real-world deployments, channels and traffic conditions for testing new solutions; (iii) a data collection module for artificial intelligence and machine learning-based applications, and (iv) a set of open APIs for users to control network element functionalities in real time. Researchers can use SCOPE to test and validate NextG solutions over a variety of large-scale scenarios before implementing them on commercial infrastructures. We demonstrate the capabilities of SCOPE and its platform independence by prototyping exemplary cellular solutions in the controlled environment of Colosseum, the world's largest wireless network emulator. We then port these solutions to indoor and outdoor testbeds, namely, to Arena and POWDER, a PAWR platform.

Autonomous networks of sensors, unmanned aerial vehicles (UAVs) and unmanned underwater vehicles (UUVs) will play a vital role in scenarios/applications where a plethora of distributed assets across multiple domains – air and water - operate in unison to accomplish a common goal. However, establishing high data rate, robust, and bi-directional communication links across the air-water interface between aerial and underwater assets is still an open problem. In this article, we propose a communication system based on visible (blue) light that enables aerial and underwater assets to establish bi-directional links through the air-water interface without requiring any preexisting communication infrastructure such as buoys acting as relay nodes. We first derive a mathematical model and accordingly build a simulator for the bi-directional air-water visible light communication (VLC) channel accounting for water surface distribution, optical parameters and path losses. Then, we design and prototype a software-defined visible light communication (VLC) modem. We present an extensive experimental evaluation conducted both in a test tank and in the ocean using the proposed VLC modem prototypes.

Designing efficient medium access control protocols for underwater acoustic sensor networks (UW-ASNs) is a major challenge because of the spatial and temporal interference uncertainty caused by asynchronous transmissions and by the low propagation speed of sound. To address these challenges, in this article we propose a new approach for distributed underwater medium access based on lightweight and asynchronous distributed algorithms that optimize the access probability profile over a series of time slots based on a new statistical physical interference model. The latter is based on measuring the level of interference at multiple instants of time in each time slot in order to capture the effects of temporal uncertainty and of unaligned interference. At each measurement instant, the statistical properties of time-varying interference are represented by a Gamma probability distribution. The model is validated through extensive channel measurement experiments conducted with an underwater acoustic testbed in Lake LaSalle. Based on this model, we formulate the problem of queue-aware stochastic channel access. The objective is to maximize the sum throughput of a set of concurrent and mutually interfering source-destination pairs by letting the transmitters adjust their own transmission probability profiles, without collaborating with each other, over a series of time slots based on a statistical characterization of interference obtained through past observations. We propose an iterative distributed solution algorithm for this problem based on a best-response strategy. At each iteration, each node individually solves a non-convex optimization problem of logarithmic complexity. The performance of the proposed distributed algorithm is evaluated by comparing it with two alternative distributed schemes and with the global optimum obtained through a newly-developed centralized globally optimal solution algorithm. Results indicate that by jointly taking the queueing and multi-slot optimization into consideration considerable improvement in terms of sum-throughput can be achieved by the proposed distributed algorithm.

Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0-1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2× the signal-to-interference-plus-noise ratio.

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware. We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to automatically generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.

Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposesDeepFIR, a framework to counteract the channel action in wireless deep learning algorithmswithout retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter’s side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions. We mathematically formulate theWaveform Optimization Problem(WOP)as the problem of finding the optimum FIR to be used on a waveform to improve the classifier’s accuracy. We also propose a data-driven methodology to train the FIRs directly with dataset inputs. We extensively evaluateDeepFIRon an experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%; (ii) decreases an adversary’s accuracy by about 54% when trying to imitate other device’s fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset; (iv) increases by$2\times$the accuracy of the modulation dataset.

Radio fingerprinting uniquely identifies wireless devices by leveraging tiny hardware-level imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be directly identified at the physical layer by analyzing the unprocessed received waveform - thus avoiding energy-expensive upper-layer cryptography that resource-challenged embedded devices may not be able to afford. Recent advances have proven that convolutional neural networks (CNNs) - thanks to their multidimensional mappings - can achieve fingerprinting accuracy levels impossible to achieve by traditional low-dimensional algorithms. The same research, however, has also suggested that the wireless channel may negatively impact the accuracy of CNN-based radio fingerprinting algorithms by making device-unique hardware imperfections much harder to recognize.In spite of the growing interest in radio fingerprinting research by academia and DARPA, the wireless research community still lacks (i) a large-scale open dataset for radio fingerprinting collected in diverse environments and rich, diverse, channel conditions; and (ii) a full-fledged, systematic, quantitative investigation of the impact of the wireless channel on the accuracy of CNN-based radio fingerprinting algorithms. The key contribution of this paper is to bridge this gap by (i) collecting and sharing with the community more than 7TB of wireless data obtained from 20 wireless devices with identical RF circuitry (and thus, worst-case scenario for fingerprinting) over the course of several days in (a) an anechoic chamber, (b) in-the-wild testbed, and (c) with cable connections; and (ii) providing a first-of-its-kind evaluation of the impact of the wireless channel on CNN-based fingerprinting algorithms through (a) the 7TB experimental dataset and (b) a 400GB dataset provided by DARPA containing hundreds of thousands of transmissions from thousands of WiFi and ADS-B devices with different SNR conditions. Experimental results conclude that (i) the wireless channel impacts the classification accuracy significantly, i.e., from 85% to 9% and from 30% to 17% in the experimental and DARPA dataset, respectively; and that (ii) equalizing I/Q data can increase the accuracy to a significant extent (i.e., by up to 23%) when the number of devices increases significantly.

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

This article studies an essential yet challenging problem in 5G wireless networks: Is it possible to enable spectrally-efficient spectrum sharing for heterogeneous wireless networks with different, possibly incompatible, spectrum access technologies on the same spectrum bands; without modifying the protocol stacks of existing wireless networks? To answer this question, this article explores the system challenges that need to be addressed to enable a new spectrum sharing paradigm based on beamforming, which we refer to as CoBeam. In CoBeam, a secondary wireless network is allowed to access a spectrum band based on cognitive beam-forming without mutual temporal exclusion, i.e., without interrupting the ongoing transmissions of coexisting wireless networks on the same bands; and without cross-technology communication. We first describe the main components of CoBeam, including programmable physical layer driver, cognitive sensing engine, and beamforming engine, and then we showcase the potential of the CoBeam framework by designing a practical coexistence scheme between Wi-Fi and LTE on unlicensed bands. We present a prototype of the resulting coexisting Wi-Fi/U-LTE network built on off-the-shelf software radios based on which we evaluate the performance of CoBeam through an extensive experimental campaign. Performance evaluation results indicate that CoBeam can achieve on average 169% throughput gain while requiring no signaling exchange between the coexisting wireless networks.

Recent work has demonstrated that cutting-edge advances in deep reinforcement learning (DRL) may be leveraged to empower wireless devices with the much-needed ability to "sense" current spectrum and network conditions and "react" in real time by either exploiting known optimal actions or exploring new actions. Yet, understanding whether real-time DRL can be at all applied in the resource-challenged embedded IoT domain, as well as designing IoT-tailored DRL systems and architectures, still remains mostly uncharted territory. This paper bridges the existing gap between the extensive theoretical research on wireless DRL and its system-level applications by presenting Deep Wireless Embedded Reinforcement Learning (DeepWiERL), a general-purpose, hybrid software/hardware DRL framework specifically tailored for embedded IoT wireless devices. DeepWiERL provides abstractions, circuits, software structures and drivers to support the training and real-time execution of state-of-the-art DRL algorithms on the device's hardware. Moreover, DeepWiERL includes a novel supervised DRL model selection and bootstrap (S-DMSB) technique that leverages transfer learning and high-level synthesis (HLS) circuit design to orchestrate a neural network architecture that satisfies hardware and application throughput constraints and speeds up the DRL algorithm convergence. Experimental evaluation on a fully-custom software-defined radio testbed (i) proves for the first time the feasibility of real-time DRL-based algorithms on a real-world wireless platform with multiple channel conditions; (ii) shows that DeepWiERL supports 16x data rate and consumes 14x less energy than a software-based implementation, and (iii) indicates that S-DMSB may improve the DRL convergence time by 6x and increase the obtained reward by 45% if prior channel knowledge is available.

Network slicing of multi-access edge computing (MEC) resources is expected to be a pivotal technology to the success of 5G networks and beyond. The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is hardly addressable with existing slicing algorithms. The main innovation of this paper is Sl-EDGE, a unified MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, 5G network access) on edge devices. We first describe the architecture and operations of Sl-EDGE, and then show that the problem of optimally instantiating joint network-MEC slices is NP-hard. Thus, we propose near-optimal algorithms that leverage key similarities among edge nodes and resource virtualization to instantiate heterogeneous slices 7.5x faster and within 25% of the optimum. We first assess the performance of our algorithms through extensive numerical analysis, and show that Sl-EDGE instantiates slices 6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore, experimental results on a 24-radio testbed with 9 smartphones demonstrate that Sl-EDGE provides simultaneously highly-efficient slicing of joint LTE connectivity, video streaming over WiFi, and ffmpeg video transcoding.

Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform, and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

In this paper we propose SkyCell, a prototyping platform for 5G autonomous aerial base stations. While the majority of work on the topic focuses on theoretical and rarely implemented solutions, SkyCell practically demonstrates the feasibility of an aerial base station where wireless backhaul, autonomous mobility and 5G functionalities are integrated within a unified framework. We showcase the advantages of Unmanned Aerial Vehicles for 5G applications, discuss the design challenges, and ultimately propose a prototyping framework to develop aerial cellular base stations. Experimental results demonstrate that SkyCell not only supports heterogeneous data traffic demand and services, but also enables the implementation of autonomous flight control algorithms while improving metrics such as network throughput (up to 35%) and user fairness (up to 39%).

Current cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors. Their configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from performing fine grained performance optimization, especially on a per-user basis. To address these key issues, this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention (“zero-touch”). CellOS leverages softwarization and automatic optimization principles to bridge Software-Defined Networking (SDN) and cross-layer optimization. Unlike state-of-the-art SDN-inspired solutions for cellular networking, CellOS: (i) Hides low-level network details through a general virtual network abstraction; (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices. CellOS has been implemented and evaluated on an indoor testbed with two different LTE-compliant implementations: OpenAirInterface and srsLTE. We further demonstrated CellOS capabilities on the long-range outdoor POWDER-RENEW PAWR 5G platform. Results from scenarios with multiple base stations and users show that CellOS is platform-independent and self-adapts to diverse network deployments. Our investigation shows that CellOS outperforms existing solutions on key metrics, including throughput (up to 86% improvement), energy efficiency (up to 84%) and fairness (up to 29%).

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Wireless networks of electronically controlled implantable medical sensors and actuators will be the basis of many innovative and potentially revolutionary therapies. The biggest obstacle in realizing this vision of networked implants is posed by the dielectric nature of the human body, which strongly attenuates radio-frequency (RF) electromagnetic waves. In this paper we present the first hardware and software architecture of an Internet of Medical Things (IoMT) platform with ultrasonic connectivity for intra-body communications. This platform can be used as a basis for building future IoT-ready medical implants and wearable devices. We demonstrate that ultrasonic waves can be efficiently generated and received with low-power mm-sized components, and that despite the conversion loss introduced by ultrasonic transducers, the attenuation of 2.4 GHz RF is substantially greater than ultrasound, e.g., ultrasound attenuates 70 dB less than RF at distances over 10 cm. We show that the proposed IoMT platform requires significantly less transmission power than 2.4 GHz RF with equal reliability, e.g., 35 dBm less at distances over 12 cm with 10-3Bit Error Rate (BER) thus enabling a lower energy per bit and a longer device lifetime. Finally, we experimentally establish 2.4 GHz RF links do not function at distances greater than 12 cm, while ultrasonic links achieve a reliability of 10-6BER up to a distance of 20 cm with less than 0 dBm transmission power.

This paper investigates the advantages and design challenges of leveraging Unmanned Aerial Vehicles (UAVs) to deploy 4G/5G femto- and pico-cells to provide quality-aware user service and improve network performance. In order to do so, we combine UAVs dashing flight capabilities with Software-defined Radios (SDRs) flexibility and devise the concept of self-optimizing UAV Base Stations (UABSs). The proposed framework allows for on-the-fly drone repositioning based on rigorous optimization techniques using real-time network metrics to enhance users' service. This makes it possible to offload the traditional cellular infrastructure, or to mend its temporary failure, by deploying UABSs in areas of interest. Cellular connectivity is, then, provided to mobile subscribers through the LTE-compliant OpenAirInterface software interfaced with the on-drone SDR. We first describe the UABS design challenges and approaches. Then, we give details on the devised optimization algorithm and its main requirements. Finally, we illustrate a prototype implementation of the proposed UABS that leverages an SDR device and a PX4 flight controller, and test its effectiveness. Experimental results demonstrate that UABSs are able to autonomously reposition themselves based on cellular network metrics and to improve network performance.

Indoor Visible Light Communications (VLC) are a promising technology to alleviate the looming spectrum crunch crisis in traditional RF spectrum bands. This article studies how to provide throughput-optimal WiFi-like downlink access to users in indoor visible light networks through a set of centrally-controlled and partially interfering light emitting diodes (LEDs). To reduce the effect of interference among users created by the partial overlap of each LED's field of view, we propose LiBeam, a cooperative beamforming scheme, based on forming multiple LED clusters. Each cluster then serves a subset of users by jointly determining the user-LED association strategies and the beamforming vectors of the LEDs. The paper first proposes a mathematical model of the cooperative beamforming problem, presented as maximizing the sum throughput of all VLC users. Then, we solve the resulting mixed integer nonlinear nonconvex programming (MINCoP) problem by designing a globally optimal solution algorithm based on a combination of branch and bound framework as well as convex relaxation techniques. We then design for the first time a large programmable visible light networking testbed based on USRP X310 software-defined radios, and experimentally demonstrate the effectiveness of the proposed joint beamforming and association algorithm through extensive experiments. Performance evaluation results indicate that over 95% utility gain can be achieved compared to suboptimal network control strategies.

Undetectable wireless transmissions are fundamental to avoid eavesdroppers or censorship by authoritarian governments. To address this issue, wireless steganography “hides” covert information inside primary information by slightly modifying the transmitted waveform such that primary information will still be decodable, while covert information will be seen as noise by agnostic receivers. Since the addition of covert information inevitably decreases the SNR of the primary transmission, a key challenge in wireless steganography is to mathematically analyze and optimize the impact of the covert channel on the primary channel as a function of different channel conditions. Another core issue is to make sure that the covert channel is almost undetectable by eavesdroppers. Existing approaches are protocol-specific and thus their performance cannot be assessed and optimized in general scenarios. To address this research gap, we notice that existing wireless technologies rely on phase-keying modulations (e.g., BPSK, QPSK) that in most cases do not use the channel up to its Shannon capacity. Therefore, the residual capacity can be leveraged to implement a wireless system based on a pseudo-noise asymmetric shift keying (PN-ASK) modulation, where covert symbols are mapped by shifting the amplitude of primary symbols. This way, covert information will be undetectable, since a receiver expecting phase-modulated symbols will see their shift in amplitude as an effect of channel/path loss degradation. Through rigorous mathematical analysis, we first investigate the SER of PN-ASK as a function of the channel; then, we find the optimal PN-ASK parameters that optimize primary and covert throughput under different channel condition. We evaluate the throughput performance and undetectability of PN-ASK through extensive simulations and on an experimental testbed based on USRP N210 software-defined radios. Results indicate that PN-ASK improves the throughput by more than 8x with respect to prior art. Finally, we demonstrate through experiments that PN-ASK is able to transmit covert data on top of IEEE 802.11g frames, which are correctly decoded by an off-the-shelf laptop WiFi card without any hardware modifications.

The notions of softwarization and virtualization of the radio access network (RAN) of next-generation (5G) wireless systems are ushering in a vision where applications and services are physically decoupled from devices and network infrastructure. This crucial aspect will ultimately enable the dynamic deployment of heterogeneous services by different network operators over the same physical infrastructure. RAN slicing is a form of 5G virtualization that allows network infrastructure owners to dynamically “slice” and “serve” their network resources (i. e., spectrum, power, antennas, among others) to different mobile virtual network operators (MVNOs), according to their current needs. Once the slicing policy (i.e., the percentage of resources assigned to each MVNO) has been computed, a major challenge is how to allocate spectrum resources to MVNOs in such a way that (i) the slicing policy defined by the network owner is enforced; and (ii) the interference among different MVNOs is minimized. In this article, we mathematically formalize the RAN slicing enforcement problem (RSEP) and demonstrate its NP-hardness. For this reason, we design three approximation algorithms that render the solution scalable as the RSEP increases in size. We extensively evaluate their performance through simulations and experiments on a testbed made up of 8 software-defined radio peripherals. Experimental results reveal that not only do our algorithms enforce the slicing policies, but can also double the total network throughput when intra-MVNO power control policies are used in conjunction.

The explosion of 5G networks and the Internet of Things will result in an exceptionally crowded RF environment, where techniques such as spectrum sharing and dynamic spectrum access will become essential components of the wireless communication process. In this vision, wireless devices must be able to (i) learn to autonomously extract knowledge from the spectrum on-the-fly; and (ii) react in real time to the inferred spectrum knowledge by appropriately changing communication parameters, including frequency band, symbol modulation, coding rate, among others. Traditional CPU-based machine learning suffers from high latency, and requires application-specific and computationally-intensive feature extraction/selection algorithms. Conversely, deep learning allows the analysis of massive amount of unprocessed spectrum data without ad-hoc feature extraction. So far, deep learning has been used for offline wireless spectrum analysis only. Therefore, additional research is needed to design systems that bring deep learning algorithms directly on the device's hardware and tightly intertwined with the RF components to enable real-time spectrum-driven decision-making at the physical layer. In this paper, we present RFLearn, the first system enabling spectrum knowledge extraction from unprocessed I/Q samples by deep learning directly in the RF loop. RFLearn provides (i) a complete hardware/software architecture where the CPU, radio transceiver and learning/actuation circuits are tightly connected for maximum performance; and (ii) a learning circuit design framework where the latency vs. hardware resource consumption trade-off can explored. We implement and evaluate the performance of RFLearn on custom software-defined radio built on a system-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers and VERT2450 antennas. We showcase the capabilities of RFLearn by applying it to solving the fundamental problems of modulation and OFDM parameter recognition. Experimental results reveal that RFLearn decreases latency and power by about 17x and 15x with respect to a software-based solution, with a comparatively low hardware resource consumption.

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by leveraging the unique hardware-level imperfections imposed on the received wireless signal by the transmitter's radio circuitry. Most of existing approaches utilize hand-tailored protocol-specific feature extraction techniques, which can identify devices operating under a pre-defined wireless protocol only. Conversely, by mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard.

Recent research advances in wireless security have shown that advanced jamming can significantly decrease the performance of wireless communications. In advanced jamming, the adversary intentionally concentrates the available energy budget on specific critical components (e.g., pilot symbols, acknowledgement packets, etc.) to (i) increase the jamming effectiveness, as more targets can be jammed with the same energy budget; and (ii) decrease the likelihood of being detected, as the channel is jammed for a shorter period of time. These key aspects make advanced jamming very stealthy yet exceptionally effective in practical scenarios. One of the fundamental challenges in designing defense mechanisms against an advanced jammer is understanding which jamming strategies yields the lowest throughput, for a given channel condition and a given amount of energy. To the best of our knowledge, this problem still remains unsolved, as an analytic model to quantitatively compare advanced jamming schemes is still missing in existing literature. To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks. We first mathematically model a number of advanced jamming schemes at the signal processing level, so that a quantitative relationship between the jamming energy and the jamming effect is established. Based on the model, theorems are derived on the optimal advanced jamming scheme for an arbitrary channel condition. The theoretical findings are validated through extensive simulations and experiments on a 5-radio 2x2 MIMO testbed. Our results show that the theorems are able to predict jamming efficiency with high accuracy. Moreover, to further demonstrate that the theoretical findings are applicable to address crucial real-world jamming problems, we show that the theorems can be incorporated to state-of-art reinforcement-learning based jamming algorithms and boost the action exploration phase so that a faster convergence is achieved.

The promise of real-time detection and response to life-crippling diseases brought by the Implantable Internet of Medical Things (IIoMT) has recently spurred substantial advances in implantable technologies. Yet, existing devices do not provide at once the miniaturized end-to-end sensing-computation-communication-recharging capabilities to implement IIoMT applications. This paper fills the existing research gap by presenting U-Verse, the first FDA-compliant rechargeable IIoMT platform packing sensing, computation, communication, and recharging circuits into a penny-scale platform. U-Verse uses a single miniaturized transducer for data exchange and for wireless charging. To predict U-Verse's performance, we (i) derive and experimentally validate a mathematical model of U-Verse's charging efficiency; and (ii) experimentally calculate the resistance-reactance parameters of our ultrasonic transducer and rectifying circuit. We design a matching circuit to maximize the amount of power transferred from the outside. We also go through the challenge of fabricating a full-fledged cm-scale printed circuit board (PCB) for U-Verse. Extensive experimental evaluation indicates that U-Verse (i) is able to recharge a 330mF and 15F energy storage unit - several orders of magnitude higher than existing work - respectively under 20 and 60 minutes at a depth of 5cm; (ii) achieves stored charge duration of up to 610 and 40 hours in case of battery and supercapacitor energy storage, respectively. Finally, U-Verse is demonstrated through (i) a closed-loop application where a periodic sensing/actuation task sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker.

In this paper we present HIRO-NET, Heterogeneous Intelligent Robotic Network. HIRO-NET is an emergency infrastructure-less network tailored to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy (BLE)and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster stricken area. HIRO-NET main goal is to enable users in the disaster to exchange text messages in order to share critical information and request help from first responders. The mesh network discovery problem is analyzed and a network protocol specifically designed to facilitate the exploration process is presented. We show how HIRO-NET robots successfully discover, bridge and interconnect local mesh networks. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g. Twitter, Gmail)to the whole network. Results suggest that a temporary emergency network to cover a metropolitan area can be created in tens of minutes.

Visible Light Ad Hoc Networks (LANETs)is being perceived as an emerging technology to complement Radio Frequency (RF)based ad hoc networks to reduce congestion in the overloaded RF spectrum. LANET is intended to support scenarios requiring dense deployment and high data rates. In Visible Light Communication (VLC), most of the attention has been centered around physical layer with emphasis on point-to-point communication. In this work, we focus on designing a routing protocol specifically to overcome the unique challenges like blockage and deafness that render routes in LANETs highly unstable. Therefore, we propose a cross-layer optimized routing protocol (VL-ROUTE)that interacts closely with the Medium Access Control (MAC)layer to maximize the throughput of the network by taking into account the reliability of routes. To accomplish this in a distributed manner, we carefully formulate a Route Reliability Score (RRS)that can be computed by each node in the network using just the information gathered from its immediate neighbors. Each node computes an RRS for every known sink in the network. RRS of a given node can be considered as an estimate of the probability of reaching a given sink via that node. The RSS value is then integrated to the utility based three-way handshake process used by the MAC protocol (VL-MAC)to mitigate the effects of deafness, blockage, hidden node, and maximize the probability of establishing full-duplex links. All these factors contribute towards maximizing the network throughput. Extensive simulation of VL-ROUTE shows 124% improvement in network throughput over a network that uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA)along with shortest path routing. Additionally, VL-ROUTE also showed up to 21% improvement in throughput over the network that uses VL-MAC along with a geographic routing.

The design and implementation of a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks is reported in this article. Different traffic classes including text, voice, surveillance video, and threat alert among others need to be handled by these networks. Each of these traffic classes have different quality of service (QoS) based deadline requirements. Additionally, these networks are characterized by dynamic channel and traffic conditions that vary with time and location. Even under these conditions, it is critical to receive packets before the deadline expires to make rapid decisions in the battlefield. Therefore, a tactical ad-hoc network should be able to adapt to these requirements and maximize the number of packets delivered to the destination within the specified deadline. A distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources and ensure delivery of packets within the deadline constraints. To this end, a weighted virtual queue (VQ) that is used to construct the network utility function is defined. Accordingly, the optimal session, next hop, transmit power, and frequency is determined by the distributed algorithm to ensure efficient utilization of the available resources. Hence, maximizing the delivery of packets to the intended destination within the specified deadline. The 49 node simulation shows up to 35 percent improvement in effective throughput and 26 percent improvement in reliability as compared to joint ROuting and Spectrum Allocation algorithm (ROSA), which does not adapt according to the deadline requirements of the data flowing through the network. As a secondary objective, this work advances the state of the art of the experimental cross-layer framework to address the challenges involved in having such cross-layer algorithms implemented on a testbed. The required flexibility to change the transmission parameters on-the-fly is provided by the proposed framework. The network is designed to enable the data exchange between neighbors using custom designed control packets (which might be different for different algorithms) since this information is critical for nodes to perform optimization. Cross-layer optimization is achieved by means of data management and control entities that enable information exchange between layers. The practicality of the proposed solution was proven by having the novel algorithm implemented on a five-node software defined radio testbed which leverages the proposed cross-layer framework. In contrast to ROSA, the proposed algorithm demonstrated up to 17 percent improvement in terms of throughput and reliability. The performance improvement achieved is expected to increase on a larger network deployment.

Wireless networks are extremely vulnerable to a plethora of security threats, including eavesdropping, jamming, and spoofing, to name a few. Recently, a number of next-generation cross-layer attacks have been unveiled, which leverage small changes on one network layer to stealthily and significantly compromise another target layer. Since cross-layer attacks are stealthy, dynamic, and unpredictable in nature, novel security techniques are needed. Since models of the environment and attacker's behavior may be hard to obtain in practical scenarios, machine learning techniques become the ideal choice to tackle cross-layer attacks. In this paper, we propose FORMAT, a novel framework to tackle cross-layer security attacks in wireless networks. FORMAT is based on Bayesian learning and made up by a detection and a mitigation component. On one hand, the attack detection component constructs a model of observed evidence to identify stealthy attack activities. On the other hand, the mitigation component uses optimization theory to achieve the desired trade-off between security and performance. The proposed FORMAT framework has been extensively evaluated and compared with existing work by simulations and experiments obtained with a real-world testbed made up by Ettus Universal Software Radio Peripheral (USRP) radios. Results demonstrate the effectiveness of the proposed methodology as FORMAT is able to effectively detect and mitigate the considered cross-layer attacks.

In Section VI of the above paper[1], incorrectly numbered reference citations were introduced during the editing of the paper. The citations should be as follows.

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware.

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a “patch” against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning (ML) and software-defined networking (SDN) will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of ML and SDN to address existing and next-generation IoT security threats.

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (μeNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of μeNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After formulating the optimal problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer as well as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE).

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a uniqueNash equilibrium(NE). Then, we evaluate thePrice of Anarchy(PoA) of the NE, i.e., the efficiency of the NE as compared with the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner’s profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

Wirelessly networked systems of implantable medical devices endowed with sensors and actuators will be the basis of many innovative, sometimes revolutionary therapies. The biggest obstacle in realizing this vision of networked implantable devices is posed by the dielectric nature of the human body, which strongly attenuates radio-frequency (RF) electromagnetic waves. In this paper we present the first hardware and software architecture of an Internet of Medical Things (IoMT) platform with ultrasonic connectivity for intra-body communications that can be used as a basis for building future IoT-ready medical implantable and wearable devices. We show that ultrasonic waves can be efficiently generated and received with low-power and mm-sized components, and that despite the conversion loss introduced by ultrasonic transducers the gap in attenuation between 2.4 GHz RF and ultrasonic waves is still substantial, e.g., ultrasounds offer 70 dB less attenuation over 10 cm. We show that the proposed IoMT platform requires much lower transmission power compared to 2.4 GHz RF with equal reliability in tissues, e.g., 35 dBm lower over 12 cm for 10-3Bit Error Rate (BEr) leading to lower energy per bit and longer device lifetime. Finally, we show experimentally that 2.4 GHz RF links are not functional at all above 12 cm, while ultrasonic links achieve a reliability of 10-6up to 20 cm with less than 0 dBm transmission power.

We are pleased to welcome you to Osaka, Japan to attend IEEE LANMAN 2017, the 23rd IEEE International Symposium on Local and Metropolitan Area Networks. LANMAN began as a workshop focused on local networking technologies, but over more than two decades it has grown into a full fledged symposium covering a broad spectrum of networking issues. This year's technical program covers both wired and wireless networks, with application areas ranging from the Internet of Things, to software defined infrastructures, to cellular networks.

We study the problem of user cost minimization in mobile cloud computing (MCC) networks. We consider a MCC model where multiple brokers assign cloud resources to mobile users. The model is characterized by an heterogeneous cloud architecture (which includes a public cloud and a cloudlet) and by the heterogeneous pricing strategies of cloud service providers. In this setting, we investigate two classes of cloud reservation strategies, i.e., a competitive strategy, and a compete-then-cooperate strategy as a performance bound. We first study a purely competitive scenario where brokers compete to reserve computing resources from remote public clouds (which are affected by long delays) and from local cloudlets (which have limited computational resources but short delays). We provide theoretical results demonstrating the existence of disagreement points (i.e., the equilibrium reservation strategy that no broker has incentive to deviate unilaterally from) and convergence of the best-response strategies of the brokers to disagreement points. We then consider the scenario in which brokers agree to cooperate in exchange for a lower average cost of resources. We formulate a cooperative problem where the objective is to minimize the total average price of all brokers, under the constraint that no broker should pay a price higher than the disagreement price (i.e., the competitive price). We design new globally optimal solution algorithm to solve the resulting non-convex cooperative problem, based on a combination of the branch and bound framework and of advanced convex relaxation techniques. The resulting optimal solution provides a lower bound on the achievable user cost without complete collusion among brokers. Compared with pure competition, we found that (i) noticeable cooperative gains can be achieved over pure competition in markets with a few brokers only, and (ii) the cooperative gain is only marginal in crowded markets, i.e., with a high number of brokers, hence there is no clear incentive for brokers to cooperate.

Biomedical systems of miniaturized implantable sensors and actuators interconnected in an intra-body area network could enable revolutionary clinical applications. Given the well-understood limitations of radio frequency (RF) propagation in the human body, in our previous work we investigated the use of ultrasonic waves as an alternative physical carrier of information, and proposed Ultrasonic WideBand (UsWB), an ultrasonic multipath-resilient integrated physical and medium access control (MAC) layer protocol. In this paper, we discuss the design and implementation of a software-defined testbed architecture for ultrasonic intra-body area networks, and propose the first experimental demonstration of the feasibility of ultrasonic communications in tissue mimicking materials. We first discuss in detail our FPGA-based prototype implementation of UsWB. We then demonstrate how the prototype can flexibly trade performance off for power consumption, and achieve, for bit error rates (BER) no higher than 10-6, either (i) high-data rate transmissions up to 700 kbit/s at a transmit power of -14 dBm (≈ 40 μW), or (ii) low-data rate and lower-power transmissions down to -21 dBm (≈ 8 μW) at 70 kbit/s. We demonstrate that the UsWB MAC protocol allows multiple transmitter-receiver pairs to coexist and dynamically adapt the transmission rate according to channel and interference conditions to maximize throughput while satisfying predefined reliability constraints. We also show how UsWB can be used to enable a video monitoring medical application for implantable devices. Finally, we propose (and validate through experiments) a statistical model of small-scale fading for the ultrasonic intra-body channel.

In this paper, we design a novel multiview video encoding/decoding architecture for wirelessly multiview video streaming applications, e.g., 360 degrees video, Internet of Things (IoT) multimedia sensing, among others, based on distributed video coding and compressed sensing principles. Specifically, we focus on joint decoding of independently encoded compressively sampled multiview video streams. We first propose a novel side-information (SI) generation method based on a new interview motion compensation algorithm for multiview video joint reconstruction at the decoder end. Then, we propose a technique to fuse the received measurements with resampled measurements from the generated SI to perform the final recovery. Based on the proposed joint reconstruction method, we also derive a blind video quality estimation technique that can be used to adapt online the video encoding rate at the sensors to guarantee desired quality levels in multiview video streaming. Extensive simulation results of real multiview video traces show the effectiveness of the proposed fusion reconstruction method with the assistance of SI generated by an interview motion compensation method. Moreover, they also illustrate that the blind quality estimation algorithm can accurately estimate the reconstruction quality.

Wearable medical devices with wireless capabilities have become the cornerstone of many revolutionary digital health applications that promise to predict and treat major diseases by acquiring and processing physiological information. Existing wireless wearable devices are connected through radio frequency electromagnetic wave carriers based on standards, such as Bluetooth or Wi-Fi. However, these solutions tend to almost blindly scale down traditional wireless technologies to the body environment, with little or no attention to the peculiar characteristics of the human body and the severe privacy and security requirements of patients. We contend that this is not the only possible approach, and we introduce U-Wear, the first networking framework for wearable medical devices based on ultrasonic communications. U-Wear encloses a set of physical, data link, and network layer functionalities that can flexibly adapt to application and system requirements to efficiently distribute information between ultrasonic wearable devices. U-Wear also offers reconfiguration functionalities at the application layer to provide a flexible platform to develop medical applications. We design two prototypes that implement U-Wear and operate in the near-ultrasonic frequency range using commercial-off-the-shelf (COTS) speakers and microphones. Despite the limited bandwidth, i.e., about 2 kHz, and COTS hardware components not optimized for operating at high frequency, our prototypes: 1) achieve data rates up to 2.76 kbit/s with bit-error-rate lower than 10-5using a transmission power of 13 dBm (20 mW ); 2) enable multiple nodes to share the medium; and 3) implement reconfigurable processing to extract medical parameters from sensors with high accuracy.

Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.

In this paper, we propose a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks. The proposed algorithm will enable nodes to adapt to various deadline requirements unique to each traffic classes. A tactical ad-hoc network needs to handle a variety of data flowing through the network including voice, surveillance video, threat alert among others. Each of these traffic classes may have different quality of service (QoS) based deadline requirements. It is critical to receive these packets before the deadline expires to make crucial decisions in the battlefield. Therefore, the network should be able to adapt to these requirements and maximize the effective throughput. Accordingly, a distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources to ensure delivery of packets within the deadline constraints. The simulations show up to 35 % improvement in effective throughput and 26 % improvement in reliability as compared to the routing and spectrum allocation algorithm (ROSA).

To cope with the increasing scarcity of spectrum resources, researchers have been working to extend LTE/LTE-A cellular systems to unlicensed bands, leading to so-called unlicensed LTE (U-LTE). However, this extension is by no means straightforward, primarily because the radio resource management schemes used by LTE and by systems already deployed in unlicensed bands are incompatible. Specifically, it is well known that coexistence with scheduled systems like LTE degrades considerably the throughput of Wi-Fi networks that are based on carrier-sense medium access schemes. To address this challenge, we propose for the first time a cognitive coexistence scheme to enable spectrum sharing between U-LTE and Wi-Fi networks, referred to as CU-LTE. The proposed scheme is designed to jointly determine dynamic channel selection, carrier aggregation and fractional spectrum access for U-LTE networks, while guaranteeing fair spectrum access for Wi-Fi based on a newly designed cross-technology fairness criterion. We first derive a mathematical model of the spectrum sharing problem for the coexisting networks; we then design a solution algorithm to solve the resulting fairness constrained mixed integer nonlinear optimization problem. The algorithm, based on a combination of branch and bound and convex relaxation techniques, maximizes the network utility with guaranteed optimality precision that can be set arbitrarily to 1 at the expense of computational complexity. Performance evaluation indicates that near-optimal spectrum access can be achieved with guaranteed fairness between U-LTE and Wi-Fi. Issues regarding implementation of CU-LTE are also discussed.

It is well known that electromagnetic radio-frequency (RF) waves that are the basis of most commercial wireless technologies are largely unsuitable to interconnect deeply implanted medical devices. RF waves are in fact absorbed by aqueous biological tissues and prone to malicious jamming attacks or to environmental interference from pervaslvely deployed RF communication systems; moreover, they pose a potential safety hazard when exposure of tissues is prolonged and at high power. While existing wireless technologies can satisfy the requirements of some specific applications, the root challenge of enabling networked intra-body miniaturized sensors and actuators that communicate through body tissues is largely unaddressed. Considering these limitations, this article proposes a high data rate ultrasonic communication scheme for wireless intra-body networks. The proposed scheme can enable various applications that require high sampling rates such as neural data recording or monitoring of the digestive tract through endoscopic pills. The proposed scheme is based on Orthogonal Frequency-Division Multiplexing (OFDM), which is proven to be robust against frequency-selective channels with relatively long delay spreads like the intra-body ultrasonic channel. The proposed scheme is implemented in a prototype ultrasonic software-radio and demonstrated to achieve data rates up to 28.12 Mbit/s through synthetic phantoms mimicking the ultrasonic propagation characteristics of biological tissues.

The current Internet is under tremendous pressure due to the exponential growth in bandwidth demand, fueled by the transfer of video consumption to online distribution, IPTV, streaming services such as Netflix, and from phone networks to videoconferencing and Skype-like video communications. The Internet has also democratized the creation, distribution and sharing of user-generated video contents through services such as YouTube, Vimeo or Hulu. The situation is further aggravated by the emerging trends of adopting higher definition video streams, requesting more and more bandwidth. Indeed, the Cisco Visual Networking Index (VNI) projects that video consumption will amount to 90% of the global consumer traffic by 2017. Another shift predicted by Cisco VNI is that most data communications will be wireless by 2018.

We study distributed queueing games in interference-limited wireless networks. We formulate the throughput maximization problem via distributed selection of users' transmission thresholds as a Nash Equilibrium Problem (NEP). We first focus on the solution analysis of the NEP and derive sufficient conditions for the existence and uniqueness of a Nash Equilibrium (NE). Then, we develop a general best-response-based algorithmic framework wherein the users can explicitly choose the degree of desired cooperation and signaling, converging to different types of solutions, namely: 1) a NE of the NEP when there is no cooperation among users and 2) a stationary point of the Network Utility Maximization (NUM) problem associated with the NEP, when some cooperation among the users in the form of (pricing) message passing is allowed. Finally, as a benchmark, we design a globally optimal but centralized solution method for the nonconvex NUM problem. Our experiments show that in many scenarios the sum-throughput at the NE of the NEP is very close to the global optimum of the NUM problem, which validates our noncooperative and distributed approach. When the gap of the NE from the global optimality is non negligible (e.g., in the presence of “high” coupling among users), exploiting cooperation among the users in the form of pricing enhances the system performance.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access (which we also refer to as cognitive spectrum access) networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign: 1) portions of the spectrum and 2) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this paper, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems: 1) spectrum management through power allocation with given relay selection strategy; and 2) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a newly designed centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution.

Most research in body area networks to date has focused on traditional RF wireless communications, typically along the body surface. However, the core challenge of enabling networked intra-body communications through body tissues is substantially unaddressed. RF waves are in fact known to suffer from high absorption and to potentially lead to overheating of human tissues. In this paper, we consider the problem of designing optimal network control algorithms for distributed networked systems of implantable medical devices wirelessly interconnected by means of ultrasonic waves, which are known to propagate better than radio-frequency electromagnetic waves in aqueous media such as human tissues. Specifically, we propose lightweight, asynchronous, and distributed algorithms for joint rate control and stochastic channel access designed to maximize the throughput of ultrasonic intra-body area networks under energy constraints. We first develop (and validate through testbed experiments) a statistical model of the ultrasonic channel and of the spatial and temporal variability of ultrasonic interference. Compared to in-air radio frequency (RF), human tissues are characterized by a much lower propagation speed, which further causes unaligned interference at the receiver. It is therefore inefficient to perform adaptation based on instantaneous channel state information (CSI). Based on this model, we formulate the problem of maximizing the network throughput by jointly controlling the transmission rate and the channel access probability over a finite time horizon based only on a statistical characterization of interference. We then propose a fully distributed solution algorithm, and through both simulation and testbed results, we show that the algorithm achieves considerable throughput gains compared with traditional algorithms.

Denial-of-service attacks launched by malicious jammers can pose significant threats to infrastructure-less wireless networks without a centralized controller. While significant recent research efforts have dealt with such attacks and several possible countermeasures have been proposed, little attention has been paid to the idea of cooperative anti-jamming. Inspired by this observation, this paper proposes and studies a cooperative anti-jamming scheme designed to enhance the quality of links degraded by jammers. To achieve this objective, users are allowed to cooperate at two levels. First, they cooperate to optimally regulate their channel access probabilities, so that jammed users gain a higher share of channel utilization. Second, users leverage multiple-input single-output cooperative communication techniques to enhance the throughput of jammed links. The problem of optimal cooperative anti-jamming is formulated as a distributed pricing-based optimization problem, and a best-response algorithm is proposed to solve it in a distributed way. Simulations demonstrate that the proposed algorithm achieves considerable gains (compared with traditional non-cooperative anti-jamming) especially under heavy traffic or high jamming power. Furthermore, the proposed distributed algorithm is shown to achieve close-to-global optimality with moderate traffic load.

Recent advances in energy harvesting devices and low-power embedded systems are enabling energetically self-sustainable wireless sensing systems able to sense, process, and wirelessly transmit environmental data. In such systems, energy resources need to be judiciously allocated to processing and transmission tasks to guarantee high-fidelity reconstruction of the phenomenon under observation while keeping the system operational over extended periods of time. Within this context, this paper addresses the problem of designing efficient policies to control the task of lossy data compression for wireless transmission over fading channels in the presence of a stochastic energy input process and a replenishable energy buffer. As a first contribution, the transmission and energy dynamics of a sensor node implementing practical lossy compression methods are modeled as a constrained Markov decision problem (CMDP). Then, an algorithm is designed to derive optimal compression/transmission policies through a Lagrangian relaxation approach combined with a dichotomic search for the Lagrangian multiplier, while also obtaining theoretical results on the optimal policy structure. Furthermore, a thorough numerical evaluation of optimal and heuristic policies is conducted under different scenarios. Finally, the impact of practical operating conditions, including perfect versus delayed channel state information and power control, is evaluated.

In this paper we design, implement, and experimentally evaluate a wireless software-defined radio platform for cognitive channelization in the presence of narrowband or wideband primary stations. Cognitive channelization is achieved by jointly optimizing the transmission power and the waveform channel of the secondary users. The process of joint resource allocation requires no a-priori knowledge of the transmission characteristics of the primary user and maximizes the signal-to- interference-plus-noise ratio (SINR) at the output of the secondary receiver. This is achieved by designing waveforms that span the whole continuum of available/device-accessible spectrum, while satisfying a peak power constraint for the secondary users and an interference temperature (IT) constraint for the primary users. We build a four-node software-defined radio testbed and experimentally demonstrate in an indoor laboratory environment the theoretical concepts of all-spectrum cognitive channelization in terms of pre-detection SINR and bit-error-rate (BER) at both primary and secondary receivers.

We consider the problem of designing optimal network control algorithms for distributed networked systems of implantable medical devices wirelessly interconnected by means of ultrasonic waves, which are known to propagate better than radio-frequency electromagnetic waves in aqueous media such as human tissues. Specifically, we propose lightweight, asynchronous, and distributed algorithms for joint rate control and stochastic channel access designed to maximize the throughput of ultrasonic intra-body area networks under energy constraints. We first develop (and validate through testbed experiments) a statistical model of the ultrasonic channel and of the spatial and temporal variability of ultrasonic interference. Compared to in-air radio frequency (RF), human tissues show a much lower propagation speed, which further causes unaligned interference at the receiver. It is therefore inefficient to perform adaptation based on instantaneous channel state information (CSI). Based on this model, we formulate the problem of maximizing the network throughput by jointly controlling the transmission rate and the channel access probability over a finite time horizon based only on a statistical characterization of interference. We then propose a fully distributed solution algorithm, and through both simulation and testbed results, we show that the algorithm achieves considerable throughput gains compared with traditional algorithms.

It is our great pleasure to welcome you to Nanjing and to introduce the Proceedings of the 34th edition of the IEEE International Performance Computing and Communications Conference (IPCCC).

Multi-view wireless video streaming has the potential to enable a new generation of efficient and low-power pervasive surveillance systems that can capture scenes of interest from multiple perspectives, at higher resolution, and with lower energy consumption. However, state-of-the-art multi-view coding architectures require relatively complex predictive encoders, thus resulting in high processing complexity and power requirements. To address these challenges, we consider a wireless video surveillance scenario and propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. It also uses estimates of image sparsity to perform efficient rate adaptation and effectively exploit inter-view correlation at the encoder side.

Wearable medical sensing devices with wireless capabilities have become the cornerstone of many revolutionary digital health applications that promise to predict and treat major diseases by acquiring and processing health information. Existing wireless wearable devices are connected through radio frequency (RF) electromagnetic wave carriers based on standards such as Bluetooth or WiFi. However, these solutions tend to almost-blindly scale down traditional wireless technologies to the body environment, with little or no attention to the peculiar characteristics of the human body and the severe privacy and security requirements of patients. We contend that this is not the only possible approach, and we present U-Wear, the first networking framework for wearable medical devices based on ultrasonic communications.

Existing commercial wireless systems are mostly hardware-based, and rely on closed and inflexible designs and architectures. Moreover, despite recent significant algorithmic developments in cross-layer network adaptation and resource allocation, existing network architectures are unable to incorporate most of these advancements. While software-defined radio (SDR) was envisioned as a new paradigm promising radical runtime adaptation through all layers of the networking protocol stack, the reality of the state-of-the-art in wireless networking practice is far from having fulfilled such promise of fast and intelligent reconfigurability and adaptability. Networking research based on the “software-defined radio” paradigm has suffered almost invariably from the lack of adequate and coherently designed abstractions to (i) define networking protocols and their cross-layer interactions across all layers of the protocol stack; (ii) define decision-making algorithms to control such interactions. To address this need, we introduce RcUBe (Real-time Re-configurable Radio), a novel architectural radio framework based on abstractions that offer real-time reconfigurability and optimization capabilities at the PHY, MAC, and network layers of the protocol stack. Unlike state-of-the-art solutions, RcUBe offers a structured methodology at variable levels of abstraction to accommodate implementations of a wide range of network architectures and protocols and complex decision-making in a modular, platform-independent way. RcUBe provides these features through a design structured into four distinct, but interacting planes, namely decision, control, data, and register plane. The broad capabilities of the proposed framework are demonstrated on a network level software-defined radio setup through a range of experiments where RcUBe is used to implement various reconfigurable functionalities of a wireless system at the PHY, MAC, and network layer.

This paper investigates the advantage of adopting a flexible resource control scheme when performing HTTP-based adaptive streaming across LTE systems. To guarantee video fluidity, mobile video streaming is known to require a large bandwidth overhead with respect to the net encoded video rate. The quality of a received video stream is impacted by variations in the size of the transmitted video packets (chunks), and by statistical fluctuations in the data rate at which the allocated downstream wireless channel operates. First, in considering an illustrative video scenario, we show that the chunk size distribution is heavy-tailed, and is well fit by a Gamma distribution. Second, we employ a HAS based proxy video manager and resource controller at the base station node. Based on the channel quality observed and reported by a mobile client, the manager selects the proper channel bandwidth and data rate levels at which to transmit the stream's chunks, in accordance with the selected encoded video rate and the configured Quality of Experience (QoE) level at which the user is targeted to receive the video stream.The communications data rate is also set to assure an acceptable low video reception stall probability. To illustrate the performance of such a dynamic bandwidth allocation scheme, we compare it with an operation that employs a stationary setting of the channel bandwidth, and we compute the gain achieved when such adaptations are performed at the base station node on a chunk by chunk basis. We show by analysis, and confirm by simulations, the improvements achieved in the system's performance behavior through the use of the adaptive resource allocation scheme.

The use of wirelessly internetworked miniaturized biomedical devices is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper, we propose for the first time to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing the theoretical feasibility of using ultrasonic waves in human tissues and by deriving an accurate channel model for ultrasonic intrabody communications. Then, we propose a new ultrasonic transmission and multiple access technique, which we refer to as Ultrasonic WideBand (UsWB). UsWB is based on the idea of transmitting information bits spread over very short pulses following a time-hopping pattern. The short impulse duration results in limited reflection and scattering effects, and the low duty cycle reduces the impact of thermal and mechanical effects, which may be detrimental for human health. We then develop a multiple access technique with distributed control to enable efficient simultaneous access by mutually interfering devices based on minimal and localized information exchange and on measurements at the receiver only. Finally, we demonstrate the performance of UsWB through a multiscale simulator that models the proposed communication system at the acoustic wave level, at the physical (bit) level, and at the network (packet) level. We also validate the simulation results by comparing them to experimental results obtained with a software-defined testbed.

We propose ANC-CDMA, a CDMA-based analog network coding scheme for underwater acoustic sensor networks (UW-ASNs) that can significantly improve the underwater acoustic channel utilization. First, we analyze a unidirectional multihop network, in which two acoustic nodes separated by two hops are assigned the same code-division channel (i.e., spreading code) to transmit concurrently. The packets transmitted by the two nodes will collide at the intermediate (relay) node. However, we show that, by exploiting a priori information, i.e., the interfered packet previously received from one of the nodes, and applying joint channel estimation through pilot supervision along with a newly designed adaptive RAKE receiver, the relay node can cancel the interference before decoding the packet of interest. We then extend our analysis to a bidirectional network, in which two nodes first concurrently transmit their packets to the relay using the same spreading code. The relay amplifies and forwards the received interfered packet to the two nodes. We show that either node can still decode the packet of interest after equalizing the channel effects introduced during the propagation from the relay to itself and jointly estimating multipath affected channels and suppressing the self-interference signal before applying the adaptive RAKE receiver. The proposed ANC-CDMA scheme is implemented in a testbed based on Teledyne Benthos Telesonar SM-975 underwater modems and tested extensively in Lake LaSalle. Experiments and simulations demonstrate that for a 1-2 dB tradeoff in signal-to-noise ratio (SNR), the proposed scheme can significantly improve the channel utilization of a unidirectional and bidirectional networks by up to 50% and 100%, respectively, compared with conventional multiuser DS-CDMA scheme.

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

Biomedical systems of implanted miniaturized sensors and actuators interconnected into an intra-body area network could enable revolutionary healthcare and clinical applications. Given the well-understood limitations of radio frequency (RF) propagation in the human body, in our previous work we investigated the use of ultrasonic waves as an alternative physical carrier of information [1], and proposed Ultrasonic WideBand (UsWB), an ultrasonic multipath-resilient integrated physical and medium access control (MAC) layer protocol [2]. In this paper, we discuss the design and implementation of a software-defined testbed architecture for ultrasonic intra-body area networks, and propose the first experimental demonstration of the feasibility of ultrasonic communications in biological tissues. We first discuss in detail our FPGA-based prototype implementation of UsWB. We then demonstrate how the prototype can flexibly trade performance off for power consumption, and achieve, for bit error rates (BER) no higher than 10-6, either (i) high-data rate transmissions up to 700 kbit/s at a transmit power of -14 dBm (≈ 40 μW), or (ii) low-data rate and lower-power transmissions down to -21dBm (≈ 8μW) at 70kbit/s. Finally, we show how the UsWB MAC protocol allows multiple transmitter-receiver pairs to coexist and dynamically adapt the transmission rate according to channel and interference conditions to maximize throughput while satisfying predefined reliability constraints.

Denial-of-service (DoS) attacks launched by malicious jammers can pose significant threats to infrastructure-less wireless networks where a centralized controller may not be available. While significant recent research efforts have dealt with such attacks and several possible countermeasures have been proposed, little attention has been paid to the idea of cooperative anti-jamming. Inspired by this observation, we propose and study a cooperative anti-jamming scheme designed to enhance the quality of links degraded by jammers. To achieve this objective, users are allowed to cooperate at two levels. First, they cooperate to optimally regulate their channel access probabilities so that jammed users gain a higher share of channel utilization. Second, users leverage multiple-input single-output cooperative communication techniques to enhance the throughput of jammed links. We formulate the problem of optimal cooperative anti-jamming as a distributed pricing-based optimization problem and propose a best response algorithm to solve it in a distributed way. Simulations demonstrate that the proposed algorithm achieves considerable gains (compared to traditional noncooperative antijamming) especially under heavy traffic or high jamming power. Furthermore, by comparing the proposed algorithm with a provably-optimal centralized algorithm, we show that it achieves close-to-global optimality under moderate traffic load.

We propose a new secure underwater acoustic communication scheme designed to let a user (Alice) transmit a confidential message to another user (Bob) in the presence of an eavesdropper (Eve). A typical approach in conventional wireless physical-layer security is to rely on a friendly jammer to jam Eve through artificial noise (AN). Instead, for the first time, we propose a secure underwater communication scheme that relies on cooperative friendly jamming through CDMA-based analog network coding (ANC). The cooperative friendly jammer transmits information using the same spreading code used in the legitimate Alice-Bob link. The information transmitted by the cooperative jammer is known a priori to Bob, but not to Eve. Although the jammer's packet will also interfere at Bob, we show that after jointly estimating the two multipath-affected channels, Bob can suppress the interfering packet and decode Alice's packet, while Eve cannot. We also formulate the problem of joint optimal selection of friendly jammer and power allocation (for Alice and the jammer) that minimize Eve's capability of intercepting the signal while guaranteeing a predefined level of quality of service (QoS) for Bob. The proposed scheme is implemented in a testbed based on Teledyne Benthos Telesonar SM-975 underwater modems and tested extensively in Lake LaSalle at the University at Buffalo. Experiments and simulations demonstrate that, for a given energy budget, the proposed scheme can guarantee much higher bit error rate (BER) at Eve, while creating minimal BER disturbance at Bob, compared to the AN-aided approach.

We study distributed queueing games in interference-limited ad-hoc wireless networks. We formulate system design as a Nash Equilibrium (NE) problem, where the users aim at maximizing their own throughput by choosing the optimal transmission threshold. We first derive conditions for the existence and uniqueness of the NE; then we propose a distributed best-response algorithm solving the game along with its convergence properties. A second contribution of the paper is to develop a Branch and Bound-based (centralized) algorithm solving the associated (nonconvex) social problem, which one can use as benchmark to evaluate the performance of the proposed game theoretical formulation. Interestingly, our numerical results show that the sum-throughput achievable at the NE of the proposed game are very close to that of the social problem, which validates our game theoretical formulation. The performance loss is not negligible only in high interference scenarios. For such cases, we proposed a pricing-based algorithm yielding sum-throughput solutions very close to the globally optimal ones, at the cost of very limited signaling among the users.

Video streaming applications are becoming increasingly popular as low priced video-enabled mobile devices (such as smart phones) become more common. However, traditional video streaming systems are not designed for mobile devices, and require both high computational complexity at the video sensor and very high channel quality to achieve good performance. Our recently proposed compressive video sensing (CVS) video streaming system is a low complexity, low power compressed-sensing-based encoder designed to address these challenges. However, even using CVS, the energy consumption of multimedia sensors is still much higher than that of traditional scalar sensors. In this article, we present a cooperative relay-assisted compressed video sensing (RA-CVS) system that takes advantage of the error resilience of video encoded using CVS to maintain good video quality at the receiver while significantly reducing the required SNR, and therefore the required transmission power at the multimedia sensor node. This system uses the natural error resilience of CS encoded video signals to design a cooperative scheme that directly reduces the mean squared error (MSE) of the reconstructed CS samples representing a video frame, which allows the receiver to correctly reconstruct the video even at very low SNR levels. The proposed system is tested using both simulation and USRP2 testbed evaluation and is shown to outperform traditional cooperative systems in terms of received video quality as a function of channel SNR.

In this paper, we introduce two families of distributed algorithms to control the movement of groups of flying robots that are monitoring an event by moving over the field where the event takes place, while optimizing some specific objective. In order to show the effectiveness of our algorithms, we formulate the Sport Event Filming (SEF) problem. The objective of the problem is to maximize the satisfaction of event viewers while minimizing the distance traveled by the camera-drones. We propose two families of solutions to solve the dynamic version of the problem, where the flying robots do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor)is based on a technique used in robotic systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and travelled distance for the flying robots, when several parameters vary.

We design a video coding and decoding framework for multi-view video systems based on compressed sensing imaging principles. Specifically, we focus on joint decoding of independently encoded compressively-sampled multi-view video streams. We first propose a novel distributed coding/decoding architecture designed to leverage inter-view correlation through joint decoding of the received compressively-sampled frames. At the encoder side, we select one view (referred to as K-view) as a reference for the other views (referred to as CS-views). The video frames of the CS-view are encoded and transmitted at a lower measurement rate than those of the selected K-view. At the decoder side, we generate side information to decode the CS-views as follows. First, each K-view frame is down-sampled and reconstructed, and then compared with the initially reconstructed CS-view frame to obtain an estimate of the inter-view motion vector. The original CS-view measurements are then fused with the generated side image to reconstruct the CS-view frame through a newly designed algorithm that operates in the measurement domain. We also propose a blind video quality estimation method that can be used within the proposed framework to design channel-adaptive rate control algorithms for quality-assured multi-view video streaming. We extensively evaluate the proposed scheme using real multi-view video traces. Results indicate that up to 1.6 dB improvement in terms of PSNR can be achieved by the proposed scheme compared with traditional independent decoding of CS frames.

The use of miniaturized biomedical devices implanted in the human body and wirelessly internetworked is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the well-understood limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper we propose to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing the feasibility of using ultrasonic propagation in human body tissues and by deriving an accurate channel model for ultrasonic intra-body communications. Then, we propose a new ultrasonic transmission and multiple access technique, which we refer to as Ultrasonic WideBand (UsWB). UsWB is based on the idea of transmitting information bits spread over very short pulses following a time-hopping pattern. The short impulse duration results in limited reflection and scattering effects, and its low duty cycle reduces the thermal and mechanical effects, which are detrimental for human health. We then develop a multiple access technique with distributed control to enable efficient simultaneous access by interfering devices based on minimal and localized information exchange and on measurements at the receiver only. Finally, we demonstrate the performance of UsWB through a multi-scale simulator that models the proposed communication system at the acoustic wave level, at the physical (bit) level, and at the network (packet) level.

We develop an empirical model of the Multiview Video Coding (MVC) performance that can be used to identify and separate situations when MVC is beneficial from cases when its use is detrimental in wireless multimedia sensor networks (WMSN). The model predicts the compression performance of MVC as a function of the correlation between cameras with overlapping fields of view. We define the common sensed area (CSA) between different views, and emphasize that it depends not only on geometrical relationships among the relative positions of different cameras, but also on various object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. With these premises, we first experimentally characterize the relationship between MVC compression gain (with respect to single view video coding) and the CSA between views. Our experiments are based on the H.264 MVC standard, and on a low-complexity estimator of the CSA that can be computed with low inter-node signaling overhead. Then, we propose a compact empirical model of the efficiency of MVC as a function of the CSA between views, and we validate the model with different multiview video sequences. Finally, we show how the model can be applied to typical scenarios in WMSN, i.e., to clustered or multi-hop topologies, and we show a few promising results of its application in the definition of cross-layer clustering and data aggregation procedures.

Real-time encoding and error-resilient wireless transmission of multimedia content using traditional encoding techniques requires relatively high processing and transmission power, while pervasive surveillance and monitoring systems often referred to as wireless multimedia sensor networks (WMSNs) are generally composed of low-power, low-complexity devices. To bridge this gap, this article introduces and analyzes a compressive video sensing (CVS) encoder designed to reduce the required energy and computational complexity at the source node. The proposed encoder leverages the properties of compressed sensing (CS) to overcome many of the limitations of traditional encoding techniques, specifically lack of resilience to channel errors, and high computational complexity. Recognizing the inadequacy of traditional rate-distortion analysis to account for the constraints introduced by resource-limited devices, we introduce the notion of rate-energy-distortion, based on which we develop an analytical/empirical model that predicts the received video quality when the overall energy available for both encoding and transmission of each frame of a video is fixed and limited and the transmissions are affected by channel errors. The model allows comparing the received video quality, computation time, and energy consumption per frame of different wireless streaming systems, and can be used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. Based on the proposed model, we show that the CVS video encoder outperforms (in an energy constrained system) two common encoders suitable for a wireless multimedia sensor network environment; H.264/AVC intra and motion JPEG (MJPEG). Extensive results show that CVS is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

Physical-layer cooperation allows leveraging the spatial diversity of wireless channels without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical-layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. A global optimization algorithm based on the branch and bound framework and on convex relaxation of nonconvex constraints is then proposed to solve the problem. The proposed algorithm can provide a theoretical upper bound on the achievable video quality and is shown to provably converge to the optimal solution. In addition, it is shown that cooperative relaying allows nodes to save energy without leading to a perceivable decrease in video quality. Based on this observation, an uncoordinated, distributed, and localized low-complexity algorithm is designed, for which we derive conditions for convergence to a Nash equlibrium (NE) of relay selection. The distributed algorithm is also shown to achieve performance comparable in practice to the optimal solution.

We consider a secondary multi-hop cognitive radio network with decentralized control that operates cognitively to coexist with primary users. We propose a new spread-spectrum management paradigm, in which, unlike mainstream dynamic spectrum access research, digital waveforms are designed to occupy the entire available spectrum, and to adaptively track the interference profile at the receiver to maximize the link capacity while avoiding interference to primary users. In this context, we study the problem of maximizing the network throughput of a multi-hop network through joint routing and spread-spectrum channelization. We first propose a centralized formulation of the network control problem. We then propose an algorithm that can be seen as a distributed localized approximation of the throughput-maximizing policy. We refer to the proposed jointly-designed routing and code-division channelization algorithm as ROCH (Routing and cOde-division CHannelization). Specifically, power and spreading code are jointly selected to maximize the pre-detection secondary \mathrm{SINR} while providing quality of service guarantees to on-going primary and secondary transmissions, while the routing algorithm dynamically selects relays based on the network traffic dynamics and on the achievable data rates on different secondary links. We study the throughput and delay performance of ROCH through a extensive simulation experiments, which demonstrate the appeal of the proposed framework through significant performance gains compared to baseline solutions.

We experimentally characterize the compression efficiency of Multiview Video Coding (MVC) techniques in Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. We derive an empirical model that predicts the compression efficiency as a function of the common sensed area (CSA) between different camera views. We show that the CSA depends not only on geometrical relationships among the relative positions of different cameras, but also on several object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. We then apply the model to a WMSN, where we create clusters based on the CSA as estimated by exchanging local data. Based on this estimates, we form clusters and measure the resulting transmission rate. Numerical simulation results show that building clusters based on a CSA criterion can bring significant performance gains in terms of bandwidth efficiency. The herein presented promising results pave the way for clustering optimization taking into account different networks constraints and conditions.

UnderWater Acoustic Sensor Networks (UW-ASNs) are experiencing a rapid growth, due to their high relevance to commercial and military applications such as oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, and tactical surveillance. However, the design of efficient communication protocols for underwater sensor networks is still an open research problem due to the unique characteristics of the underwater acoustic communication channel such as limited bandwidth, high and variable propagation delays, and significant multipath and scattering. In this paper, we consider multimedia underwater monitoring applications with heterogeneous traffic demands in terms of bandwidth and end-to-end reliability. Distributed routing algorithms are introduced for delay-insensitive and delay-sensitive applications, with the objective of reducing the energy consumption by i) leveraging the tradeoff between multiplexing and diversity gain that characterizes MIMO links, and ii) allocating transmit power on suitable subcarriers according to channel conditions and application requirements. To achieve the objective above, each node jointly i) selects its next hop, ii) chooses a suitable transmission mode, and iii) assigns optimal transmit power on different subcarriers to achieve a target level of Quality of Service (QoS) in a cross-layer fashion. Extensive simulation results demonstrate that our proposed protocol is adaptive to the unique characteristics of the underwater acoustic communication channel, and achieves excellent performance through local cooperations between transmitter and receiver.

This paper presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of Compressed Sensing (CS). The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing-based video encoding for transmission over Wireless Multimedia Sensor Networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among different videos while maximizing the received video quality. It is shown that the rate of Compressed Sensed Video (CSV) can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and test bed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. The test bed results show that the rates converge to stable values in real channels.

Real-time encoding and error-resilient wireless transmission of multimedia content require high processing and transmission power. This paper investigates the rate-distortion performance of video transmission over lossy wireless links for low-complexity multimedia sensing devices with a limited budget of available energy per video frame. An analytical/empirical model is developed to determine the received video quality when the overall energy allowed for both encoding and transmitting each frame of a video is fixed and the received data is affected by channel errors. The model is used to compare the received video quality, computation time, and energy consumption per frame of different wireless streaming systems. Furthermore, it is used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. The proposed model is then applied to compare the energy-constrained wireless streaming performance of three encoders suitable for a wireless multimedia sensor network environment; H.264, motion JPEG (MJPEG) and our recently developed compressed sensing video encoder (CSV). Extensive results show that CSV, thanks to its low complexity, and to a video representation that is inherently resilient to channel errors, is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

The problem of optimal resource allocation to share high-quality multimedia content in cognitive ad hoc networks with cooperative relays is addressed in this paper. Cooperative transmission is a promising technique to increase the capacity of wireless links by exploiting spatial diversity without multiple antennas at each node. However, mainstream research in this field focuses on optimizing physical layer performance measures, with little consideration for application-specific and network-wide performance measures. In this paper, the problem of joint video encoding rate control, power control, relay selection and channel assignment is formulated as a mixed-integer nonlinear problem(MINLP), and a solution algorithm based on a combination of the branch and bound framework and convex relaxation techniques is then proposed. The proposed solution jointly allocates channel, power, video encoding rate, and relay nodes for secondary users to maximize the video quality under the constraints posed by delay-sensitive video applications. Performance evaluation results show that cognitive networks with cooperative relaying can provide considerably higher video quality (in terms of the average peak signal-to-noise ratio (PSNR)) than solutions that do not rely on cooperation or without dynamic spectrum allocation.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign (i) portions of the spectrum and (ii) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate.

Physical-layer cooperation allows leveraging the spatial diversity of the wireless channel without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint video rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. An asynchronous, distributed and localized low-complexity algorithm is designed, based on the iterative solution of convex optimization problems at each individual node. In addition, a global-optimization centralized algorithm based on convex relaxations of non-convex constraints is also proposed as performance benchmark. The distributed algorithm is shown to achieve performance within a few percentage points of the optimal solution. It is also shown that cooperative relaying allows nodes to reduce the overall power consumption without leading to a perceivable decrease in video quality.

The requirements of multimedia underwater monitoring applications with heterogeneous traffic demands in terms of bandwidth and end-to-end reliability are considered in this article. To address these requirements, a new medium access control protocol named UMIMO-MAC is proposed. UMIMO-MAC is designed to i) adaptively leverage the tradeoff between multiplexing and diversity gain according to channel conditions and application requirements, ii) select suitable transmit power to reduce energy consumption, and iii) efficiently exploit the UW channel, minimizing the impact of the long propagation delay on the channel utilization efficiency. To achieve the objectives above, UMIMO-MAC is based on a two-way handshake protocol. Multiple access by simultaneous and co-located transmissions is achieved by using different pseudo-orthogonal spreading codes. An algorithm is proposed that, in a cross-layer fashion, jointly selects optimal transmit power and transmission mode through the cooperation of transmitter and receiver to achieve the desired level of reliability and data rate according to application needs and channel condition. Extensive simulation results show that UMIMO-MAC increases network throughput, decreases channel access delay, and decrease energy consumption compared with existing MAC protocols for UW-ASNs.

We consider the problem of cognitive code-division channelization (simultaneous power and code-channel allocation) for secondary transmission links co-existing with an unknown primary code-division multiple-access (CDMA) system. We first develop a blind primary-user identification scheme to detect the binary code sequences (signatures) utilized by primary users. To create a secondary link we propose two alternative procedures -one of moderate and one of low computational complexity- that optimize the secondary transmitting power and binary code-channel assignment in accordance with the detected primary code channels to avoid "harmful" interference. At the same time, the optimization procedures guarantee that the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear secondary receiver is no less than a certain threshold to meet secondary transmission quality of service (QoS) requirements. The extension of the channelization problem to multiple secondary links is also investigated. Simulation studies presented herein illustrate the theoretical developments.

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to study performance limits and outline key design principles that will be the basis for cross-layer protocol stacks for efficient transport of compressive video streams. Hence, this paper investigates the effect of key video parameters (i.e., quantization, CS samples per frame, and channel encoding rate) on the received video quality of CS images transmitted through a wireless channels. It is shown that, unlike JPEG-encoded images, CS-encoded images exhibit an inherent resiliency to channel errors, caused by the unstructured image representation; this leads to basically zero loss in image quality for random channel bit error rates as high as 10-4, and low degradation up to 10-3. Furthermore, it is shown how, unlike traditional wireless imaging systems, forward error correction is not beneficial for wireless transmission of CS images. Instead, an adaptive parity scheme that drops samples in error is proposed and shown to improve image quality. Finally, we present our initial investigations on a low-complexity, adaptive video encoder that performs low-complexity motion estimation.

Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hop-by-hop. Cooperative transmission exploits spatial diversity without multiple antennas at each node to increase capacity with reliability guarantees. This idea is particularly attractive in wireless environments due to the diverse channel quality and the limited energy and bandwidth resources. With cooperation, source node and relay node cooperatively transmit data to the destination. In such a virtual multiple antenna transmission system, the capacity of the cooperative link is much larger than that of the direct link from source to destination. In this paper, we will study decentralized and localized algorithms for joint dynamic routing, relay assignment, and spectrum allocation under a distributed and dynamic environment.

A cognitive radio network with decentralized control (i.e., a cognitive ad hoc network) is considered in this demonstration. The demo implements a decentralized and localized algorithm for through put maximization through joint routing and interference-avoiding waveform selection. The algorithm adapts to time-varying traffic demands, interference profile, and network topology to locally maximize the achievable data rate while avoiding harmful interference to co-located primary or secondary users. The prototype is based on a cross-layer protocol stack implemented in Python, which leverages GNU Radio for adaptive signal generation on a USRP2 software-defined-radio platform.

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to co-design a low-complexity video encoder based on compressed sensing and a rate-adaptive streaming protocol for wireless video transmission. The proposed rate control scheme is designed with the objectives to maximize the received video quality at the receiver and to prevent network congestion while maintaining fairness between multiple video transmissions. Video distortion is represented through analytical and empirical models and minimized based on a new cross-layer control algorithm that jointly regulates the video encoding rate and the channel coding rate at the physical layer based on the estimated channel quality. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. The proposed scheme is shown to outperform TCP-Friendly Rate Control (TFRC).

Wireless Multimedia Sensor Networks (WMSNs) are distributed systems of wirelessly networked devices that allow retrieving video and audio streams, still images, and scalar sensor data. WMSNs will be a crucial component of mission-critical networks to protect the operation of strategic national infrastructure, provide support to counteract emergencies and threats, and enhance infrastructure for tactical military operations. To enable these applications, WMSNs require the sensor network paradigm to be re-thought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). In this paper, a new cross-layer communication architecture based on the time-hopping impulse radio ultra wide band technology is described, whose objective is to reliably and flexibly deliver QoS to heterogeneous applications in WMSNs, by leveraging and controlling interactions among different layers of the protocol stack according to applications requirements. Simulations show that the proposed system achieves the performance objectives of WMSNs without sacrificing on the modularity of the overall design.

In Wireless Sensor and Actor Networks (WSANs), the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data and perform appropriate actions. WSANs can be thought of as a distributed control system that needs to timely react to sensor information with an effective action. In this paper, coordination and communication problems in WSANs with mobile actors are studied. First, a new location management scheme is proposed to handle the mobility of actors with minimal energy expenditure for the sensors, based on a hybrid strategy that includes location updating and location prediction. Actors broadcast location updates limiting their scope based on Voronoi diagrams, while sensors predict the movement of actors based on Kalman filtering of previously received updates. The location management scheme enables efficient geographical routing, and based on this, an optimal energy-aware forwarding rule is derived for sensor-actor communication. Consequently, algorithms are proposed that allow controlling the delay of the data-delivery process based on power control, and deal with network congestion by forcing multiple actors to be recipients for traffic generated in the event area. Finally, a model is proposed to optimally assign tasks to actors and control their motion in a coordinated way to accomplish the tasks based on the characteristics of the events. Performance evaluation shows the effectiveness of the proposed solution.

Underwater Acoustic Sensor Networks (UW-ASNs) consist of devices with sensing, processing, and communication capabilities that are deployed underwater to perform collaborative monitoring tasks to support a broad range of applications. The enabling communication technology for distances over one hundred meters is wireless acoustic networking because of the high attenuation and scattering affecting radio and optical waves, respectively. In this work, the problem of data gathering is investigated by considering the interactions between the routing functions and the characteristics of the underwater acoustic channel. Two distributed geographical routing algorithms for delay-insensitive and delay-sensitive applications are proposed and shown through simulation experiments to meet the application requirements.

The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. WMSNs require the sensor network paradigm to be re-thought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). A new rate control scheme for WMSNs is introduced in this paper with a two-fold objective: i) maximize the video quality of each individual video stream; ii) maintain fairness in video quality between different video streams. The rate control scheme is based on both analytical and empirical models and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the end-to-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality without sacrificing fairness.

Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where local spectrum resources may change from time to time and hop-by-hop. For this reason, a cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, called ROSA (ROuting and Spectrum Allocation algorithm). Through local control actions, ROSA aims at maximizing the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing bounded BER for the receiver. In addition, the algorithm aims at maximizing the weighted sum of differential backlogs to stabilize the system by giving priority to higher-capacity links with high differential backlog. The proposed algorithm is distributed, computationally efficient, and with bounded BER guarantees. ROSA is shown through discrete-event packet-level simulations to outperform baseline solutions leading to a high throughput, low delay, and fair bandwidth allocation.

The requirements of multimedia underwater monitoring applications with heterogeneous traffic demands in terms of bandwidth and end-to-end reliability are considered in this article. To address these requirements, a new medium access control protocol named UMIMO-MAC is proposed. UMIMO-MAC is designed to i) adaptively leverage the tradeoff between multiplexing and diversity gain according to channel conditions and application requirements, ii) select suitable transmit power to reduce energy consumption, and iii) efficiently exploit the UW channel, minimizing the impact of the long propagation delay on the channel utilization efficiency.

UnderWater Acoustic Sensor Networks (UW-ASNs) consist of sensors and Autonomous Underwater Vehicles (AUVs) performing collaborative monitoring tasks. In this article, UWMAC, a distributed Medium Access Control (MAC) protocol designed for UW-ASNs, is introduced. The proposed MAC protocol is a transmitter-based Code Division Multiple Access (CDMA) scheme that incorporates a novel closed-loop distributed algorithm to jointly set the optimal transmit power and code length. CDMA is the most promising physical layer and multiple access technique for UW-ASNs because it is robust to frequency-selective fading, it compensates for the effect of multipath at the receiver, and it allows receivers to distinguish among signals simultaneously transmitted by multiple devices. UW-MAC aims at achieving three objectives, i.e., guarantee i) high network throughput, ii) low channel access delay, and iii) low energy consumption. It is demonstrated that UW-MAC simultaneously achieves these three objectives in deep water communications (where the ocean depth is more than 100 m), which are usually not severely affected by multipath. In shallow water communications, which may be heavily affected by multipath, it dynamically finds the optimal trade-off among these objectives according to the application requirements. UW-MAC is the first protocol that leverages CDMA properties to achieve multiple access to the scarce underwater bandwidth, while other protocols tailored for this environment have considered CDMA merely from a physical layer perspective. Experiments show that UW-MAC outperforms many existing MAC protocols tuned for the underwater environment under different architecture scenarios and simulation settings.

Wireless multimedia sensor networks (WMSNs) are networks of wirelessly interconnected devices that allow retrieving video and audio streams, still images, and scalar sensor data. WMSN require the sensor network paradigm to be rethought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). In this paper, a new cross-layer communication architecture based on the time-hopping impulse radio ultra wide band technology is described, designed to reliably and flexibly deliver QoS to heterogeneous applications in WMSNs, by leveraging and controlling interactions among different layers of the protocol stack according to applications requirements. Simulations show that the proposed system achieves the performance objectives of WMSNs without sacrificing on design modularity.

The availability of low-cost hardware is enabling the development of wireless multimedia sensor networks (WMSNs), i.e., networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, ongoing research onprototypesofmultimediasensorsand their integration intotestbedsforexperimentalevaluationof algorithms and protocols for WMSNs are described. Furthermore, open research issues and future research directions, both at the device level and at the testbed level, are discussed. This paper is intended to be a resource for researchers interested in advancing the state-of-the-art in experimental research on wireless multimedia sensor networks.

An IEEE 802.15.4-based wireless sensor network is considered, and the relationship between the IEEE 802.15.4 topology formation mechanism and possible routing strategies at the network layer is studied. Two alternative routing schemes proposed in the framework of the ZigBee alliance are analyzed. The first is the well-known ad-hoc on demand distance vector (AODV) routing protocol, which was designed for highly dynamic application scenarios in wireless ad-hoc networks. The second is a tree-based routing scheme based on a hierarchical structure established among nodes during the network formation phase. This latter approach, referred to as HERA (hierarchical routing algorithm) in the paper, routes packets from sensors to sink based on the parent-child relationships established by the IEEE 802.15.4 topology formation procedure. An extensive simulation analysis is carried out to compare HERA and AODV. It is shown that a hierarchical routing scheme based on the MAC association procedures offers several benefits with respect to reactive routing in typical sensor network applications. Moreover, it is to be noted that most sensor network scenarios are concerned with delivery of packets from a series of static sensors to a single, static, sink.

In this paper, coordination and communication problems in wireless sensor and actor networks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordination model is proposed based on an event-driven partitioning paradigm. Sensors are partitioned into different sets, and each set is constituted by a data-delivery tree associated with a different actor. The optimal solution for the partitioning strategy is determined by mathematical programming, and a distributed solution is proposed. In addition, a new model for the actor-actor coordination problem is introduced. The actor coordination is formulated as a task assignment optimization problem for a class of coordination problems in which the area to be acted upon needs to be optimally split among different actors. An auction-based distributed solution of the problem is also presented. Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be achieved in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.

Underwater sensor networks consist of sensors and vehicles deployed to perform collaborative monitoring tasks over a given region. Underwater sensor networks will find applications in oceano-graphic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical surveillance, and mine reconnaissance. Underwater acoustic networking is the enabling technology for these applications. In this paper, an architecture for three-dimensional underwater sensor networks is considered, and a model characterizing the acoustic channel utilization efficiency is introduced, which allows investigating some fundamental characteristics of the underwater environment. In particular, the model allows setting the optimal packet size for underwater communications given monitored volume, density of the sensor network, and application requirements. Moreover, the problem of data gathering is investigated at the network layer by considering the cross-layer interactions between the routing functions and the characteristics of the underwater acoustic channel. Two distributed routing algorithms are introduced for delay-insensitive and delay-sensitive applications. The proposed solutions allow each node to select its next hop, with the objective of minimizing the energy consumption taking the varying condition of the underwater channel and the different application requirements into account. The proposed routing solutions are shown to achieve the performance targets by means of simulation.

In wireless sensor and actor networks (WSANs), the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data and perform appropriate actions. In this paper, the coordination and communication problems in WSANs with mobile actors are studied. A hybrid location management scheme is introduced to handle the mobility of actors with minimal energy expenditure. Actors broadcast location updates limiting their scope based on Voronoi diagrams, whereas sensors predict the movement of actors based on Kalman filtering of previously received updates. An optimal energy-aware forwarding rule is then derived for sensor-actor communication, based on geographical routing. The proposed scheme allows controlling the delay of the data-delivery process based on power control, and deals with network congestion by forcing multiple actors to be recipients for traffic generated in the event area. The motion of actors is coordinated to optimally accomplish the tasks based on the characteristics of the events

Wireless Sensor and Actor Networks (WSANs) are composed of a large number of heterogeneous nodes called sensors and actors. The collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while the role of actors is to collect and process sensor data and perform appropriate actions.In this paper, a coordination framework for WSANs is addressed. A new sensor-actor coordination model is proposed, based on an event-driven clustering paradigm in which cluster formation is triggered by an event so that clusters are created on-the-fly to optimally react to the event itself and provide the required reliability with minimum energy expenditure. The optimal solution is determined by mathematical programming and a distributed solution is also proposed. In addition, a new model for actor-actor coordination is introduced for a class of coordination problems in which the area to be acted upon is optimally split among different actors. An auction-based distributed solution of the problem is also presented.Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be reached in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.

Since ad hoc and sensor networks can be composed of a very large number of devices, the scalability of network protocols is a major design concern. Furthermore, network protocols must be designed to prolong the battery lifetime of the devices. However, most existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. In a geographical routing algorithm, data packets are forwarded by a node to its neighbor based on their respective positions. The neighborhood of each node is constituted by the nodes that lie within a certain radio range. Thus, from the perspective of a node forwarding a packet, the next hop depends on the width of the neighborhood it perceives. The analytical framework proposed in this paper allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. A wider topology knowledge may improve the energy efficiency of the routing tasks but increases the cost of topology information due to signaling packets needed to acquire this information. The problem of determining the optimal topology knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is shown that the problem is intrinsically localized, i.e., a limited topology knowledge is sufficient to make energy efficient forwarding decisions. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover, a new forwarding scheme, partial topology knowledge forwarding (PTKF), is introduced, and shown to outperform other existing schemes in typical application scenarios. A probe-based distributed protocol for knowledge range adjustment (PRADA) is finally introduced that allows each node to efficiently select online its topology knowledge range. PRADA is shown to rapidly converge to a near-optimal solution.

In a scenario where different radio technologies cooperate to provide access to the Internet and advanced wireless services to mobile and nomadic users, Bluetooth is considered an enabling technology for the personal area networking segment. To this aim, Bluetooth devices should be able to set-up a wireless multihop network with given topological characteristics and with limited formation delay. In this work SHAPER, a distributed algorithm for tree scatternet formation, is enhanced to work in a dynamic environment where devices enter and leave the personal area network and require a fast interconnection with an optimized topology. We define a procedure (called SHAPER-OPT) that produces a meshed topology applying a distributed scatternet optimization algorithm (DSOA) on the network built by SHAPER. Nodes are shown to be able to easily join or leave the scatternet at any time, without compromising the long term connectivity. Benefits brought by DSOA are shown by performance analysis, while the delay for network set-up and reconfiguration in dynamic environments is shown to be within acceptable bounds.

Since sensor networks can be composed of a very large number of nodes, the developed protocols for these networks must be scalable. Moreover, these protocols must be designed to prolong the battery lifetime of the nodes. Typical existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. For this reason, a novel analytical framework is introduced. In a geographical routing algorithm, the packets are forwarded by a node to its neighbor based on their respective positions. The proposed framework allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover partial topology knowledge forwarding, a new forwarding scheme, is introduced. A wider topology knowledge can improve the energy efficiency of the routing tasks but can increase the cost of topology information due to signaling packets that each node must transmit and receive to acquire this information, especially in networks with high mobility. The problem of determining the optimal knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is demonstrated that the problem is intrinsically localized, i.e., a limited knowledge of the topology is sufficient to take energy efficient forwarding decisions, and that the proposed forwarding scheme outperforms the others in typical application scenarios. For online solution of the problem, a probe-based distributed protocol which allows each node to efficiently select its topology knowledge, is introduced and shown to converge to a near-optimal solution very fast.

Bluetooth is an enabling technology for Personal Area Networks. A scatternet is an ad hoc network created by interconnecting several Bluetooth piconets, each with at most eight devices. Each piconet uses a different radio channel constituted by a frequency hopping code. The way the devices are grouped in different piconets and the way the piconets are interconnected greatly affect the performance of the scatternet in terms of capacity, data transfer delay, and energy consumption. There is a need to develop distributed scatternet formation algorithms, which guarantee full connectivity of the devices, reconfigure the network due to mobility and failure of devices, and interconnect them such a way to create an optimal topology to achieve gainful performance. The contribution of this paper is to provide an integrated approach for scatternet formation and quality-of-service support (called SHAPER-OPT). To this aim, two main procedures are proposed. First, a new scatternet formation algorithm called self-healing algorithm producing multihop Bluetooth scatternets (SHAPER) is developed which forms tree-shaped scatternets. A procedure that produces a meshed topology applying a distributed scatternet optimization algorithm (DSOA) on the network built by SHAPER is then defined. Performance evaluation of the proposed algorithms, and of the accordingly created scatternets, is carried out by using ns2 simulation. Devices are shown to be able to join or leave the scatternet at any time, without compromising the long term connectivity. Delay for network setup and reconfiguration in dynamic environments is shown to be within acceptable bounds. DSOA is also shown to be easy to implement and to improve the overall network performance.

This paper deals with scatternet formation in Bluetooth. A scatternet is an ad hoc network of Bluetooth devices. Some works in the literature rely on the single-hop hypothesis, i.e., all devices are in radio visibility of each other. Other works refer to the more likely circumstance that devices are scattered in an area where some of them can not directly communicate. A challenging issue in this latter scenario (often referred to as multi-hop) is the design of a formation algorithm that: i) operates in a distributed way; ii) dynamically adapts the topology to the mobility of devices; iii) forms a scatternet with given topological properties. In this paper a distributed algorithm for scatternet formation that gives rise to a tree-like structure is introduced. The algorithm is shown to present three key properties that make it innovative with respect to the literature in the field: i) it is fully distributed and asynchronous; ii) it can be applied in a multi-hop environment; iii) it operates in order to dynamically adapt the topology to nodes' mobility and failures. The key steps and rules of the algorithm are described and performance results obtained by simulation are discussed.

To fully exploit the capabilities of Bluetooth for the deployment of wireless ad-hoc networks, the scatternet concept has been proposed. A scatternet is constituted by an overlapping of simple structures called piconets, each composed of up to eight devices sharing the same radio channel. A scatternet may present different topological configurations, depending on the number of composing piconets, the role of involved devices and the configuration of the links. This paper presents a general methodology for scatternet formation and proposes metrics that can be used to evaluate scatternet performance. Several numerical examples are presented and discussed, highlighting the impact of metric selection on scatternet performance.