Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access

Besides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis, 1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information recovery than that achieved by existing approaches. 2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. 3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead. 4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

A survey of symbiotic radio: Methodologies, applications, and future directions

The sixth generation (6G) wireless technology aims to achieve global connectivity with environmentally sustainable networks to improve the overall quality of life. The driving force behind these networks is the rapid evolution of the Internet of Things (IoT), which has led to a proliferation of wireless applications across various domains through the massive deployment of IoT devices. The major challenge is to support these devices with limited radio spectrum and energy-efficient communication. Symbiotic radio (SRad) technology is a promising solution that enables cooperative resource-sharing among radio systems through symbiotic relationships. By fostering mutualistic and competitive resource sharing, SRad technology enables the achievement of both common and individual objectives among the different systems. It is a cutting-edge approach that allows for the creation of new paradigms and efficient resource sharing and management. In this article, we present a detailed survey of SRad with the goal of offering valuable insights for future research and applications. To achieve this, we delve into the fundamental concepts of SRad technology, including radio symbiosis and its symbiotic relationships for coexistence and resource sharing among radio systems. We then review the state-of-the-art methodologies in-depth and introduce potential applications. Finally, we identify and discuss the open challenges and future research directions in this field

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

Joint access-backhaul mechanisms in 5G cell-less architectures

Older generations of wireless networks, such as 1G and 2G were deployed using leased line, copper or fibre line as backhaul. Later, in 3G and 4G, microwave wireless links have also worked as backhaul links while the backbone of the network was still wireline-based. However, due to multiple different use cases and deployment scenarios of 5G, solo wireline based backhaul network is not a cost-efficient option for the operators anymore. For cost-efficient and fast deployment, wireless backhaul options are very attractive. As drawbacks, wireless backhaul links have capacity and distance limitations. To take the advantages of both the solutions, i.e., wired and wireless, 5G transport networks are anticipated to be a heterogeneous, complex, and with stringent performance requirements. To address the aforementioned challenges, wireless backhaul options are providing more attractive solutions, and hence, technologies using the same resources (e.g., frequency channels) may be used by both access and backhaul networks. In this scenario, blurring the separation line between access and backhaul networks allows resource sharing and cooperation between both the networks and minimizes the network deployment and maintenance cost significantly. Therefore, in 5G, the access and backhaul networks cannot be seen as separate entities; rather, we seek to integrate them together to ensure the best use of resources. In this thesis, firstly, we investigate the challenges and potential technologies of 5G transport network. Later, to address these challenges, we identify and present different approaches to perform joint access-backhaul mechanism. An initial performance evaluation of access-aware backhaul optimization is presented, where backhaul network is dynamically assigned with the required resources to serve the dynamic requirements of a 5G access network. The evaluation results and discussions manifest the resource efficiency of joint access-backhaul mechanisms. Functional splits in different layers of the access network comes as an intelligent solution to reduce the enormous capacity requirements of the transport network in a centralized radio access network approach, which tends to centralize almost all the functionalities into a central unit, leaving only radio frequency functions at the access points. From the joint access-backhaul mechanism perspective, we propose a novel technique, which takes the benefit of functional splits at physical layer, to design a heterogeneous transport network in an economical budget-limited and capacity-limited scenario. Till today, the limited capacity of the wireless backhaul links remains a challenge, and hence, frequency spectrum becomes scarce, and requires efficient utilization. To address this challenge, a joint spectrum sharing technique to implement joint accessbackhaul mechanism is presented. Evaluation results show that our proposed joint spectrum sharing technique, where spectrum allocation in the backhaul network follows the access network's traffic load, is fair and efficient in terms of spectrum utilization. We also propose a machine learning technique, which analyses data from a real network and estimates access network's traffic pattern, and subsequently, assigns bandwidth in the access network according to the traffic estimations. Presented evaluation results show that a well-trained machine learning model can be very efficient to obtain an efficient utilization of frequency spectrum.Las primeras generaciones de redes móviles, se implementaron utilizando líneas de cobre o fibra para la conexión entre la red de acceso y el núcleo de la red (conexión backhaul). Más tarde, los enlaces inalámbricos también han funcionado como backhaul mientras que la columna vertebral de la red seguía basada en cable. Sin embargo, debido a los múltiples escenarios de implementación de 5G, una red de backhaul basada solamente en cable ya no es una opción rentable para los operadores. Para una implementación rentable y rápida, las opciones de backhaul inalámbrico son muy atractivas. Como inconvenientes, los enlaces backhaul inalámbricos tienen limitaciones de capacidad y distancia. Para aprovechar las ventajas de ambas soluciones, es decir, cableadas e inalámbricas, se prevé que las redes de transporte 5G sean heterogéneas, complejas y con estrictos requisitos de rendimiento. Para abordar los desafíos antes mencionados, las opciones de backhaul inalámbrico brindan soluciones más atractivas y, por lo tanto, las tecnologías que usan los mismos recursos (por ejemplo, canales de frecuencia) pueden usarse tanto en las redes de acceso como en las de backhaul. En este escenario, desdibujar la línea de separación entre las redes de acceso y backhaul permite el intercambio de recursos y la cooperación entre ambas redes, y minimiza significativamente los costes de implementación y mantenimiento de la red. Por lo tanto, en 5G las redes de acceso y backhaul no pueden verse como entidades separadas; más bien consideraremos su integración para asegurar el mejor uso de los recursos. En esta tesis, en primer lugar, investigamos los desafíos y las tecnologías potenciales para la implementación de la red de backhaul 5G. Más tarde, para abordar dichos desafíos, identificamos diferentes enfoques para un mecanismo conjunto de gestión de la red de acceso y backhaul. Se presenta una evaluación de rendimiento inicial para la optimización de backhaul que tiene en cuenta el estado de la red de acceso, donde la red de backhaul se equipa dinámicamente con los recursos necesarios para cumplir con los requisitos de la red de acceso 5G. Los resultados de la evaluación manifiestan la mayor eficiencia de los mecanismos de gestión de recursos que consideran redes de acceso y backhaul conjuntamente. Las divisiones funcionales en diferentes capas de la red de acceso (functional splits) se presentan como una solución inteligente para reducir los enormes requisitos de capacidad de la red de transporte en un enfoque de red de acceso, que tiende a centralizar casi todas las funcionalidades en una unidad central, dejando solo las funciones más relacionadas con la transmisión/recepción de señales en los puntos de acceso. Desde la perspectiva del mecanismo conjunto de red de acceso y backhaul, proponemos una técnica novedosa, que aprovecha las divisiones funcionales en la capa física para diseñar una red de transporte heterogénea con un presupuesto económico y un escenario de capacidad limitada. Hasta el día de hoy, la capacidad limitada de los enlaces inalámbricos sigue siendo un desafío, dado que el espectro de frecuencias es escaso y requiere una utilización eficiente. Para hacer frente a este desafío, se presenta una técnica de gestión de recursos espectrales compartidos entre red de acceso y backhaul. Los resultados de la evaluación muestran que nuestra propuesta, donde la asignación de espectro en la red de backhaul se hace de acuerdo a la carga de tráfico de la red de acceso, es justa y eficiente. También proponemos una técnica de aprendizaje automático, que analiza datos de una red real y estima el patrón de tráfico de la red de acceso para, posteriormente, asignar ancho de banda en la red de acceso de acuerdo con dichas estimaciones. Los resultados de la evaluación presentados muestran que un modelo de aprendizaje automático bien entrenado puede ser una herramienta muy útil a la hora de obtener una utilización eficiente del espectro de frecuencias.Postprint (published version