Spectrum sharing systems for improving spectral efficiency in cognitive cellular network

Since spectrum is the invisible infrastructure that powers the wireless communication, the demand has been exceptionally increasing in recent years after the implementation of 4G and immense data requirements of 5G due to the applications, such as Internet-of-Things (IoT). Therefore, the effective optimization of the use of spectrum is immediately needed than ever before. The spectrum sensing is the prerequisite for optimal resource allocation in cognitive radio networks (CRN). Therefore, the spectrum sensing in wireless system with lower latency requirements is proposed first. In such systems with high spatial density of the base stations and users/objects, spectrum sharing enables spectrum reuse across very small regions. The proposed method in this Thesis is a multi-channel cooperative spectrum sensing technique, in which an independent network of sensors, namely, spectrum monitoring network, detects the spectrum availability. The locally aggregated decision in each zone associated with the zone aggregator (ZA) location is then passed to a decision fusion centre (DFC). The secondary base station (SBS) accordingly allocates the available channels to secondary users to maximize the spectral efficiency. The function of the DFC is formulated as an optimization problem with the objective of maximizing the spectral efficiency. The optimal detection threshold is obtained for different cases with various spatial densities of ZAs and SBSs. It is further shown that the proposed method reduces the spectrum sensing latency and results in a higher spectrum efficiency. Furthermore, a novel power allocation scheme for multicell CRN is proposed where the subchannel power allocation is performed by incorporating network-wide primary system communication activity. A collaborative subchannel monitoring scheme is proposed to evaluate the aggregated subchannel activity index (ASAI) to indicate the activity levels of primary users. Two utility functions are then defined to characterize the spectral efficiency (SE) and energy efficiency (EE) as a function of ASAI to formulate a utility maximization problem. The optimal transmit power allocation is then obtained with the objective of maximizing the total utility at the SBS, subject to maximum SBS transmit power and collision probability constraint at the primary receivers. Since optimal EE and SE are two contradicting objectives to obtain the transmit power allocation, the design approach to handle both EE and SE as a function of common network parameter, i.e., ASAI, is provided which ultimately proves the quantitative insights on efficient system design. Extensive simulation results confirm the analytical results and indicate a significant improvement in sensing latency and accuracy and a significant gain against the benchmark models on the rate performance, despite the proposed methods perform with lower signalling overhead

Spectrum sharing systems for improving spectral efficiency in cognitive cellular network

Since spectrum is the invisible infrastructure that powers the wireless communication, the demand has been exceptionally increasing in recent years after the implementation of 4G and immense data requirements of 5G due to the applications, such as Internet-of-Things (IoT). Therefore, the effective optimization of the use of spectrum is immediately needed than ever before. The spectrum sensing is the prerequisite for optimal resource allocation in cognitive radio networks (CRN). Therefore, the spectrum sensing in wireless system with lower latency requirements is proposed first. In such systems with high spatial density of the base stations and users/objects, spectrum sharing enables spectrum reuse across very small regions. The proposed method in this Thesis is a multi-channel cooperative spectrum sensing technique, in which an independent network of sensors, namely, spectrum monitoring network, detects the spectrum availability. The locally aggregated decision in each zone associated with the zone aggregator (ZA) location is then passed to a decision fusion centre (DFC). The secondary base station (SBS) accordingly allocates the available channels to secondary users to maximize the spectral efficiency. The function of the DFC is formulated as an optimization problem with the objective of maximizing the spectral efficiency. The optimal detection threshold is obtained for different cases with various spatial densities of ZAs and SBSs. It is further shown that the proposed method reduces the spectrum sensing latency and results in a higher spectrum efficiency. Furthermore, a novel power allocation scheme for multicell CRN is proposed where the subchannel power allocation is performed by incorporating network-wide primary system communication activity. A collaborative subchannel monitoring scheme is proposed to evaluate the aggregated subchannel activity index (ASAI) to indicate the activity levels of primary users. Two utility functions are then defined to characterize the spectral efficiency (SE) and energy efficiency (EE) as a function of ASAI to formulate a utility maximization problem. The optimal transmit power allocation is then obtained with the objective of maximizing the total utility at the SBS, subject to maximum SBS transmit power and collision probability constraint at the primary receivers. Since optimal EE and SE are two contradicting objectives to obtain the transmit power allocation, the design approach to handle both EE and SE as a function of common network parameter, i.e., ASAI, is provided which ultimately proves the quantitative insights on efficient system design. Extensive simulation results confirm the analytical results and indicate a significant improvement in sensing latency and accuracy and a significant gain against the benchmark models on the rate performance, despite the proposed methods perform with lower signalling overhead

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

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Radio and computing resource management in SDR clouds

The aim of this thesis is defining and developing the concept of an efficient management of radio and computing resources in an SDR cloud. The SDR cloud breaks with today's cellular architecture. A set of distributed antennas are connected by optical fibre to data processing centres. The radio and computing infrastructure can be shared between different operators (virtualization), reducing costs and risks, while increasing the capacity and creating new business models and opportunities. The data centre centralizes the management of all system resources: antennas, spectrum, computing, routing, etc. Specially relevant is the computing resource management (CRM), whose objective is dynamically providing sufficient computing resources for a real-time execution of signal processing algorithms. Current CRM techniques are not designed for wireless applications. We demonstrate that this imposes a limit on the wireless traffic a CRM entity is capable to support. Based on this, a distributed management is proposed, where multiple CRM entities manage a cluster of processors, whose optimal size is derived from the traffic density. Radio resource management techniques (RRM) also need to be adapted to the characteristics of the new SDR cloud architecture. We introduce a linear cost model to measure the cost associated to the infrastructure resources consumed according to the pay-per-use model. Based on this model, we formulate the efficiency maximization power allocation problem (EMPA). The operational costs per transmitted bit achieved by EMPA are 6 times lower than with traditional power allocation methods. Analytical solutions are obtained for the single channel case, with and without channel state information at the transmitter. It is shown that the optimal transmission rate is an increasing function of the product of the channel gain with the operational costs divided by the power costs. The EMPA solution for multiple channels has the form of water-filling, present in many power allocation problems. In order to be able to obtain insights about how the optimal solution behaves as a function of the problem parameters, a novel technique based on ordered statistics has been developed. This technique allows solving general water-filling problems based on the channel statistics rather than their realization. This approach has allowed designing a low complexity EMPA algorithm (2 to 4 orders of magnitude faster than state-of-the-art algorithms). Using the ordered statistics technique, we have shown that the optimal transmission rate behaviour with respect to the average channel gains and cost parameters is equivalent to the single channel case and that the efficiency increases with the number of available channels. The results can be applied to design more efficient SDR clouds. As an example, we have derived the optimal ratio of number of antennas per user that maximizes the efficiency. As new users enter and leave the network, this ratio should be kept constant, enabling and disabling antennas dynamically. This approach exploits the dynamism and elasticity provided by the SDR cloud. In summary, this dissertation aims at influencing towards a change in the communications system management model (typically RRM), considering the introduction of the new infrastructure model (SDR cloud), new business models (based on Cloud Computing) and a more conciliatory view of an efficient resource management, not only focused on the optimization of the spectrum usage.El objetivo de esta tesis es de nir y desarrollar el concepto de gesti on e ciente de los recursos de radio y computaci on en un SDR cloud. El SDR cloud rompe con la estructura del sistema celular actual. Un conjunto de antenas distribuidas se conectan a centros de procesamiento mediante enlaces de comunicaci on de bra optica. La infraestructura de radio y procesamiento puede ser compartida entre distintos operadores (virtualizacion), disminuyendo costes y riesgos, aumentando la capacidad y abriendo nuevos modelos y oportunidades de negocio. La centralizaci on de la gesti on del sistema viene soportada por el centro de procesamiento, donde se realiza una gesti on de todos los recursos del sistema: antenas, espectro, computaci on, enrutado, etc. Resulta de especial relevancia la gesti on de los recursos de computaci on (CRM) cuyo objetivo es el de proveer, din amicamente, de su cientes recursos de computaci on para la ejecuci on en tiempo real de algoritmos de procesado del señal. Las t ecnicas actuales de CRM no han sido diseñadas para aplicaciones de comunicaciones. Demostramos que esta caracter stica impone un l ímite en el tr áfi co que un gestor CRM puede soportar. En base a ello, proponemos una gesti on distribuida donde m ultiples entidades CRM gestionan grupos de procesadores, cuyo tamaño optimo se deriva de la densidad de tr áfi co. Las t ecnicas actuales de gesti on de recursos radio (RRM) tambi en deben ser adaptadas a las caracter sticas de la nueva arquitectura SDR cloud. Introducimos un modelo de coste lineal que caracteriza los costes asociados al consumo de recursos de la infraestructura seg un el modelo de pago-por-uso. A partir de este modelo, formulamos el problema de asignaci on de potencia de m axima e ciencia (EMPA). Mediante una asignaci on EMPA, los costes de operaci on por bit transmitido son del orden de 6 veces menores que con los m etodos tradicionales. Se han obtenido soluciones anal ticas para el caso de un solo canal, con y sin informacion del canal disponible en el transmisor, y se ha demostrado que la velocidad optima de transmisi on es una funci on creciente del producto de la ganancia del canal por los costes operativos dividido entre los costes de potencia. La soluci on EMPA para varios canales satisface el modelo "water- lling", presente en muchos tipos de optimizaci on de potencia. Con el objetivo de conocer c omo esta se comporta en funci on de los par ametros del sistema, se ha desarrollado una t ecnica nueva basada en estadí sticas ordenadas. Esta t ecnica permite solucionar el problema del water- lling bas andose en la estadí stica del canal en vez de en su realizaci on. Este planteamiento, despu es de profundos an alisis matem aticos, ha permitido desarrollar un algoritmo de asignaci on de potencia de baja complejidad (2 a 4 ordenes de magnitud m as r apido que el estado del arte). Mediante esta t ecnica, se ha demostrado que la velocidad optima de transmisi on se comporta de forma equivalente al caso de un solo canal y que la e ciencia incrementa a medida que aumentan el numero de canales disponibles. Estos resultados pueden aplicarse a diseñar un SDR cloud de forma m as e ciente. A modo de ejemplo, hemos obtenido el ratio optimo de n umero de antenas por usuario que maximiza la e ciencia. A medida que los usuarios entran y salen de la red, este ratio debe mantenerse constante, a fin de mantener una efi ciencia lo m as alta posible, activando o desactivando antenas din amicamente. De esta forma se explota completamente el dinamismo ofrecido por una arquitectura el astica como el SDR cloud. En de nitiva, este trabajo pretende incidir en un cambio del modelo de gesti on de un sistema de comunicaciones (t ípicamente RRM) habida cuenta de la introducci on de una nueva infraestructura (SDR cloud), nuevos modelos de negocio (basados en Cloud Computing) y una visi on m as integradora de la gesti on e ciente de los recursos del sistema, no solo centrada en la optimizaci on del uso del espectro

Joint multicell subchannel assignment with interference control and resource fairness in multiband OFDMA cellular networks

Optimized distribution of downlink resources to users is a key challenge in future cellular communication systems with increasing base station density. Mobile broadband networks are expected to operate under frequency reuse factor one and are therefore interference-limited. We derive a novel framework for resource-fair sum rate maximization in OFDMA multicell deployments with single antenna links where power allocation among subchannels is predefined. The framework holds for multiband scenarios with non-contiguously aggregated carriers. Our proposed algorithm for joint subchannel scheduling is based on semidefinite reformulation and programming techniques. It has a significant contribution by taking into account active control of inter-cell interference on shared frequency resources. We achieve throughput bounds and near-optimal feasible sum rates under guarantee of interference temperature constraints by extensively exploiting all degrees of the three-dimensional binary assignment problem. We give simulation results that are compliant with wireless propagation assumptions under the Long Term Evolution (LTE) standard. Moreover, we show that the network performance can gain substantial improvements by our scheme when compared to the current generation of wireless networks where subchannel assignment has distributed implementations among locally operating cell sites