Spectrum sharing and aggregation for future wireless networks, part II

The papers in this special issue represent the second one in the sequel of three special issues on spectrum sharing and aggregation for future wirelessn networks

Dynamic spectrum allocation following machine learning-based traffic predictions in 5G

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The popularity of mobile broadband connectivity continues to grow and thus, the future wireless networks are expected to serve a very large number of users demanding a huge capacity. Employing larger spectral bandwidth and installing more access points to enhance the capacity is not enough to tackle the stated challenge due to related costs and the interference issues involved. In this way, frequency resources are becoming one of the most valuable assets, which require proper utilization and fair distribution. Traditional frequency resource management strategies are often based on static approaches, and are agnostic to the instantaneous demand of the network. These static approaches tend to cause congestion in a few cells, whereas at the same time, might waste those precious resources on others. Therefore, such static approaches are not efficient enough to deal with the capacity challenge of the future network. Thus, in this paper we present a dynamic access-aware bandwidth allocation approach, which follows the dynamic traffic requirements of each cell and allocates the required bandwidth accordingly from a common spectrum pool, which gathers the entire system bandwidth. We perform the evaluation of our proposal by means of real network traffic traces. Evaluation results presented in this paper depict the performance gain of the proposed dynamic access-aware approach compared to two different traditional approaches in terms of utilization and served traffic. Moreover, to acquire knowledge about access network requirement, we present a machine learning-based approach, which predicts the state of the network, and is utilized to manage the available spectrum accordingly. Our comparative results show that, in terms of spectrum allocation accuracy and utilization efficiency, a well designed machine learning-based bandwidth allocation mechanism not only outperforms common static approaches, but even achieves the performance (with a relative error close to 0.04) of an ideal dynamic system with perfect knowledge of future traffic requirements.This work was supported in part by the EU Horizon 2020 Research and Innovation Program (5GAuRA) under Grant 675806, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement from the Generalitat de Catalunya under Grant 2017 SGR 376.Peer ReviewedPostprint (published version

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page

Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

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Radio resource management techniques for QoS provision in 5G networks

Premi extraordinari doctorat UPC curs 2017-2018. Àmbit d’Enginyeria de les TICAs numerous mobile applications and over-the-top (OTT) services emerge and mobile Internet connectivity becomes ubiquitous, the provision of high quality of service (QoS) is more challenging for mobile network operators (MNOs). Research efforts focus on the development of innovative resource management techniques and have introduced the long term evolution advanced (LTE-A) communication standard. Novel business models make the growth of network capacity sustainable by enabling MNOs to combine their resources. The fifth generation (5G) mobile networks will involve technologies and business stakeholders with different capabilities and demands that may affect the QoS provision, requiring efficient radio resource sharing. The need for higher network capacity has introduced novel technologies that improve resource allocation efficiency. Direct connectivity among user equipment terminals (UEs) circumventing the LTE-A infrastructure alleviates the network overload. Part of mobile traffic is offloaded to outband device-to-device (D2D) connections (in unlicensed spectrum) enabling data exchange between UEs directly or via UEs-relays. Still, MNOs need additional spectrum resources and infrastructure. The inter-operator network sharing concept has emerged motivating the adoption of virtualization that enables network slicing, i.e., dynamic separation of resources in virtual slices (VSs). VSs are managed in isolation by different tenants using software defined networking and encompass core and radio access network resources allocated periodically to UEs. When UEs access OTT applications, flows with different QoS demands and priorities determined by OTT service providers (OSPs) are generated. OSPs’ policies should be considered in VS allocation. The coexisting technologies, business models and stakeholders require sophisticated radio resource management (RRM) techniques. To that end, RRM is performed in a complex ecosystem. When D2D communication involves data concurrently downloaded by the mobile network, QoS may be affected by LTE-A network parameters (resource scheduling policy, downlink channel conditions). It is also affected by the relay selection, as UEs may not be willing to help unknown UE pairs and UEs’ social ties in mobile applications may influence willingness for D2D cooperation. Thus, effective medium access control (MAC) mechanisms should coordinate D2D transmissions employing advanced techniques, e.g., network coding (NC). When UEs access OTT applications, OSPs’ policies are not considered by MNOs in RRM and OSPs cannot apply flow prioritization. Network neutrality issues also arise when OSPs claim resources from MNOs aiming to minimize grade of service (GoS). OSPs’ intervention may delay flows’ accommodation due to the time required for OSP-MNO interaction and the time the flows spent waiting for resources. This thesis proposes novel solutions to the RRM issues of outband D2D communication and VS allocation for OSPs in 5G networks. We present a cooperative D2D MAC protocol that leverages the opportunities for NC in D2D communication under the influence of LTE-A network parameters and its throughput performance analysis. The protocol improves D2D throughput and energy efficiency, especially for UEs with better downlink channel conditions. We next introduce social awareness in D2D MAC design and present a social-aware cooperative D2D MAC protocol that employs UEs’ social ties to promote the use of friendly relays reducing the total energy consumption. Motivated by the lack of approaches for OSP-oriented RRM, we present a novel flow prioritization algorithm based on matching theory that applies OSPs’ policies respecting the network neutrality and the analysis of its GoS and delay performance. The algorithm maintains low overhead and delay without affecting fairness among OSPs. Our techniques highlight the QoS improvement induced by the joint consideration of different technologies and business stakeholders in RRM design.A medida que varias aplicaciones móviles y servicios over-the-top (OTT) surgen y el Internet móvil se vuelve ubicua, la prestación de alta calidad de servicio (QoS) es desafiante para los operadores de red móvil (MNOs). Los estudios de investigación se enfocan en técnicas innovadoras para la gestión de recursos de red y han resultado en la especificación del estándar de comunicación long term evolution advanced (LTE-A). Modelos comerciales nuevos hacen que el crecimiento de la capacidad de red sea sostenible al permitir que MNOs combinen sus recursos. La quinta generación (5G) de redes móviles implicará tecnologías y partes comerciales interesadas con varias habilidades y demandas que pueden afectar la provisión de QoS y demandan la gestión eficaz de recursos de radio. La necesidad de capacidad de red más alta ha introducido tecnologías que hacen más eficiente la asignación de recursos. La conectividad directa entre terminales de equipos de usuarios (UEs) eludiendo la infraestructura LTE-A alivia la sobrecarga de red. Parte del tráfico es dirigido a conexiones de dispositivo a dispositivo (D2D) outband permitiendo la comunicación de UEs directamente o con relés. Los MNOs necesitan nuevos recursos de espectro e infraestructura. El intercambio de recursos entre MNOs ha surgido motivando la adopción de virtualización que realiza la segmentación de red i.e., la separación dinámica de recursos en trozos virtuales (VSs). Los VSs son administrados de forma aislada por inquilinos diferentes con software defined networking y abarcan recursos de red core y radio access asignadas periódicamente a UEs. Cuando UEs usan aplicaciones OTT, flujos de aplicación con demandas y prioridades definidas por proveedores de servicios OTT (OSPs) se generan. Las políticas de OSPs deben ser integradas en la asignación de VSs. La coexistencia de varias tecnologías y partes comerciales demanda técnicas sofisticadas de gestión de recursos radio (RRM). Con ese fin, la RRM se realiza en un ecosistema complejo. Si la comunicación D2D involucra datos descargados simultáneamente por la red móvil, los parámetros de red LTE-A (política de scheduling de recursos, condiciones de canal downlink) afectan el QoS. La selección de relés afecta el rendimiento porque los UEs no desean siempre ayudar a UEs desconocidos. Las relaciones sociales de los UEs en aplicaciones móviles pueden determinar la voluntad para la comunicación cooperativa D2D. Por lo tanto, mecanismos de control de acceso al medio (MAC) deben coordinar las transmisiones D2D con técnicas avanzadas ej., codificación de red. Si los UEs usan servicios OTT, las políticas de OSPs no son consideradas en RRM y los OSPs no emplean flujos prioritarios. Problemas de neutralidad de red surgen cuando los OSPs reclaman recursos de MNOs para minimizar el grado de servicio (GoS). La intervención de OSPs puede causar retraso en el servicio de flujos debido a la interacción OSP-MNO y el tiempo requerido para que los flujos reciban recursos. Esta tesis presenta soluciones nuevas para los problemas RRM de comunicación D2D outband y asignación de VSs a OSPs en redes 5G. Proponemos un protocolo D2D MAC cooperativo que explota las oportunidades de NC bajo la influencia de parámetros de red LTE-A y su análisis de rendimiento. El protocolo mejora el rendimiento y la eficiencia energética especialmente para UEs con mejores condiciones de canal downlink. Introducimos la conciencia social en el D2D MAC y proponemos un protocolo que utiliza relaciones sociales de UEs para elegir relés-amigos y reduce el consumo de energía. Dada la falta de técnicas que aborden el problema RRM de OSPs presentamos un algoritmo que aplique políticas de OSPs y respete la neutralidad usando la teoría de matching, y su análisis de GoS y retraso. El algoritmo induce bajo coste y retraso sin afectar la imparcialidad entre OSPs. Estas técnicas demuestran la mejora de QoS gracias a la consideración de tecnologas y partes comerciales diferentes en RRM.Award-winningPostprint (published version

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research