Enabling virtual radio functions on software defined radio for future wireless networks

Today's wired networks have become highly flexible, thanks to the fact that an increasing number of functionalities are realized by software rather than dedicated hardware. This trend is still in its early stages for wireless networks, but it has the potential to improve the network's flexibility and resource utilization regarding both the abundant computational resources and the scarce radio spectrum resources. In this work we provide an overview of the enabling technologies for network reconfiguration, such as Network Function Virtualization, Software Defined Networking, and Software Defined Radio. We review frequently used terminology such as softwarization, virtualization, and orchestration, and how these concepts apply to wireless networks. We introduce the concept of Virtual Radio Function, and illustrate how softwarized/virtualized radio functions can be placed and initialized at runtime, allowing radio access technologies and spectrum allocation schemes to be formed dynamically. Finally we focus on embedded Software-Defined Radio as an end device, and illustrate how to realize the placement, initialization and configuration of virtual radio functions on such kind of devices

Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.Peer ReviewedPostprint (author's final draft

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Traffic control for energy harvesting virtual small cells via reinforcement learning

Due to the rapid growth of mobile data traffic, future mobile networks are expected to support at least 1000 times more capacity than 4G systems. This trend leads to an increasing energy demand from mobile networks which raises both economic and environmental concerns. Energy costs are becoming an important part of OPEX by Mobile Network Operators (MNOs). As a result, the shift towards energy-oriented design and operation of 5G and beyond systems has been emphasized by academia, industries as well as standard bodies. In particular, Radio Access Network (RAN) is the major energy consuming part of cellular networks. To increase the RAN efficiency, Cloud Radio Access Network (CRAN) has been proposed to enable centralized cloud processing of baseband functions while Base Stations (BSs) are reduced to simple Radio Remote Heads (RRHs). The connection between the RRHs and central cloud is provided by high capacity and very low latency fronthaul. Flexible functional splits between local BS sites and a central cloud are then proposed to relax the CRAN fronthaul requirements via partial processing of baseband functions at the local BS sites. Moreover, Network Function Virtualization (NFV) and Software Defined Networking (SDN) enable flexibility in placement and control of network functions. Relying on SDN/NFV with flexible functional splits, network functions of small BSs can be virtualized and placed at different sites of the network. These small BSs are known as virtual Small Cells (vSCs). More recently, Multi-access Edge Computing (MEC) has been introduced where BSs can leverage cloud computing capabilities and offer computational resources on demand basis. On the other hand, Energy Harvesting (EH) is a promising technology ensuring both cost effectiveness and carbon footprint reduction. However, EH comes with challenges mainly due to intermittent and unreliable energy sources. In EH Base Stations (EHBSs), it is important to intelligently manage the harvested energy as well as to ensure energy storage provision. Consequently, MEC enabled EHBSs can open a new frontier in energy-aware processing and sharing of processing units according to flexible functional split options. The goal of this PhD thesis is to propose energy-aware control algorithms in EH powered vSCs for efficient utilization of harvested energy and lowering the grid energy consumption of RAN, which is the most power consuming part of the network. We leverage on virtualization and MEC technologies for dynamic provision of computational resources according to functional split options employed by the vSCs. After describing the state-of-the-art, the first part of the thesis focuses on offline optimization for efficient harvested energy utilization via dynamic functional split control in vSCs powered by EH. For this purpose, dynamic programming is applied to determine the performance bound and comparison is drawn against static configurations. The second part of the thesis focuses on online control methods where reinforcement learning based controllers are designed and evaluated. In particular, more focus is given towards the design of multi-agent reinforcement learning to overcome the limitations of centralized approaches due to complexity and scalability. Both tabular and deep reinforcement learning algorithms are tailored in a distributed architecture with emphasis on enabling coordination among the agents. Policy comparison among the online controllers and against the offline bound as well as energy and cost saving benefits are also analyzed.Debido al rápido crecimiento del tráfico de datos móviles, se espera que las redes móviles futuras admitan al menos 1000 veces más capacidad que los sistemas 4G. Esta tendencia lleva a una creciente demanda de energía de las redes móviles, lo que plantea preocupaciones económicas y ambientales. Los costos de energía se están convirtiendo en una parte importante de OPEX por parte de los operadores de redes móviles (MNO). Como resultado, la academia, las industrias y los organismos estándar han enfatizado el cambio hacia el diseño orientado a la energía y la operación de sistemas 5G y más allá de los sistemas. En particular, la red de acceso por radio (RAN) es la principal parte de las redes celulares que consume energía. Para aumentar la eficiencia de la RAN, se ha propuesto Cloud Radio Access Network (CRAN) para permitir el procesamiento centralizado en la nube de las funciones de banda base, mientras que las estaciones base (BS) se reducen a simples cabezales remotos de radio (RRH). La conexión entre los RRHs y la nube central es proporcionada por una capacidad frontal de muy alta latencia y muy baja latencia. Luego se proponen divisiones funcionales flexibles entre los sitios de BS locales y una nube central para relajar los requisitos de red de enlace CRAN a través del procesamiento parcial de las funciones de banda base en los sitios de BS locales. Además, la virtualización de funciones de red (NFV) y las redes definidas por software (SDN) permiten flexibilidad en la colocación y el control de las funciones de red. Confiando en SDN / NFV con divisiones funcionales flexibles, las funciones de red de pequeñas BS pueden virtualizarse y ubicarse en diferentes sitios de la red. Estas pequeñas BS se conocen como pequeñas celdas virtuales (vSC). Más recientemente, se introdujo la computación perimetral de acceso múltiple (MEC) donde los BS pueden aprovechar las capacidades de computación en la nube y ofrecer recursos computacionales según la demanda. Por otro lado, Energy Harvesting (EH) es una tecnología prometedora que garantiza tanto la rentabilidad como la reducción de la huella de carbono. Sin embargo, EH presenta desafíos principalmente debido a fuentes de energía intermitentes y poco confiables. En las estaciones base EH (EHBS), es importante administrar de manera inteligente la energía cosechada, así como garantizar el suministro de almacenamiento de energía. En consecuencia, los EHBS habilitados para MEC pueden abrir una nueva frontera en el procesamiento con conciencia energética y el intercambio de unidades de procesamiento de acuerdo con las opciones de división funcional flexible. El objetivo de esta tesis doctoral es proponer algoritmos de control conscientes de la energía en vSC alimentados por EH para la utilización eficiente de la energía cosechada y reducir el consumo de energía de la red de RAN, que es la parte más consumidora de la red. Aprovechamos las tecnologías de virtualización y MEC para la provisión dinámica de recursos computacionales de acuerdo con las opciones de división funcional empleadas por los vSC. La primera parte de la tesis se centra en la optimización fuera de línea para la utilización eficiente de la energía cosechada a través del control dinámico de división funcional en vSC con tecnología EH. Para este propósito, la programación dinámica se aplica para determinar el rendimiento limitado y la comparación se realiza con configuraciones estáticas. La segunda parte de la tesis se centra en los métodos de control en línea donde se diseñan y evalúan los controladores basados en el aprendizaje por refuerzo. En particular, se presta más atención al diseño de aprendizaje de refuerzo de múltiples agentes para superar las limitaciones de los enfoques centralizados debido a la complejidad y la escalabilidad. También se analiza la comparación de políticas entre los controladores en línea y contra los límites fuera de línea,Postprint (published version