650 research outputs found

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark

    Toward Dynamic Social-Aware Networking Beyond Fifth Generation

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    The rise of the intelligent information world presents significant challenges for the telecommunication industry in meeting the service-level requirements of future applications and incorporating societal and behavioral awareness into the Internet of Things (IoT) objects. Social Digital Twins (SDTs), or Digital Twins augmented with social capabilities, have the potential to revolutionize digital transformation and meet the connectivity, computing, and storage needs of IoT devices in dynamic Fifth-Generation (5G) and Beyond Fifth-Generation (B5G) networks. This research focuses on enabling dynamic social-aware B5G networking. The main contributions of this work include(i) the design of a reference architecture for the orchestration of SDTs at the network edge to accelerate the service discovery procedure across the Social Internet of Things (SIoT); (ii) a methodology to evaluate the highly dynamic system performance considering jointly communication and computing resources; (iii) a set of practical conclusions and outcomes helpful in designing future digital twin-enabled B5G networks. Specifically, we propose an orchestration for SDTs and an SIoT-Edge framework aligned with the Multi-access Edge Computing (MEC) architecture ratified by the European Telecommunications Standards Institute (ETSI). We formulate the optimal placement of SDTs as a Quadratic Assignment Problem (QAP) and propose a graph-based approximation scheme considering the different types of IoT devices, their social features, mobility patterns, and the limited computing resources of edge servers. We also study the appropriate intervals for re-optimizing the SDT deployment at the network edge. The results demonstrate that accounting for social features in SDT placement offers considerable improvements in the SIoT browsing procedure. Moreover, recent advancements in wireless communications, edge computing, and intelligent device technologies are expected to promote the growth of SIoT with pervasive sensing and computing capabilities, ensuring seamless connections among SIoT objects. We then offer a performance evaluation methodology for eXtended Reality (XR) services in edge-assisted wireless networks and propose fluid approximations to characterize the XR content evolution. The approach captures the time and space dynamics of the content distribution process during its transient phase, including time-varying loads, which are affected by arrival, transition, and departure processes. We examine the effects of XR user mobility on both communication and computing patterns. The results demonstrate that communication and computing planes are the key barriers to meeting the requirement for real-time transmissions. Furthermore, due to the trend toward immersive, interactive, and contextualized experiences, new use cases affect user mobility patterns and, therefore, system performance.Cotutelle -yhteisväitöskirj

    Survey on Wi-Fi and Cellular Communication Technology for Advanced Metering Infrastructure (AMI) in a Developing Economy

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    Traditional energy meters have suffered from a lack of automated analysis and inaccuracy in reading energy consumption, which has brought about smart metering systems. Developing economies such as in Africa. still experience a setback in electricity monitoring and load distribution because of existing traditional meter systems in use. Communication technologies play an important role to improve the monitoring of energy consumption and ensure a road map toward a smart grid. This paper reviews communication technologies used for Advanced Metering Infrastructure (AMI) emphasizing Wi-Fi and Cellular technologies. Metrics used to evaluate their performance include cost, energy efficiency, coverage, deployment, latency, payload, and scalability. The review presents a benchmark for research on AMI communication technologies in developing economies. When adopted, the expected AMI benefits are reduced energy theft, cost efficiency, real-time analysis, security, and safety of energy supply in developing economies

    Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 173-194)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022This works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Introduction -- Matrix exponential and deep learning neural network modeling of cellular handovers -- Survivability modeling in cellular networks -- Multi connectivity based handover enhancement and adaptive fractional packet duplication in 5G cellular networks -- Deepslice and Secure5G: a deep learning framework towards an efficient, reliable and secure network slicing in 5G networks -- Conclusion and future scop

    Point-to-Multipoint Services on Fifth-Generation Mobile Networks

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    [ES] Esta disertación cubre el estado del arte en LTE eMBMS Release 14, también conocido como Enhanced Television Services (ENTV). ENTV trajo un conjunto de mejoras, tanto a nivel radio como a nivel de núcleo, que transformó a eMBMS en un estándar de televisión terrestre completo. La última versión de esta tecnología se denomina LTE-based 5G Broadcast; pero no usa New Radio ni el núcleo 5G. Para proveer una solución nativa 5G de servicios punto-a-multipunto, hubo investigación en entornos acad\'emicos y colaboraciones público-privada. La iniciativa más notable en este aspecto fue el proyecto del Horizon 2020 5G-Xcast, que transcurrió de 2017 a 2019. 5G-Xcast produjo varias soluciones a nivel de arquitectura, desde la perspectiva de provisión de contenidos, nuevas funciones de red interoperables con el núcleo 5G, hasta modificaciones a la interfaz aire basada en New Radio. Los hallazgos del proyecto están descritos en esta tesis. La tesis incluye dos ejemplos de eMBMS aplicados a verticales diferentes, una para el uso de eMBMS en entornos industriales, y otra presentando eMBMS como un sistema SAP. Incluir servicios punto-a-multipunto como un modo adicional celular trae algunos desafíos, como ya mostró la estandarización de eMBMS: las redes de radiodifusión terrestre y las redes celulares son muy distintas entre ellas. Encontrar una forma de onda viable para ambas infraestructuras es complejo. Esta tesis ofrece un punto de vista distinto al problema: un escenario de colaboración entre cadenas televisivas y operadores móviles, donde la infraestructura de radiodifusión y móvil son compartidas. Este concepto se ha definido como Convergence of Terrestrial and Mobile Networks. Las tecnologías elegidas para converger son ATSC 3.0 y 5G, usando el Advanced Traffic Steering, Switching and Splitting (ATSSS). ATSSS está compuesto de una serie de procedimientos, interfaces, funciones de red, para permitir el uso compartido de un acceso 3GPP con uno non-3GPP, como Wi-Fi. Sin embargo, el uso de ATSSS para juntar radiodifusión y celular no es trivial, ya que ATSSS no fue dise\~{n}ado para enlaces radio unidireccionales como ATSC 3.0. Estas limitaciones son descritas en detalle, y una propuesta para solventarlas tambi\'en está incluida. La solución se basa en Quick UDP Internet Connections (QUIC), y se usa como ejemplo para la provisión de Convergent Services (File Repair y Video Offloading). La tesis concluye con una descripción de Release 17 5MBS, con los nuevos conceptos introducidos. 5MBS es capaz de cambiar entre unicast, multicast y broadcast; dependiendo del servicio, la ubicación geográfica de los usuarios, y las capacidades de la infraestructura móvil involucradas. Para evaluar 5MBS, se ha realizado un estudio de prestaciones, basado en comunicaciones multicast dentro del núcleo de red 5G. Este prototipo 5MBS forma parte del laboratorio VLC Campus 5G, y utiliza el software comercial Open5GCore como base del desarrollo. El modelo de sistema para la experimentación esta formado por un servidor de vídeo, que se conecta al Open5GCore y a las funciones de red mejoradas con funcionalidades 5MBS. Estas funciones de red envían el contenido mediante punto-a-multipunto a un entorno radio y terminales simulados. Los resultados obtenidos resaltan el objetivo principal de la tesis: las comunicaciones punto-a-multipunto son una solución escalable para el envío de contenido multimedia en directo.[CA] Aquesta dissertació cobreix capdavanter en LTE eMBMS Release 14, també conegut com Enhanced Television Services (ENTV). ENTV va portar un conjunt de millores, tant a nivell de ràdio com a nivell de nucli, que va transformar el eMBMS en un estàndard de televisió terrestre complet. La última versió d'aquesta tecnologia es denomina LTE-based 5G Broadcast; però no fa servir New Ràdio ni el nucli 5G. Per a proveir una solució nativa 5G de serveis punt-a-multipunt, va haver-hi investigació en entorns acadèmics i col·laboracions pública i privada. La iniciativa més notable en aquest aspecte va ser el projecte del Horizon 2020 5G-Xcast, que va transcórrer del 2017 a 2019. 5G-Xcast va produir diverses solucions a nivell d'arquitectura, des de la perspectiva de provisió de continguts, noves funcions de xarxa interoperables amb el nucli 5G, fins a modificacions a la interfície aire basada en New Radio. Les troballes del projecte estan descrits en aquesta tesi. La tesi inclou dos exemples de eMBMS aplicats a verticals diferents, una per a l'ús de eMBMS en entorns industrials, i una altra presentant eMBMS com un sistema SAP. Incloure serveis punt-a-multipunt com una manera addicional cel·lular duu alguns desafiaments, com ja va mostrar l'estandardització de eMBMS: les xarxes de radiodifusió terrestre i les xarxes cel·lulars són molt diferents entre elles. Trobar una forma d'ona viable per a totes dues infraestructures és complex. Aquesta tesi ofereix un punt de vista diferent al problema: un escenari de col·laboració entre cadenes televisives i operadors mòbils, on la infraestructura de radiodifusió i mòbil són compartides. Aquest concepte s'ha definit com Convergence of Terrestrial and Mobile Networks. Les tecnologies triades per a convergir són ATSC 3.0 i 5G, usant el Advanced Traffic Steering, Switching and Splitting (ATSSS). ATSSS està compost d'una sèrie de procediments, interfícies, funcions de xarxa, per a permetre l'ús compartit d'un accés 3GPP amb un non-3GPP, com a Wi-Fi. No obstant això, l'ús de ATSSS per a adjuntar radiodifusió i cel·lular no és trivial, ja que ATSSS no va ser dissenyada per a per a enllaços ràdio unidireccionals com ATSC 3.0. Aquestes limitacions són descrites detalladament, i una proposta per a solucionar-les també està inclosa. La solució es basa en Quick UDP Internet Connections (QUIC), i s'usa com a exemple per a la provisió de Convergent Services (File Repair i Vídeo Offloading). La tesi conclou amb una descripció de Release 17 5MBS, amb els nous conceptes introduïts. 5MBS és capaç de canviar entre unicast, multicast i broadcast; depenent del servei, la ubicació geogràfica dels usuaris, i les capacitats de la infraestructura mòbil involucrades. Per a avaluar 5MBS, s'ha realitzat un estudi de prestacions, basat en comunicacions multicast dins del nucli de xarxa 5G. Aquest prototip 5MBS forma part del laboratori VLC Campus 5G, i utilitza el programari comercial Open5GCore com a base del desenvolupament. El model de sistema per a l'experimentació està format per un servidor de vídeo, que es connecta al Open5GCore i a les funcions de xarxa millorades amb funcionalitats 5MBS. Aquestes funcions de xarxa envien el contingut mitjançant punt-a-multipunt a un entorn ràdio i terminals simulats. Els resultats obtinguts ressalten l'objectiu principal de la tesi: les comunicacions punt-a-multipunt són una solució escalable per a l'enviament de contingut multimèdia en directe.[EN] This dissertation covers the state-of-the-art in LTE eMBMS Release 14, also known as Enhanced Television Services (ENTV). ENTV provided a suite of radio and core enhancements that made eMBMS into a viable terrestrial broadcast standard. The latest iteration of this technology is known as LTE-based 5G Broadcast; even though it is not New Radio or 5G Core based. To bridge this gap, research efforts by academia, public and private enterprises evaluated how to provide a 5G-based solution for point-to-multipoint services. The most notable effort in this regard is the Horizon 2020 project 5G-Xcast, which ran from 2017 to 2019. 5G-Xcast provided several architectural solutions, from the content delivery perspective down to air interface specifics; providing new waveforms based on New Radio and Network Functions interoperable with a Release 15 5G Core. The findings are summarized in this thesis. Two examples of eMBMS applied to different verticals are included in the thesis, one for the use of eMBMS in industrial environments, and the other using eMBMS as a PWS technology. Providing point-to-multipoint services as another cellular service poses some problems, as the standardization process of eMBMS showed: the broadcast infrastructure is different than the cellular one. Having a waveform that is suited for both scenarios is a difficult endeavour. The thesis provides a new perspective into this problem: Having existing Terrestrial Broadcast standards and infrastructure be the point-to-multipoint solution of 5G, where mobile operators and broadcasters collaborate together. This is defined in the dissertation as Convergence of Terrestrial and Mobile Networks. The technologies chosen to be converged together were ATSC 3.0 and 5G; using the existing Release 16 framework known as Advanced Traffic Steering, Switching and Splitting (ATSSS). ATSSS is a series of procedures, interfaces, new Network Functions, to allow the joint use of a 3GPP Access Network alongside a non-3GPP one, like Wi-Fi. However, the use of ATSSS for cellular plus broadcast brings challenges, as the ATSSS technology was not designed to be used with a unidirectional access network like ATSC 3.0. These limitations are described in detail, and an architectural proposal that overcomes the limitations is proposed. This solution is based on Quick UDP Internet Connections (QUIC), and how to provide Convergent Services (i.e File Repair and Video Offloading) is shown. The thesis concludes with a description of Release 17 5MBS, including the new concepts introduced. 5MBS features the capacity of switching between unicast, multicast and broadcast; depending on the service addressed, the geographical location of the users, and the capability of the RAN infrastructure targeted. In order to evaluate 5MBS, a performance study of the use of multicast inside the 5G Core has been carried out. The 5MBS prototype was developed as part of the VLC Campus 5G laboratory, using the commercial software Open5GCore which provides the libraries and Network Functions to deploy your own 5G Private Network in testing environments. The system model of the experiment is formed by a video server, connected to the Open5GCore and the 5MBS enhanced functions; which will deliver the content to an emulated RAN environment hosting virtual gNBs and devices. The results obtained reinforce the objective of the thesis, positioning point-to-multipoint as a scalable way to deliver live content.Research projects: 5G-Xcast: Broadcast and Multicast Communication Enablers for the Fifth-Generation of Wireless Systems (H2020 No 761498); 5G-TOURS: SmarT mObility, media and e-health for toURists and citizenS (H2020 No 856950); FUDGE-5G: FUlly DisinteGrated private nEtworks for 5G verticals (H2020 No 957242).Barjau Estevan, CS. (2022). Point-to-Multipoint Services on Fifth-Generation Mobile Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19140

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

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    Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

    Utilization of cloud RAN architecture with eCPRI fronthaul in 5G network

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    With increased reliability, massive network capacity, and extremely reduced latency, 5G expands the mobile ecosystem into new realms. 5G impacts every industry and innovation, making transportation and conveyance safer, remote healthcare, accuracy agriculture, digitized logistics, and much more. In this age, 5G calls for new levels of flexibility and broadness in architecting, scaling, and deploying telecommunication networks, which need a further step ahead in technology and enter Cloud Technology. Cloud technology provides fascinating possibilities to complement the existing tried and tested technologies in the Radio Access Network (RAN) domain. Cloud RAN (CRAN) refers to relying on RAN functions over an inclusive platform instead of a purpose-built hardware platform. It represents a progression in wireless communication technology, leveraging the Common public radio interface (CPRI) standard, Dense Wavelength Division Multiplexing (DWDM) innovation, and millimeter wave (mmWave) propagation for extended-range signals. A CRAN network comprises of three fundamental elements. The initial element is the Distant Wireless Unit (DRU) or Remote Radio Component (RRH), utilized within a network to link wireless devices to entry points; these units are equipped with transceivers for transmitting and receiving signals. Next, a Baseband Unit (BBU) centre or hub serves as a centralized site functioning as a data processing hub. Separate BBU modules can be assembled independently or interconnected to distribute resources, adapting to the network's changing dynamics and needs. Communication among these modules boasts remarkably high bandwidth and exceptionally low latency. The BBU can be further segmented into DU (Distributed Unit) and CU (Centralized Unit). The third crucial component is a fronthaul or conveyance network – the connecting layer between a baseband unit (BBU) and a set of RRUs, utilizing optical fibres, cellular links, or mmWave communication. The goal of this thesis is to find a way to utilize the 5G RAN Architecture as efficiently as possible and for this purpose, Enhanced Common Public Radio Interface (eCPRI) or enhanced CPRI fronthaul is adopted instead of CPRI as it is a manner of splitting up the functions performed by baseband unit and putting some of that in the RRU so it can reduce the burden on the fibre. Enhanced CPRI makes it possible to send some data packets to a virtual Distributed Unit (vDU) and others to a virtual Centralized Unit (vCU) which results in reduced data traffic on fibre. The first part of this research paper focuses on considering and learning about the 5G Cloud RAN architecture's main components, some cloud RAN history, and important components included in the 5G Cloud RAN. In the second part, research goes in depth about the fronthaul gateway technology that is eCPRI structure, its functional split, its difference from CPRI in structure and functionality, and how it is enhanced and developed. Considering CRAN specifications, it will also include some eCPRI protocol delay management and timing studies. Finally, Test cases are developed that can authenticate the low latency and high throughput of data with eCPRI fronthaul in 5G Cloud RAN as compared to CPRI fronthaul. The inspiration behind this is to recreate the model with substantial changes that work with an ideal behaviour of a subsystem, with this a tool or an environment can be obtained that maximizes the efficiency of 5G CRAN. It will also permit network architects and designers to experiment with new features, which can reduce costs, save time, improve latency. It can also provide a tool to verification engineers that will help them to generate optimal replies of the system necessary for evaluating the practical realization of that system
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