157 research outputs found

    An adaptive 5G multiservice and multitenant radio access network architecture

    Get PDF
    This article provides an overview on objectives and first results of the Horizon 2020 project 5G NOvel Radio Multiservice adaptive network Architecture (5GNORMA). With 5G NORMA, leading players in the mobile ecosystem aim to underpin Europe's leadership position in 5G. The key objective of 5G NORMA is to develop a conceptually novel, adaptive and future-proof 5G mobile network architecture. This architecture will allow for adapting the network to a wide range of service specific requirements, resulting in novel service-aware and context-aware end-to-end function chaining. The technical approach is based on an innovative concept of adaptive (de)composition and allocation of mobile network functions based on end-user requirements and infrastructure capabilities. At the same time, cost savings and faster time to market are to be expected by joint deployment of logically separated multiservice and multitenant networks on common hardware and other physical resources making use of traffic multiplexing gains. In this context architectural enablers such as network function virtualization and software-defined mobile networking will play a key role for introducing the needed flexible resource assignment to logical networks and specific virtual network functions.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA

    A NOvel radio multiservice adaptive network architecture for 5G networks

    Get PDF
    Proceeding of: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring)This paper proposes a conceptually novel, adaptive and future-proof 5G mobile network architecture. The proposed architecture enables unprecedented levels of network customisability, ensuring stringent performance, security, cost and energy requirements to be met; as well as providing an API-driven architectural openness, fuelling economic growth through over-the-top innovation. Not following the 'one system fits all services' paradigm of current architectures, the architecture allows for adapting the mechanisms executed for a given service to the specific service requirements, resulting in a novel service- and context-dependent adaptation of network functions paradigm. The technical approach is based on the innovative concept of adaptive (de)composition and allocation of mobile network functions, which flexibly decomposes the mobile network functions and places the resulting functions in the most appropriate location. By doing so, access and core functions no longer (necessarily) reside in different locations, which is exploited to jointly optimize their operation when possible. The adaptability of the architecture is further strengthened by the innovative software-defined mobile network control and mobile multi-tenancy concepts

    Beyond 5G Fronthaul based on FSO Using Spread Spectrum Codes and Graphene Modulators.

    Get PDF
    High data rate coverage, security, and energy efficiency will play a key role in the continued performance scaling of next-generation mobile systems. Dense, small mobile cells based on a novel network architecture are part of the answer. Motivated by the recent mounting interest in free-space optical (FSO) technologies, this paper addresses a novel mobile fronthaul network architecture based on FSO, spread spectrum codes, and graphene modulators for the creation of dense small cells. The network uses an energy-efficient graphene modulator to send data bits to be coded with spread codes for achieving higher security before their transmission to remote units via high-speed FSO transmitters. Analytical results show the new fronthaul mobile network can accommodate up to 32 remote antennas under error-free transmissions with forward error correction. Furthermore, the modulator is optimized to provide maximum efficiency in terms of energy consumption per bit. The optimization procedure is carried out by optimizing both the amount of graphene used on the ring resonator and the modulator’s design. The optimized graphene modulator is used in the new fronthaul network and requires as low as 4.6 fJ/bit while enabling high-speed performance up to 42.6 GHz and remarkably using one-quarter of graphene only

    A Review for the Current Advancements in 5G Technology

    Get PDF
    الجيل الخامس للاتصالات أو ما يشار اليه باختصار (5G) يمثل التطور الجديد في عالم الاتصالات والذي يعتمد عليه لكسر حاجز سرعة البيانات الحالي للوصول إلى سرعات عالية. تَعِد هذه التقنية الحديثة بسرعات خيالية ستمكن المستخدمين من إجراء مكالمات صورية ذات دقة عالية جدا وبوقت حقيقي دون تقطيعات. كما وستوفر هذه التقنية أيضا البنى التحتية لما يعرف بإنترنت الأشياء (IoT) والذي سيعتمد من قبل الحكومات الالكترونية ومراكز الرعاية الصحية الالكترونية وشبكات الواصل الاجتماعي وتبادل البيانات بالإمكانات المفتوحة والتحكم عن بعد بالمنشآت الحكومية المهمة والحساسة. الغرض من ها البحث هو تقديم مسح بياني عن التحديثات الجديدة في هذا الميدان (5G) والمقاييس المتوفرة والتي هي تحت البحث والمناقشة والإمكانات المستقبلية لهذه التقنية. بالإضافة لهذا يتطرق البحث لموضوع التحديات والصعوبات التي تواجه هذه التقنية الحديثة والخطط الموضوعة لمستقبل الاتصالات.The fifth generation technology or in short (5G technology) is the recent technology that is meant to break the data limits barrier. It promises very high data rates that will provide the user with enough bandwidth to conduct a real time HD telephone conversation. It will also provide the infrastructure for the IoT (Internet of Things) that will be dedicated for electronic governments, electronic healthcare centers, social media networks, full-scale data sharing, and remote controlling for sensitive governmental facilities. This paper is intended as survey for the current developments and technologies available for the coming 5G mobile technology. It discusses the ideas, the preparations, the developments, the standards under discussion and the potentials for this technology. In addition, this work takes into consideration the challenges and the difficulties facing this new coming technology and the plans laid ahead for the futuristic mobile networks

    An Approach to Data Analysis in 5G Networks

    Get PDF
    5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEUnión Europea. Horizonte 2020pu

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

    Get PDF
    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

    Mitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning

    Get PDF
    Introducción: El constante crecimiento de usuarios conectados a internet por medio de dispositivos móviles ha conllevado a incrementar la investigación en el paradigma de las redes híbridas conocido como Radio-sobre-Fibra. Estas redes aprovechan las ventajas del ancho de banda de la fibra óptica y la movilidad de las transmisiones inalámbricas, evitando el cuello de botella que se da por la conversión óptico a eléctrico. No obstante, la dispersión cromática propia de la fibra óptica genera distorsiones en la señal de radiofrecuencia modulada ópticamente, lo cual limita su alcance.  Objetivo: Mejorar el desempeño de un sistema de radio sobre fibra en términos de la tasa de error de bit, usando demodulación no simétrica por medio del algoritmo de aprendizaje automático Máquina de Soporte Vectorial.  Metodología: Se simula un sistema de Radio-sobre-Fibra en el software especializado VPIDesignSuite. Se transmiten señales de radiofrecuencia moduladas en formatos 16 y 64-QAM con diferentes anchos de línea de láser sobre fibra óptica. Se aplica el algoritmo Máquina de Soporte Vectorial para la demodulación de la señal.  Resultados: La implementación del algoritmo de aprendizaje automático para la demodulación de la señal mejora significativamente el desempeño de la red permitiendo alcanzar los 30 km de transmisión por fibra óptica. Esto implica una reducción de la tasa de error de bit hasta en dos órdenes de magnitud en comparación con la demodulación tradicional.  Conclusiones: Se demuestra que con el uso de umbrales asimétricos usando algoritmo de Máquina de Soporte Vectorial se logran mitigar distorsiones en términos de la tasa de error de bit. Así, esta técnica se hace atractiva para futuras redes de acceso de alta capacidad.  Introduction: The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission.  Objective: To improve the performance of a Radio-over-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine.  Methodology: A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation.  Results: The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two Introduction: The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission.  Objective: To improve the performance of a Radio-over-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine.   Methodology: A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation.  Results: The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two orders of magnitude in comparison with conventional demodulation.  Conclusions: Mitigation of distortions in terms of bit-error-rate is demonstrated in a Radio-over-Fiber system using nonsymmetrical demodulation by using the Support Vector Machine algorithm. Thus, the proposed technique can be suitable for future high-capacity access networks. &nbsp

    Quality of Service Differentiation in Heterogeneous CDMA Networks : A Mathematical Modelling Approach

    Get PDF
    Next-generation cellular networks are expected to enable the coexistence of macro and small cells, and to support differentiated quality-of-service (QoS) of mobile applications. Under such conditions in the cell, due to a wide range of supported services and high dependencies on efficient vertical and horizontal handovers, appropriate management of handover traffic is very crucial. Furthermore, new emerging technologies, such as cloud radio access networks (C-RAN) and self-organizing networks (SON), provide good implementation and deployment opportunities for novel functions and services. We design a multi-threshold teletraffic model for heterogeneous code division multiple access (CDMA) networks that enable QoS differentiation of handover traffic when elastic and adaptive services are present. Facilitated by this model, it is possible to calculate important performance metrics for handover and new calls, such as call blocking probabilities, throughput, and radio resource utilization. This can be achieved by modelling the cellular CDMA system as a continuous-time Markov chain. After that, the determination of state probabilities in the cellular system can be performed via a recursive and efficient formula. We present the applicability framework for our proposed approach, that takes into account advances in C-RAN and SON technologies. We also evaluate the accuracy of our model using simulations and find it very satisfactory. Furthermore, experiments on commodity hardware show algorithm running times in the order of few hundreds of milliseconds, which makes it highly applicable for accurate cellular network dimensioning and radio resource management

    Digital Double Sideband Frequency Translation for Transmission of RF MIMO Signals over Fiber

    Get PDF
    In this letter, we demonstrate digitization, digital frequency translation and pre-distortion compensation using ADC and DAC for transmitting analog radio frequency (RF) multipleinput-multiple-output (MIMO) signals over a single fiber without wavelength division multiplexing (WDM). This technique allows more flexible signal manipulation than the previous purely analog implementations. With the digital pre-distortion compensation for a Mach Zehnder modulator (MZM), a 6-dB reduction in the intermodulation distortion is achieved for a 10-km RF over fiber link
    corecore