Energy efficiency benefits of RAN-as-a-service concept for a cloud-based 5G mobile network infrastructure

This paper focuses on energy efficiency aspects and related benefits of radio-access-network-as-a-service (RANaaS) implementation (using commodity hardware) as architectural evolution of LTE-advanced networks toward 5G infrastructure. RANaaS is a novel concept introduced recently, which enables the partial centralization of RAN functionalities depending on the actual needs as well as on network characteristics. In the view of future definition of 5G systems, this cloud-based design is an important solution in terms of efficient usage of network resources. The aim of this paper is to give a vision of the advantages of the RANaaS, to present its benefits in terms of energy efficiency and to propose a consistent system-level power model as a reference for assessing innovative functionalities toward 5G systems. The incremental benefits through the years are also discussed in perspective, by considering technological evolution of IT platforms and the increasing matching between their capabilities and the need for progressive virtualization of RAN functionalities. The description is complemented by an exemplary evaluation in terms of energy efficiency, analyzing the achievable gains associated with the RANaaS paradigm

View on 5G Architecture: Version 1.0

The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper

Distribution of Low Latency Machine Learning Algorithm

Mobile networks are evolving towards centralization and cloudification while bringing computing power to the edge, opening its scope to a new range of applications. Ultra-low latency is one of the requirements of such applications in the next generation of mobile networks (5G), where deep learning is expected to play a big role. Hence, to enable the usage of deep learning solutions on the edge cloud, ultra-low latency inference must be investigated. The study presented here relies on the usage of an in-house framework (CRUN) that enables the distribution of acceleration on data center environment. The objective of this thesis is to leverage the best solution for the inference of a machine learning algorithm for an anomaly detection application using neural networks in the edge cloud context. To evaluate the obtained results with CRUN a comparison work is also carried out. Five inference solutions were compared using CPU, GPU and FPGA. The results show a superior performance in terms of latency for all CRUN experiments, that basically comprehends three cases. The first one utilizing the RTL anomaly detection neural network as a baseline solution, the second using the same baseline code but unrolling the biggest layer for obtaining reduced latency and the third by distributing the neural network in two FPGAs. The requirements for this solution were to obtain latency between 20 μs to 40 μs for inference time and at least 20000 inferences per second. These goals were categorically fulfilled for all CRUN experiments, providing 30 μs latency in average, while the second best solution provided 272 μs

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

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

Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN). The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage. First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes. While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs). DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements. Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN. Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks

Role of Interference and Computational Complexity in Modern Wireless Networks: Analysis, Optimization, and Design

Owing to the popularity of smartphones, the recent widespread adoption of wireless broadband has resulted in a tremendous growth in the volume of mobile data traffic, and this growth is projected to continue unabated. In order to meet the needs of future systems, several novel technologies have been proposed, including cooperative communications, cloud radio access networks (RANs) and very densely deployed small-cell networks. For these novel networks, both interference and the limited availability of computational resources play a very important role. Therefore, the accurate modeling and analysis of interference and computation is essential to the understanding of these networks, and an enabler for more efficient design.;This dissertation focuses on four aspects of modern wireless networks: (1) Modeling and analysis of interference in single-hop wireless networks, (2) Characterizing the tradeoffs between the communication performance of wireless transmission and the computational load on the systems used to process such transmissions, (3) The optimization of wireless multiple-access networks when using cost functions that are based on the analytical findings in this dissertation, and (4) The analysis and optimization of multi-hop networks, which may optionally employ forms of cooperative communication.;The study of interference in single-hop wireless networks proceeds by assuming that the random locations of the interferers are drawn from a point process and possibly constrained to a finite area. Both the information-bearing and interfering signals propagate over channels that are subject to path loss, shadowing, and fading. A flexible model for fading, based on the Nakagami distribution, is used, though specific examples are provided for Rayleigh fading. The analysis is broken down into multiple steps, involving subsequent averaging of the performance metrics over the fading, the shadowing, and the location of the interferers with the aim to distinguish the effect of these mechanisms that operate over different time scales. The analysis is extended to accommodate diversity reception, which is important for the understanding of cooperative systems that combine transmissions that originate from different locations. Furthermore, the role of spatial correlation is considered, which provides insight into how the performance in one location is related to the performance in another location.;While it is now generally understood how to communicate close to the fundamental limits implied by information theory, operating close to the fundamental performance bounds is costly in terms of the computational complexity required to receive the signal. This dissertation provides a framework for understanding the tradeoffs between communication performance and the imposed complexity based on how close a system operates to the performance bounds, and it allows to accurately estimate the required data processing resources of a network under a given performance constraint. The framework is applied to Cloud-RAN, which is a new cellular architecture that moves the bulk of the signal processing away from the base stations (BSs) and towards a centralized computing cloud. The analysis developed in this part of the dissertation helps to illuminate the benefits of pooling computing assets when decoding multiple uplink signals in the cloud. Building upon these results, new approaches for wireless resource allocation are proposed, which unlike previous approaches, are aware of the computing limitations of the network.;By leveraging the accurate expressions that characterize performance in the presence of interference and fading, a methodology is described for optimizing wireless multiple-access networks. The focus is on frequency hopping (FH) systems, which are already widely used in military systems, and are becoming more common in commercial systems. The optimization determines the best combination of modulation parameters (such as the modulation index for continuous-phase frequency-shift keying), number of hopping channels, and code rate. In addition, it accounts for the adjacent-channel interference (ACI) and determines how much of the signal spectrum should lie within the operating band of each channel, and how much can be allowed to splatter into adjacent channels.;The last part of this dissertation contemplates networks that involve multi-hop communications. Building on the analytical framework developed in early parts of this dissertation, the performance of such networks is analyzed in the presence of interference and fading, and it is introduced a novel paradigm for a rapid performance assessment of routing protocols. Such networks may involve cooperative communications, and the particular cooperative protocol studied here allows the same packet to be transmitted simultaneously by multiple transmitters and diversity combined at the receiver. The dynamics of how the cooperative protocol evolves over time is described through an absorbing Markov chain, and the analysis is able to efficiently capture the interference that arises as packets are periodically injected into the network by a common source, the temporal correlation among these packets and their interdependence

Ondas milimétricas e MIMO massivo para otimização da capacidade e cobertura de redes heterogeneas de 5G

Today's Long Term Evolution Advanced (LTE-A) networks cannot support the exponential growth in mobile traffic forecast for the next decade. By 2020, according to Ericsson, 6 billion mobile subscribers worldwide are projected to generate 46 exabytes of mobile data traffic monthly from 24 billion connected devices, smartphones and short-range Internet of Things (IoT) devices being the key prosumers. In response, 5G networks are foreseen to markedly outperform legacy 4G systems. Triggered by the International Telecommunication Union (ITU) under the IMT-2020 network initiative, 5G will support three broad categories of use cases: enhanced mobile broadband (eMBB) for multi-Gbps data rate applications; ultra-reliable and low latency communications (URLLC) for critical scenarios; and massive machine type communications (mMTC) for massive connectivity. Among the several technology enablers being explored for 5G, millimeter-wave (mmWave) communication, massive MIMO antenna arrays and ultra-dense small cell networks (UDNs) feature as the dominant technologies. These technologies in synergy are anticipated to provide the 1000_ capacity increase for 5G networks (relative to 4G) through the combined impact of large additional bandwidth, spectral efficiency (SE) enhancement and high frequency reuse, respectively. However, although these technologies can pave the way towards gigabit wireless, there are still several challenges to solve in terms of how we can fully harness the available bandwidth efficiently through appropriate beamforming and channel modeling approaches. In this thesis, we investigate the system performance enhancements realizable with mmWave massive MIMO in 5G UDN and cellular infrastructure-to-everything (C-I2X) application scenarios involving pedestrian and vehicular users. As a critical component of the system-level simulation approach adopted in this thesis, we implemented 3D channel models for the accurate characterization of the wireless channels in these scenarios and for realistic performance evaluation. To address the hardware cost, complexity and power consumption of the massive MIMO architectures, we propose a novel generalized framework for hybrid beamforming (HBF) array structures. The generalized model reveals the opportunities that can be harnessed with the overlapped subarray structures for a balanced trade-o_ between SE and energy efficiently (EE) of 5G networks. The key results in this investigation show that mmWave massive MIMO can deliver multi-Gbps rates for 5G whilst maintaining energy-efficient operation of the network.As redes LTE-A atuais não são capazes de suportar o crescimento exponencial de tráfego que está previsto para a próxima década. De acordo com a previsão da Ericsson, espera-se que em 2020, a nível global, 6 mil milhões de subscritores venham a gerar mensalmente 46 exa bytes de tráfego de dados a partir de 24 mil milhões de dispositivos ligados à rede móvel, sendo os telefones inteligentes e dispositivos IoT de curto alcance os principais responsáveis por tal nível de tráfego. Em resposta a esta exigência, espera-se que as redes de 5a geração (5G) tenham um desempenho substancialmente superior às redes de 4a geração (4G) atuais. Desencadeado pelo UIT (União Internacional das Telecomunicações) no âmbito da iniciativa IMT-2020, o 5G irá suportar três grandes tipos de utilizações: banda larga móvel capaz de suportar aplicações com débitos na ordem de vários Gbps; comunicações de baixa latência e alta fiabilidade indispensáveis em cenários de emergência; comunicações massivas máquina-a-máquina para conectividade generalizada. Entre as várias tecnologias capacitadoras que estão a ser exploradas pelo 5G, as comunicações através de ondas milimétricas, os agregados MIMO massivo e as redes celulares ultradensas (RUD) apresentam-se como sendo as tecnologias fundamentais. Antecipa-se que o conjunto destas tecnologias venha a fornecer às redes 5G um aumento de capacidade de 1000x através da utilização de maiores larguras de banda, melhoria da eficiência espectral, e elevada reutilização de frequências respetivamente. Embora estas tecnologias possam abrir caminho para as redes sem fios com débitos na ordem dos gigabits, existem ainda vários desafios que têm que ser resolvidos para que seja possível aproveitar totalmente a largura de banda disponível de maneira eficiente utilizando abordagens de formatação de feixe e de modelação de canal adequadas. Nesta tese investigamos a melhoria de desempenho do sistema conseguida através da utilização de ondas milimétricas e agregados MIMO massivo em cenários de redes celulares ultradensas de 5a geração e em cenários 'infraestrutura celular-para-qualquer coisa' (do inglês: cellular infrastructure-to-everything) envolvendo utilizadores pedestres e veiculares. Como um componente fundamental das simulações de sistema utilizadas nesta tese é o canal de propagação, implementamos modelos de canal tridimensional (3D) para caracterizar de forma precisa o canal de propagação nestes cenários e assim conseguir uma avaliação de desempenho mais condizente com a realidade. Para resolver os problemas associados ao custo do equipamento, complexidade e consumo de energia das arquiteturas MIMO massivo, propomos um modelo inovador de agregados com formatação de feixe híbrida. Este modelo genérico revela as oportunidades que podem ser aproveitadas através da sobreposição de sub-agregados no sentido de obter um compromisso equilibrado entre eficiência espectral (ES) e eficiência energética (EE) nas redes 5G. Os principais resultados desta investigação mostram que a utilização conjunta de ondas milimétricas e de agregados MIMO massivo possibilita a obtenção, em simultâneo, de taxas de transmissão na ordem de vários Gbps e a operação de rede de forma energeticamente eficiente.Programa Doutoral em Telecomunicaçõe