Cloud RAN for Mobile Networks - a Technology Overview

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user’s needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on base- band resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This article surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RA

Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

An Innovative RAN Architecture for Emerging Heterogeneous Networks: The Road to the 5G Era

The global demand for mobile-broadband data services has experienced phenomenal growth over the last few years, driven by the rapid proliferation of smart devices such as smartphones and tablets. This growth is expected to continue unabated as mobile data traffic is predicted to grow anywhere from 20 to 50 times over the next 5 years. Exacerbating the problem is that such unprecedented surge in smartphones usage, which is characterized by frequent short on/off connections and mobility, generates heavy signaling traffic load in the network signaling storms . This consumes a disproportion amount of network resources, compromising network throughput and efficiency, and in extreme cases can cause the Third-Generation (3G) or 4G (long-term evolution (LTE) and LTE-Advanced (LTE-A)) cellular networks to crash. As the conventional approaches of improving the spectral efficiency and/or allocation additional spectrum are fast approaching their theoretical limits, there is a growing consensus that current 3G and 4G (LTE/LTE-A) cellular radio access technologies (RATs) won\u27t be able to meet the anticipated growth in mobile traffic demand. To address these challenges, the wireless industry and standardization bodies have initiated a roadmap for transition from 4G to 5G cellular technology with a key objective to increase capacity by 1000Ã? by 2020 . Even though the technology hasn\u27t been invented yet, the hype around 5G networks has begun to bubble. The emerging consensus is that 5G is not a single technology, but rather a synergistic collection of interworking technical innovations and solutions that collectively address the challenge of traffic growth. The core emerging ingredients that are widely considered the key enabling technologies to realize the envisioned 5G era, listed in the order of importance, are: 1) Heterogeneous networks (HetNets); 2) flexible backhauling; 3) efficient traffic offload techniques; and 4) Self Organizing Networks (SONs). The anticipated solutions delivered by efficient interworking/ integration of these enabling technologies are not simply about throwing more resources and /or spectrum at the challenge. The envisioned solution, however, requires radically different cellular RAN and mobile core architectures that efficiently and cost-effectively deploy and manage radio resources as well as offload mobile traffic from the overloaded core network. The main objective of this thesis is to address the key techno-economics challenges facing the transition from current Fourth-Generation (4G) cellular technology to the 5G era in the context of proposing a novel high-risk revolutionary direction to the design and implementation of the envisioned 5G cellular networks. The ultimate goal is to explore the potential and viability of cost-effectively implementing the 1000x capacity challenge while continuing to provide adequate mobile broadband experience to users. Specifically, this work proposes and devises a novel PON-based HetNet mobile backhaul RAN architecture that: 1) holistically addresses the key techno-economics hurdles facing the implementation of the envisioned 5G cellular technology, specifically, the backhauling and signaling challenges; and 2) enables, for the first time to the best of our knowledge, the support of efficient ground-breaking mobile data and signaling offload techniques, which significantly enhance the performance of both the HetNet-based RAN and LTE-A\u27s core network (Evolved Packet Core (EPC) per 3GPP standard), ensure that core network equipment is used more productively, and moderate the evolving 5G\u27s signaling growth and optimize its impact. To address the backhauling challenge, we propose a cost-effective fiber-based small cell backhaul infrastructure, which leverages existing fibered and powered facilities associated with a PON-based fiber-to-the-Node/Home (FTTN/FTTH)) residential access network. Due to the sharing of existing valuable fiber assets, the proposed PON-based backhaul architecture, in which the small cells are collocated with existing FTTN remote terminals (optical network units (ONUs)), is much more economical than conventional point-to-point (PTP) fiber backhaul designs. A fully distributed ring-based EPON architecture is utilized here as the fiber-based HetNet backhaul. The techno-economics merits of utilizing the proposed PON-based FTTx access HetNet RAN architecture versus that of traditional 4G LTE-A\u27s RAN will be thoroughly examined and quantified. Specifically, we quantify the techno-economics merits of the proposed PON-based HetNet backhaul by comparing its performance versus that of a conventional fiber-based PTP backhaul architecture as a benchmark. It is shown that the purposely selected ring-based PON architecture along with the supporting distributed control plane enable the proposed PON-based FTTx RAN architecture to support several key salient networking features that collectively significantly enhance the overall performance of both the HetNet-based RAN and 4G LTE-A\u27s core (EPC) compared to that of the typical fiber-based PTP backhaul architecture in terms of handoff capability, signaling overhead, overall network throughput and latency, and QoS support. It will also been shown that the proposed HetNet-based RAN architecture is not only capable of providing the typical macro-cell offloading gain (RAN gain) but also can provide ground-breaking EPC offloading gain. The simulation results indicate that the overall capacity of the proposed HetNet scales with the number of deployed small cells, thanks to LTE-A\u27s advanced interference management techniques. For example, if there are 10 deployed outdoor small cells for every macrocell in the network, then the overall capacity will be approximately 10-11x capacity gain over a macro-only network. To reach the 1000x capacity goal, numerous small cells including 3G, 4G, and WiFi (femtos, picos, metros, relays, remote radio heads, distributed antenna systems) need to be deployed indoors and outdoors, at all possible venues (residences and enterprises)

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

Optimization of 5G Second Phase Heterogeneous Radio Access Networks with Small Cells

Due to the exponential increase in high data-demanding applications and their services per coverage area, it is becoming challenging for the existing cellular network to handle the massive sum of users with their demands. It is conceded to network operators that the current wireless network may not be capable to shelter future traffic demands. To overcome the challenges the operators are taking interest in efficiently deploying the heterogeneous network. Currently, 5G is in the commercialization phase. Network evolution with addition of small cells will develop the existing wireless network with its enriched capabilities and innovative features. Presently, the 5G global standardization has introduced the 5G New Radio (NR) under the 3rd Generation Partnership Project (3GPP). It can support a wide range of frequency bands (<6 GHz to 100 GHz). For different trends and verticals, 5G NR encounters, functional splitting and its cost evaluation are well-thought-out. The aspects of network slicing to the assessment of the business opportunities and allied standardization endeavours are illustrated. The study explores the carrier aggregation (Pico cellular) technique for 4G to bring high spectral efficiency with the support of small cell massification while benefiting from statistical multiplexing gain. One has been able to obtain values for the goodput considering CA in LTE-Sim (4G), of 40 Mbps for a cell radius of 500 m and of 29 Mbps for a cell radius of 50 m, which is 3 times higher than without CA scenario (2.6 GHz plus 3.5 GHz frequency bands). Heterogeneous networks have been under investigation for many years. Heterogeneous network can improve users service quality and resource utilization compared to homogeneous networks. Quality of service can be enhanced by putting the small cells (Femtocells or Picocells) inside the Microcells or Macrocells coverage area. Deploying indoor Femtocells for 5G inside the Macro cellular network can reduce the network cost. Some service providers have started their solutions for indoor users but there are still many challenges to be addressed. The 5G air-simulator is updated to deploy indoor Femto-cell with proposed assumptions with uniform distribution. For all the possible combinations of apartments side length and transmitter power, the maximum number of supported numbers surpassed the number of users by more than two times compared to papers mentioned in the literature. Within outdoor environments, this study also proposed small cells optimization by putting the Pico cells within a Macro cell to obtain low latency and high data rate with the statistical multiplexing gain of the associated users. Results are presented 5G NR functional split six and split seven, for three frequency bands (2.6 GHz, 3.5GHz and 5.62 GHz). Based on the analysis for shorter radius values, the best is to select the 2.6 GHz to achieve lower PLR and to support a higher number of users, with better goodput, and higher profit (for cell radius u to 400 m). In 4G, with CA, from the analysis of the economic trade-off with Picocell, the Enhanced multi-band scheduler EMBS provide higher revenue, compared to those without CA. It is clearly shown that the profit of CA is more than 4 times than in the without CA scenario. This means that the slight increase in the cost of CA gives back more than 4-time profit relatively to the ”without” CA scenario.Devido ao aumento exponencial de aplicações/serviços de elevado débito por unidade de área, torna-se bastante exigente, para a rede celular existente, lidar com a enormes quantidades de utilizadores e seus requisitos. É reconhecido que as redes móveis e sem fios atuais podem não conseguir suportar a procura de tráfego junto dos operadores. Para responder a estes desafios, os operadores estão-se a interessar pelo desenvolvimento de redes heterogéneas eficientes. Atualmente, a 5G está na fase de comercialização. A evolução destas redes concretizar-se-á com a introdução de pequenas células com aptidões melhoradas e características inovadoras. No presente, os organismos de normalização da 5G globais introduziram os Novos Rádios (NR) 5G no contexto do 3rd Generation Partnership Project (3GPP). A 5G pode suportar uma gama alargada de bandas de frequência (<6 a 100 GHz). Abordam-se as divisões funcionais e avaliam-se os seus custos para as diferentes tendências e verticais dos NR 5G. Ilustram-se desde os aspetos de particionamento funcional da rede à avaliação das oportunidades de negócio, aliadas aos esforços de normalização. Exploram-se as técnicas de agregação de espetro (do inglês, CA) para pico células, em 4G, a disponibilização de eficiência espetral, com o suporte da massificação de pequenas células, e o ganho de multiplexagem estatística associado. Obtiveram-se valores do débito binário útil, considerando CA no LTE-Sim (4G), de 40 e 29 Mb/s para células de raios 500 e 50 m, respetivamente, três vezes superiores em relação ao caso sem CA (bandas de 2.6 mais 3.5 GHz). Nas redes heterogéneas, alvo de investigação há vários anos, a qualidade de serviço e a utilização de recursos podem ser melhoradas colocando pequenas células (femto- ou pico-células) dentro da área de cobertura de micro- ou macro-células). O desenvolvimento de pequenas células 5G dentro da rede com macro-células pode reduzir os custos da rede. Alguns prestadores de serviços iniciaram as suas soluções para ambientes de interior, mas ainda existem muitos desafios a ser ultrapassados. Atualizou-se o 5G air simulator para representar a implantação de femto-células de interior com os pressupostos propostos e distribuição espacial uniforme. Para todas as combinações possíveis do comprimento lado do apartamento, o número máximo de utilizadores suportado ultrapassou o número de utilizadores suportado (na literatura) em mais de duas vezes. Em ambientes de exterior, propuseram-se pico-células no interior de macro-células, de forma a obter atraso extremo-a-extremo reduzido e taxa de transmissão dados elevada, resultante do ganho de multiplexagem estatística associado. Apresentam-se resultados para as divisões funcionais seis e sete dos NR 5G, para 2.6 GHz, 3.5GHz e 5.62 GHz. Para raios das células curtos, a melhor solução será selecionar a banda dos 2.6 GHz para alcançar PLR (do inglês, PLR) reduzido e suportar um maior número de utilizadores, com débito binário útil e lucro mais elevados (para raios das células até 400 m). Em 4G, com CA, da análise do equilíbrio custos-proveitos com pico-células, o escalonamento multi-banda EMBS (do inglês, Enhanced Multi-band Scheduler) disponibiliza proveitos superiores em comparação com o caso sem CA. Mostra-se claramente que lucro com CA é mais de quatro vezes superior do que no cenário sem CA, o que significa que um aumento ligeiro no custo com CA resulta num aumento de 4-vezes no lucro relativamente ao cenário sem CA