Survey on 5G Second Phase RAN Architectures and Functional Splits

The Radio Access Network (RAN) architecture evolves with different generations of mobile communication technologies and forms an indispensable component of the mobile network architecture. The main component of the RAN infrastructure is the base station, which includes a Radio Frequency unit and a baseband unit. The RAN is a collection of base stations connected to the core network to provide coverage through one or more radio access technologies. The advancement towards cloud native networks has led to centralizing the baseband processing of radio signals. There is a trade-off between the advantages of RAN centralization (energy efficiency, power cost reduction, and the cost of the fronthaul) and the complexity of carrying traffic between the data processing unit and distributed antennas. 5G networks hold high potential for adopting the centralized architecture to reduce maintenance costs while reducing deployment costs and improving resilience, reliability, and coordination. Incorporating the concept of virtualization and centralized RAN architecture enables to meet the overall requirements for both the customer and Mobile Network Operator. Functional splitting is one of the key enablers for 5G networks. It supports Centralized RAN, virtualized Radio Access Network, and the recent Open Radio Access Networks. This survey provides a comprehensive tutorial on the paradigms of the RAN architecture evolution, its key features, and implementation challenges. It provides a thorough review of the 3rd Generation Partnership Project functional splitting complemented by associated challenges and potential solutions. The survey also presents an overview of the fronthaul and its requirements and possible solutions for implementation, algorithms, and required tools whilst providing a vision of the evaluation beyond 5G second phase.info:eu-repo/semantics/submittedVersio

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

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

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

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

On the 40 GHz Remote Versus Local Photonic Generation for DML-Based C-RAN Optical Fronthaul

[EN] Local and remote photonic millimeter wave (mmW) signal generation schemes are theoretically and experimentally evaluated in order to compare both approaches for practical deployment in a cloud radio access network (C-RAN) fronthaul network. The paper presents a full comprehensive formulation of the frequency response of a system based on a directly modulated laser transmitting data over 40 GHz signal which is generated by external carrier suppressed modulation and optical frequency multiplication. Theoretical and experimental characterization of the system response at baseband and mmW band for local and remote generation setups show very good agreement. The remote configuration leads to a higher electrical output power (i.e., 15 dB higher in 25 km fiber links) than the local generation setup in the mmW band due to the combined effect of chirp and fiber dispersion, although intermodulation distortion is higher in the former case. Transmission experiments using quadrature phase-shift keying (QPSK) signals with 250 MHz bandwidth centered at 0.5 GHz over 10 and 25 km fiber links also confirm the superior performance of the remote setup, whereas the local setup leads to similar results to optical back-to-back (OB2B) measurements, which is also validated with data signals centered at different frequencies within the laser bandwidth frequency range. Finally, experimental results show the quality of the recovered signals in terms of error vector magnitude (EVM) as a function of the received electrical power and demonstrate that no further penalties are introduced by photonic mmW signal generation with respect to electrical back-to-back (EB2B) levels.This work was supported in part by Generalitat Valenciana through PROMETEO2017/103, in part by Ministerio de Ciencia, Innovacion y Universidades through FOCAL RTI2018-101658-B-I00, in part by MEYES under Grant LTC18008, in part by the Ministry of Industry and Trade in Czech Republic under Grant FV40089, and in part by European Cooperation in Science and Technology under Grants CA16220 and CA19111.Vallejo-Castro, L.; Mora Almerich, J.; Nguyen, D.; Bohata, J.; Almenar Terre, V.; Zvanovec, S.; Ortega Tamarit, B. (2021). On the 40 GHz Remote Versus Local Photonic Generation for DML-Based C-RAN Optical Fronthaul. Journal of Lightwave Technology. 39(21):6712-6723. https://doi.org/10.1109/JLT.2021.3102818S67126723392