open icdt: convergence of it, ct and dt for 5g ran and beyond ict_convergence of... ·...

Open ICDT: Convergence of IT, CT and DT for 5G RAN and beyond

Open ICDT: Convergence of IT, CT and DT for 5G

RAN and beyond

2019


Executive Summary

With the advent of 5G, the wireless networks are becoming more and more complex than previous

generations. There are several reasons behind that, including the much higher network densification,

the larger network scale, the more demanding applications with diverse requirements, and so on.

However, the current networks are developed in a closed, instead of open way with tight coupling

among different components, leading to the difficulty in quick system upgrade, compatibility, fast

service roll-out, innovation etc. A foreseeable big challenge lies ahead of large-scale deployment of

5G and beyond.

While architects in the telecom industry is designing future networks of much higher flexibility,

scalability, agility and manageability, a lot of experience could be and is being learned from

neighboring industries of IT and DT. In the past few decades, the society has witnessed the huge

success by IDT technologies such as the virtualization, the cloud, the artificial intelligence etc.

In 2015, FuTURE FORUM published two landmark White Papers, proposing that the 5G networks

should feature “soft” and “green”. The White Paper (WP) is a continuity of previous White Papers,

reflecting the continuous thinking, progresss and evolvement on the 5G network design philophy,

which makes itself the programmatic document for other WPs published in the same series. In this

WP, it is pointed out that the design of future wireless networks is deemed to a journey of

convergence of IT, CT and DT technologies. In fact, such trend has come to the surface for the first

time in 2010 when the concept of C-RAN (Centralized, Collaborative, Cloud and Clean RAN) was

first proposed. Then it did not receive enough attention until in 2012 when the ETSI NFV ISG was

set up to specify the adoption of virtualization technologies to the telecom networks. Recently more

and more people are aware of the importance and the huge potential by big data and the artificial

intelligence. The convergence trend is accelerating its momentum. The O-RAN Alliance, which was

formed in 2018 and targeted at designing an open and smart RAN is a typical representative of such

trend.


The white paper gives an introduction of future 5G networks based on ICDT convergence.

Essentially, the ICDT convergence-basaed ruture RAN features two points.

• Open X. There are multiple-fold meanings, including open architecture, open interface, open

source and open hardware reference design. Open interfaces are essential to enable smaller

vendors and operators to quickly introduce their own services, or enable operators to customize

the network to suit their own unique needs. It also enables multi-vendor deployments, enabling

a more competitive and vibrant supplier ecosystem. While open source software enables faster

product development and encourages innovation, opening the reference design for hardware

could further reduces the R&D cost and the entry threshold for small vendors, which further

spurs the innovation throughout the industry and bring prosperity to the ecosystem.

• Embedded RAN intelligence. The basic idea is to endow the RAN with intelligence with the

introduction of ML/AI techniques, which could not only help optimize and simply the network

management and orchestration, but also help improve system performance optimization.


摘要

随着5G的到来，无线网络比前几代变得越来越复杂。其背后的原因有很多，包括更高的网络密度，更

大的网络规模，更多有着不同需求的应用等等。但是，当前的网络是封闭，而不是开放的，网络中各个组

件之间紧密耦合，导致系统在快速升级、兼容性、快速服务推出、创新等方面存在困难，这一可预见的巨

大挑战就影响5G的大规模部署商用。

为了应对上述的巨大挑战，电信行业的架构师们正致力于设计更具灵活性、可伸缩性、敏捷性和可管

理性的未来网络。在这一过程中，来自IT和DT行业经验被大量汲取。近几十年来，虚拟化、云计算、人工

智能等IDT技术取得了巨大的成功。

2015年，未来论坛发布了两份具有里程碑意义的白皮书，提出5G网络应具有“绿色” 与“柔性”的特

性。目前这版白皮书是之前版本的延续，反映了对5G网络设计理念的不断思考、进步和发展，同时它也是

同一系列其他白皮书的纲领性文件。它指出，未来无线网络的设计将是IT、CT和DT技术的融合过程。实际

上，这种趋势早在2010年，C-RAN（集中化、协作化、云化和绿色RAN）概念首次提出时就已浮出水面。

然后，直到2012年ETSI NFV-ISG成立，并开始制定电信网络虚拟化技术时，这一理念才受到足够的关注。

近年来，越来越多的人们意识到大数据和人工智能技术的重要性和巨大潜力。理念趋同的趋势正在加速这

一发展势头。而这种趋势中的典型代表是成立于2018年，并致力于设计开放和智能RAN的O-RAN联盟。

本白皮书介绍了基于ICDT融合的未来5G网络。基于ICDT融合的未来无线接入网具有如下两个特点：

1、开放。包括开放架构，开放接口，开源和开放硬件参考设计。开放接口能够使小型供应商和运营商快

速引入自己的服务，也能够使运营商定制网络以满足自身的独特需求；此外，开放接口还支持多供应商部

署，从而实现更具竞争力和活力的供应商生态系统。开源软件能够加快产品开发速度、鼓励创新。而开放

硬件参考设计可以进一步降低研发成本和小厂商的准入门槛，从而进一步推动整个行业的创新并为生态系

统带来繁荣。

2、智能。通过引入ML / AI技术使RAN具有智能，这不仅可以帮助优化和简化网络管理和编排，还可以帮

助提高系统性能。


Table of Contents

1 Background ........................................................................................................................................................... 1

2 Key technologies for ICDT convergence for 5G/B5G/6G RAN .......................................................................... 2

2.1 Virtualization ............................................................................................................................................ 2

2.1.1 Hardware and software decoupling ............................................................................................... 2

2.1.2 Network slicing ............................................................................................................................. 2

2.1.3 Network sharing ............................................................................................................................ 4

2.1.4 Challenges and future studies ....................................................................................................... 4

2.2 Big Data .................................................................................................................................................... 5

2.3 Artificial Intelligence ................................................................................................................................ 6

2.4 Open Source .............................................................................................................................................. 8

3 O-RAN: the ICDT-convergence RAN .................................................................................................................. 9

3.1 Overview ................................................................................................................................................... 9

3.2 Use Case .................................................................................................................................................. 10

3.2.1 Unmanned Aerial Vehicle (UAV) .............................................................................................. 10

3.2.2 Quality of Experience ................................................................................................................. 10

3.3 O-RAN Architecture ............................................................................................................................... 11

3.4 Key elements of O-RAN network ........................................................................................................... 12

3.4.1 RAN intelligence ........................................................................................................................ 12

3.4.2 Whitebox ..................................................................................................................................... 15

3.4.3 Open interface ............................................................................................................................. 18

3.4.4 Cloud platform ............................................................................................................................ 21

3.4.4.1 Hierarchical Cloud Architecture ..................................................................................... 24

3.4.5 Open Source O-RAN .................................................................................................................. 27

4 Summary ............................................................................................................................................................. 27

5 Reference ............................................................................................................................................................ 28

Acknowledgement ..................................................................................................................................................... 29

Abbreviation .............................................................................................................................................................. 29


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1 Background

It is deemed that the fifth-generation (5G) mobile networks will bring fundamental change to the society. In

ITU-T, three key 5G scenarios have been defined, including: enhanced mobile broadband (eMBB), massive

machine type communication (mMTC) and ultra-reliable low latency communication (uRLLC) [1][2]. Compared to

the transition from 3G to 4G where it was mainly about user data rate increase, the feature of 5G is more about

“internet of everything” and a diverse range of services, ranging from mobile Internet, industrial Internet, to

Internet of Vehicles and so on. Many more new services are expected to be supported by 5G, including augmented

reality, virtual reality, and ultra-high definition video. In addition, 5G will provide its services to abundant vertical

industry applications such as autonomous driving, industrial control, medicine, transportation and so on.

The start of 5G study could be traced back to 2012 when LTE was at its peak (yet of course today LTE is still the

most dominant mobile network in many countries). In 2014, IEEE Communication Society published its first issue

on dedicated 5G topic. It was at that time that people began to grasp the idea of what the future 5G could look like

and what could be the key technical solutions [3][4]. As one of the young pioneers in the 5G endeavor, FuTURE

FORUM published two landmark White Papers [5][6], proposing that the 5G networks should feature “soft” and

“green”. While “Soft” means that 5G elements, from core networks to access networks, should be flexible, elastic

and agile enough to provide users with timely services in response to the ever-fast change on market trend, “Green”

emphasizes the importance of energy saving and environment conservative. Today the philosophy has been widely

accepted and recognized in the industry. Following that vision, many new technologies emerged to serve that

purpose.

In the meantime, in the past few decades, the society has witnessed the huge success in the neighboring

industries of IT and DT by IDT technologies such as the virtualization, the cloud, the artificial intelligence etc.

Following that, a lot of experience could be and is being learned by the telecom architects while designing the

future networks.

The White Paper (WP) is a continuity of previous White Papers from FuTURE FORUM, reflecting the

continuous thinking, progresss and evolvement on the 5G network design philophy. In particular, the White Paper

points out that the design of future wireless networks is deemed to a journey of convergence of IT, CT and DT

technologies. In fact, such trend has come to the surface for the first time in 2010 when the concept of C-RAN

(Centralized, Collaborative, Cloud and Clean RAN) [7] was first proposed. Then it did not receive enough attention

until in 2012 when the ETSI NFV ISG was set up to specify the adoption of virtualization technologies to the

telecom networks. Recently more and more people are aware of the importance and the huge potential by big data

and the artificial intelligence. The convergence trend is accelerating its momentum.

The WP serves as a programmatic guideline for other documents in the seriese of published WP by FuTURE

FORUM. Readers are suggested to read this WP first to understand the overall design principles and the key

philophy before reading others.


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In Section 2, key technologies of IT and DT will be introduced while in Section 3, the future wirelesss networks

based on ICDT-convergence technolgoeis are described, using O-RAN [8] as an example, followed by the

conclusion.

2 Key technologies for ICDT convergence for 5G/B5G/6G RAN

2.1 Virtualization

2.1.1 Hardware and software decoupling

The evolution of mobile networks from 2G to 4G has been mainly driven by the supporting applications, whose

requirements defined the features of the network.

As each generation of mobile networks has a design to deliver specific services, the introduction of novel

applications to satisfy customers’ demands requires the re-design or the introduction of novel functionalities in the

network. However the traditional way to implement specific network functionalities by tightly coupled hardware

and software has the following drawbacks:

(1) Updating the already deployed network functions requires the introduction of novel hardware equipment;

(2) Supporting novel applications could require totally novel network design.

(3) It also has high capital expenditure (CAPEX) in deploying new network architectures and operating

expenditure (OPEX) when upgrading network functionalities.

5G and the future networks undoubtedly hold enormous potential. It demands for increased speed, performance,

scalability, and flexible service deployment.

While the IT infrastructure continues to evolve, virtualization is being embraced in other areas such as mobile

networks[9]. Virtualization enhances the software and hardware decoupling by creating abstracted instances of

hardware platforms, operating systems, storage, devices, and computer network resources. This means that, with

virtualization, software runs in COTS equipment (e.g., standard server) by exploiting a virtual machine (VM) or

container instead of a dedicated hardware. As VMs can be moved across different hardware platforms,

virtualization introduces flexibility by means of flexible VM placement and migration. Other benefits are in terms

of reduced CAPEX and OPEX, as the introduction of new services requires the introduction of new VMs without

requiring any effort from a hardware point of view.

2.1.2 Network slicing

Let's see some drives behind 5G and wide range of IOT requires virtualization.

(1) 20+ Billion connected devices by 2020. The IOT device will range from those that send Gbps of data to those

that send only a few bits every month


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(2) low latency for some mission critical devices such as driverless cars will need some of the network function

being distributed to the edge and some being pooled in the cloud.

(3) Vertical applications requiring different types of services from the network: high bandwidth, low power, low

latency and high availability.

In order to support such varying use cases across multiple verticals, operators need to shift from being network

centric to being more service oriented. And this shift could be realized by network slicing.

According to the study done by 3GPP, network slicing is presented as a valid solution to simultaneously handle

multiple verticals in 5G networks in a robust way. Each of these use cases requires a different configuration of the

requirements and parameters in the network. It means they need their own network slice.

The best way to implement these network slices would be virtualization. This implementation includes SDN,

NFV and network orchestration. SDN controller will configure and build the network slice, the network services

are turned into virtualized VNFs，and can be scaled in/out by network orchestration. Virtualization helps to bring

into reality the idea of network slicing. And the Virtualization will go through the Core (core network) to Edge

(radio access network) to provide E2E network slicing.

Virtualization can help network slicing to support multiple virtual networks over one physical network

infrastructure. Network slicing permits the logical separation of a network so that each slice provides unique

connectivity—but all slices run on the same shared infrastructure. In this way, virtualization provides a new level of

flexibility. To efficiently support certain sets of services, each network slice will be able to access different types of

resources, such as infrastructure (e.g., VPNs, cloud services) and virtualized network functions (VNFs). With

virtualization, operators will be able to create custom networks with unique sets of capabilities, and will be able to

create unique services that are customized for various use cases such as IoT, automated cars, streaming video,

remote health care, and so on by offering various network performance, capacity, latency, security, reliability, and

coverage.

From a network point of view, the exploitation of the virtualization paradigm brings to the concept of Network

Function Virtualization (NFV), proposed in 2012 by the European Telecommunications Standards Institute (ETSI).

With NFV, network node functions (such as firewall, switches, etc.) are virtualized and thus totally decoupled from

the underlying hardware running such functions. The three main components of NFV are:

1. Virtualized Network Functions (VNFs). A VNF is the software implementation of network functions which

can be composed of one or more VMs or containers. A set of VNFs executed in a specific order is referred to as

Service Function Chain (SFC).

2. Network function virtualization infrastructure (NFVI). This includes all the hardware and software

components that build the environment where VNFs are deployed.

3. Network functions virtualization management and orchestration (NFV-MANO) Architectural Framework.

This is usually composed of:


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a) Virtual Infrastructure Manager (VIM), controlling physical and virtual infrastructures;

b) VNF manager, handling VNFs and SFCs and their placement;

c) Orchestrator, in charge of managing the VIM and the VNF manager.

Many service providers are already adopting NFV and SDN as a means of boosting efficiencies, launching

services faster, and supporting a wider range of applications.

For example, 4G LTE mobile network is widely deployed today, and consists of RAN and EPC, and IMS is a

subsystem that provides the ability to handle packetized voice and video calls thereby supporting Volte and other

services that are commonly used today. Visualized EPC has become a reality after many successful trials

worldwide. Many visualized IMS components have also been successfully demonstrated. Virtualizing the EPC

requires moving several functional components such as subscriber databases, policy servers and gateways to one or

more VMs(virtual machines) hosted as VNFs(virtual network functions) in COTS servers.

Virtualization will provide the various services built upon a single mobile network infrastructure, opening up the

potential for limitless numbers and types of supported use cases. 5G and the future networks depend on

virtualization technologies such as SDN and NFV as well as orchestration. SDN provides the complete abstraction

of the physical network infrastructure, and NFV with orchestration allows the deployment of network functions on

common cloud platforms to provide scalability and flexiblity.

2.1.3 Network sharing

While Virtual Network Operators have played a role in the mobile networks service market, the possibility of

having different operators to share the same physical infrastructure is becoming more important. Network sharing

solutions are already available and standardized by 3GPP in order to improve network availability, integration and

reliability. Studies show that operators could save at least 40% of their costs with network sharing.

Network virtualization is a viable solution to implement network sharing. And to enable end-to-end cellular

network virtualization, the mobile CORE and RAN have to be virtualized.

2.1.4 Challenges and future studies

Network functionalities are no longer considered as a monolithic block implemented in a specific layer of the

protocol stack. Further study is needed to define a full set of functionalities as well as the interfaces between the

functionalities. Such study should be driven by considering aspects such as latency, jitter and data overhead for the

communication between network functionalities, to avoid that a fine-granular definition of network functions will

involve higher delays and overheads. In addition, APIs for the communication between network functionalities

should be defined to guarantee interoperability.

According to the front pages, virtualization is used to guarantee flexibility, customization and configurability, but


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there is a lack in understanding how virtualized solutions will be able to guarantee the expected performance in

terms of latency and reliability for 5G and the future. Performance degradation when functionalities run in software

instead of dedicated hardware is a well-known issue, so there needs more study in this area.

2.2 Big Data

With the rapid development of the mobile Internet, the level of informatization in all walks of life continues to

increase, which is accompanied by explosive growth of various types of data. Human society has entered the era of

structural and unstructured data in units of “Pb”. The emergence of cloud computing enables these large,

high-speed, and multi-variable data to be efficiently stored and analyzed and computed. Big data has become a new

resource and has become an important productivity in various industries. Through the analysis of the nature of data,

we can predict development trends, guide future decisions, and optimize the allocation of resources[10]. Therefore,

all walks of life have devoted themselves to the big data industry. The big data market is huge and promising.

In the field of mobile networks, with the commercialization of 5G, traditional user services upgrade and services

have been extended to various vertical industries. The new 5G era and future mobile networks will have a profound

impact on big data. First, the amount of data will increase dramatically. By increasing the connection rate and

reducing the delay, 5G increases the amount of data generated per unit time, and the number of connected devices

per unit area increases exponentially, and massive raw data will be collected. Second, the data type will be more

abundant. In the 5G era, the connection between people and things, things and things will increase dramatically,

and data collection channels will be more abundant, such as connected cars, wearable devices, and robots. The data

types will be more diverse. From the connected content, 5G generated new applications such as car networking,

smart manufacturing, smart energy, wireless medical, wireless home entertainment, and drones. That will create

new rich data dimensions. Third, 5G will promote the continuous development of big data technology. On the one

hand, the expansion of data volume and the richness of data collection channels will place higher demands on big

data storage technology and acquisition technology. On the other hand, with the increasing amount of data, the

increasing diversity of data types, and the increasing use of big data applications, massive, low-latency,

unstructured data features will put forward higher requirements on the computing ability, real-time engine and data

processing engine of big data industry in the future.

The natural data advantages of mobile networks and the continuous evolution of big data technologies will

synergistically promote the emergence of more application scenarios. The internal applications of the mobile

network include network optimization, such as collecting Massive MIMO inter-beam interference data, and

analyzing and optimizing; Performing joint signal processing on all access points through big data analysis, guiding

each antenna and micro base station to achieve low interference. Collecting user and network data to analyze with

big data to improve positioning accuracy. And also, in the case of CU-DU split in the 5G era, when the CU

manages multiple DUs, large amount of user time and space data can help optimize design, and realize different

resource allocations during busy or idle time.

The application of big data in the vertical industry is more extensive, including promoting smart cities, by

collecting dozens of data from operator network, billing or other systems, and completing quasi-real-time data


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analysis and warehousing to achieve Real-time and accurate insight analysis of multi-dimensional information such

as user portraits, location information, business characteristics, network capabilities, terminal structures, etc. That

will provide effective support for the construction of government smart cities; Also in the car networking industry,

through the data analysis which is reported by terminals, the network will make decisions about path planning,

hazard warning, and those data will also help car network optimization and upgrade for offering better service to

vehicle terminals; Big data in various mobile Internet applications also plays a role, through the utilization of

consumer behavior data. Different contents can be delivered for different target audience, and content performance

can be ultimately measured.

Big data has a wide space in the application of mobile networks, and it also faces many challenges. It needs to

solve the problems of data mining computational complexity, timeliness, energy efficiency, security, etc., and also

puts forward a lot of innovative topics for mobile network standardization and implementation.

2.3 Artificial Intelligence

Artificial Intelligence, is a new technical science that studies and develops theories, methods, techniques, and

applications for simulating, extending, and extending human intelligence. It is a branch of computer science that

attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner

similar to human intelligence. Technically, it is not a new business process or a new business model, but a

fundamental transformation of existing business processes and business models. Since the birth of the concept, AI

has experienced several troughs and revival, and the current boom lies on deep learning which is the core driver.

With the rise of Internet + and the digital transformation of various industries, high-quality data has been gradually

accumulated and laid the foundation for many frontier industries, now AI has been used in various areas, such as

E-commerce retail, warehousing logistics, intelligent shopping guide and customer service, medical health

monitoring and diagnosis, smart phone voice assistant, intelligent layer of education, personalized counseling.

Mobile network used to be a closed system, but now in the 5G area, the network architecture has changed a lot,

new technologies such as NFV, SDN, Cloud are involved, in order to make the network more flexible, open and

satisfy various services in vertical industries. So the network becomes complex and the difficulty of operation and

maintenance management increases. At the same time, the concept of slice appears, the connection type and QoS

requirements vary a lot, and how to properly allocate resources and present to the industry customers to visualize

the slice management interface is crucial. Artificial intelligence technology has natural advantages in solving

high-volume data analysis, cross-domain feature mining and dynamic strategy generation. Combining AI and the

new mobile network will give new modes and capabilities for network operation and operation and

maintenance[11].

Using AI in mobile network can optimize the wireless network performance, achieve intelligent management of

operation and maintenance, and intelligent slicing. Some specific applications are described below.

⚫ AI+ performance optimization

As a key technology of 5G, Massive MIMO can greatly increase system capacity. The antennas in different


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directions form a specific beam, and transmit data to UEs in a specific direction, which can reduce interference and

improve receiving gain. However, the algorithm for calculating and forming the beam is very complicated, and

different scenarios may need to match different algorithms. According to the distribution rules of users which are in

high-rise buildings, venues, areas with tidal effects (such as college dormitory areas, canteens and classrooms, CBD

and residential, shopping malls and streets), Combining AI and Massive MIMO technology can adjust the beam

distribution of the broadcast/control channel to achieve optimal coverage and capacity and reduce interference.

For example, for a fixed-site scenario, since the distribution of personnel is relatively fixed for a long time, the

broadcast weight adaptive can be designed according to this feature to achieve optimal coverage. Based on data

such as network management and MR, combined with scene recognition and related algorithms, it identifies scenes

such as sports events or concerts, and calculates optimal weights based on this scene and current user distribution，

in order to improve the CQI, SINR and other indicators in the venue area. For areas with tidal effects, intelligent

adjustment can also be made based on the characteristics of traffic distribution in each area and the tidal effect

period.

⚫ AI+ Intelligent slice

The application of artificial intelligence in network slicing scenarios includes intelligent resource allocation and

intelligent management. The performance and parameters of different slicing requirements are different, including

QoS (delay, rate, packet loss rate), capacity-related parameters（active user number）, and so on. Combined with the

historical slice deployment, the AI algorithm models and analyzes the service class, template information, resource

characteristics, and configuration parameters, and infers the optimal resource allocation scheme to ensure the

performance of the slice and the load of the network. The purpose of intelligent slice management is to ensure the

normal operation of the slice. The network management system or the probe monitors and collects the

corresponding slice data. Based on the data collected by the monitoring, the AI enhanced data analysis triggers the

pre-defined event to be reported to the policy center. The self-healing and self-optimization processing is

automatically performed by the policy center. In addition, cross-slicing strategy synergy, slice self-healing, etc., can

be enhanced with AI technology.

⚫ AI+ Intelligent operation and maintenance

Intelligent operation and maintenance using AI technology includes scenarios such as device energy saving,

abnormal detection, and alarm analysis. For servers deploying specific communication network functions in the

data center, by analyzing time, service, and composite data, operators can predict the server busy/free time through

AI algorithm modeling, and periodically shut down some servers to achieve energy saving. Anomaly detection is

based on different network state-aware tasks, abstractly generating different detectors, initiating corresponding

sensing tasks according to requirements, and delivering the index subscriptions to the data collection platform, such

as performance, alarm, log, configuration, etc., and starting related analysis. The AI algorithm is used for abnormal

analysis and detection, such as dynamic threshold, single-dimensional analysis, multi-dimensional correlation

analysis, and time series prediction analysis. The alarm analysis method is to extract the common features in the

diversity alarm, quickly guide the common fault points, solve the problem first, reduce the operation and

maintenance difficulty, and improve the processing efficiency. Based on artificial intelligence feature mining


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algorithm, the system can synthesize multi-dimensional historical data, such as alarm, performance, configuration

data, operation log, fault resolution history, fault handling experience database, etc., automatically mine potential

features and rules which is hardly relying on manual experience, and output a matching rule base for fault events

and features.

2.4 Open Source

Open source model has been widely adopted by ICT industry and academic which accelerated academic research,

SW/HW design, and generated commercial value in many ICT areas. From WiKi definition – “Open source

products include permission to use the source code, design documents, or content of the product. It most commonly

refers to the open-source model, in which open-source software or other products are released under an open-source

license as part of the open-source-software movement. Use of the term originated with software, but has expanded

beyond the software sector to cover other open content and forms of open collaboration”, such as open source

hardware, open source cores etc.

Before “open source” was proposed and adopted in 1998, there were already many SW collaboration and sharing

efforts. In 1950-1960s, the computer SW and HW were highly integrated and sold together, usually source code

was provided, and many SW applications, and OS enhancement, tools was developed and shared freely in academia.

In late 1960s, OS, SW were getting more and more complex and cost of development dramatically increased. SW

source code sharing started to decline, company mostly was not sharing the source code anymore, and SW was

licensed separated to HW. In early 1970s, AT&T distributed UNIX to government and academia with no cost, but

in 1980s, AT&T stopped the free distribution.

In 1983, GNU Project was launched to write a complete operating system free, the first version of GNU general

public license was published in 1989, and the complier, debugged and also GNU Emacs later achieved successes.

In 1912, Linus Torvalds released the OS kernel version 0.12 with GNU General Public License. The combination

GNU and the Linux kernel made the first complete free software operating system.

In 1998, the "open source" was formally proposed, the change from “free software” to “open source” was mainly

in reaction to Netscape's Navigator source code release and try to bring free software principles and benefits to not

only non-profit org but also the commercial-software. The "Freeware Summit" later on was named the "Open

Source Summit", which brought many leaders of the open-source projects.

Till now days, many open source projects succeeded, the APACHE, MySQL, CGI laid foundation of Internet

development. JAVA, Python, Swift, Git became the popular SW language and development tools. Web2.0, Cloud

computing infrastructure, big data, AI framework, Android, etc., which were becoming the core ICT infrastructure

and enabler.

Inspired by Open source SW achievement, Open source hardware, such as the TIP project, open source CPU

cores, such as RISC-V are also established in recent years, aiming to create broader impact not only in SW industry

but also other ICT area from the silicon, to HW design, to OS, SW infrastructure and applications.

For telecom industry, which benefits from Open source ecosystem, such as recent NFV, SDN effort, DPDK


9

packet processing SW was also widely used in router/gateway. Cloud Native was widely adopted by Edge

computing framework etc. More and more open source SW was used in telecom, and telecom also initialized more

and more open source project, like OpenAirInterface, ONAP, oRAN etc.

However, for open source in telecom industry, which also has some challenges, such as the smaller number of

users’ base from several companies, not like an OS project which has much boarder user base and companies

involved. Telecom products are highly standardized, usually IP and patents are very critical and the licensing model

are complex, open source doesn’t means “open IP” and free to use the IP associated to the standard. So it may

cause some confusion and no previous successful story in this area on open source SW highly related to standard

and IP.

The good thing is that oRAN considered and solved this issue with “dual-charter” model. The oRAN open

source officially formation was on 4/2/2019 in Linux Foundation, Apache 2.0 license for software community,

O-RAN software license for specification-code project. Therefore, the innovative “dual-charter” model further

improves the license model to inspire telecom industry to actively contribute to open source projects and

accelerated the ICT convergence, combining the IT and CT industry open efforts, accelerate ICDT evolution.

3 O-RAN: the ICDT-convergence RAN

3.1 Overview

The O-RAN Alliance is committed to promote Radio Access Network (RAN) to evolve towards more openness

and intelligence. The O-RAN network [8], proposed by the O-RAN Alliance, is a typical representative of the

ICDT-convergence RAN. The core features cover the following four aspects:

1. Open interfaces. Most of the traditional radio network interfaces are closed and specific to vendors’

implementation, for instance, the interface between RAN BBU and RRU and that between BBU and OMC. The

opening interfaces aims at standardizing and opening the radio network interfaces that are not open originally. It

will energize the innovation and reduce the cost, whilst bringing about a vibrant industrial ecology and cutting

down the cost of various module units.

2. Open source software. The traditional radio equipment are developed by manufacturers independently and

separately in a considerably long period and at a relatively high cost. The purpose of open source software is to

build up a common radio network and open-source the software, such as the operating system, protocol stack

software and so forth. It can lower the cost and accelerate the progress of development and research (R&D) with

involving more vendors into R&D and sharing the open source codes. Based on the open source software, every

industrial partner can focus on the core algorithms that reflect the self-ability and value, and the R&D of

differentiated functionalities. Consequently, the win-win outcome will be produced through lowering the cost of the

overall industrial R&D as well as reflecting the differentiated value of each unit.


10

3. White box hardware. There are some high barriers for entry into the R&D for traditional radio equipment

vendors, which makes against the participation of more small and medium-sized innovative companies. The idea of

white box hardware is to use the open reference designs to form the scale effect, reduce the cost of the whole

industry chain, lower the cost and difficulty of R&D, and attract more small and medium-sized enterprises into the

telecom industry to promote competition and innovation.

4. Intelligence. Its target is to endow radio networks with the certain intelligence by introducing wireless big

data, artificial intelligence (AI) and opening network capability. Big data and AI can support the requirements of

the intricate vertical industry services and slicings in an agile and efficient way, realize the effective automatic

network management and the efficient utilization of spectrum resource under complicated networking, reduce the

complexity of the existing radio resource management and scheduling algorithms, and further enhance the

performance and energy efficiency of radio networks.

3.2 Use Case

3.2.1 Unmanned Aerial Vehicle (UAV)

Air Traffic Management systems for low-altitude drones has been applied to support the UAV services, for

example UTM in the USA. And NASA and the Federal Aviation Administration (FAA) recognized the need for a

way to safely manage UAV flying at low altitudes in airspace not currently managed by the FAA. For more than 25

years, NASA has conducted air traffic management system research in partnership with the FAA, providing a

variety of computer-based tools that help improve flight efficiency, reduce delays, and reduce fuel use and

emissions while maintaining safety in increasingly crowded skies. Today, with innovators constantly identifying

new, beneficial applications for UAS – goods delivery, infrastructure inspection, search and rescue, agricultural

monitoring – a safety system may be needed to help ensure this newest entrant into the skies does not collide with

buildings, larger aircraft. Building on its legacy of work in air traffic management for crewed aircraft, NASA is

researching prototype technologies such as airspace design, dynamic geofencing, congestion management and

terrain avoidance for a UAS Traffic Management (UTM) system that could develop airspace integration

requirements for enabling safe, efficient low-altitude operations.

RAN can retrieve necessary of aerial vehicles related measurement metrics from network level measurement

report and NMS (may acquire data from applications) for constructing/training relevant AI/ML model that will be

deployed in RAN. For example, this could be UL/DL interference from/to aerial vehicles, the detection of aerial

vehicle UEs, and available radio resource (e.g. frequency, cell, beam, BWP, numerology) prediction. And the radio

resource allocation for on-demand coverage for UAV considering the flight path information.

3.2.2 Quality of Experience

The highly demanding 5G native applications like Cloud VR are both bandwidth consuming and latency

sensitive. However, such traffic-intensive and highly interactive applications could not be efficiently satisfied with


11

current semi-static QoS framework. This is because dynamic traffic volume could be generated by user interactions,

not to mention also the fast fluctuating radio transmission capabilities. And therefore, the service QoE could not be

always guaranteed except with overprovisioning QoS considering the peak traffic demand.

Application specific QoE prediction and QoE based proactive close-loop network optimization in real-time

would help deal with the issue. Before QoE degrades, the radio resources could be allocated to the user and services

where the radio resources are most urgently required. In this way, QoE is optimized while the radio resources are

most efficiently utilized.

With the software defined RAN intelligent controller and the open interfaces, AI models could be easily

deployed and upgraded to optimize QoE of emerging vertical services. Multi-dimensional data can be acquired and

processed via ML algorithms to support traffic recognition, QoE prediction, and finally guiding close-loop QoS

enforcement decisions. ML models can be trained offline, while model inference will be executed real-time. The

interface help to deliver the policy/intents/AI models and RAN control to enforce the QoS for the QoE

optimization.

3.3 O-RAN Architecture

The following Figure shows the O-RAN architecture defined by the O-RAN Alliance [8].


12

Figure 3.1 O-RAN reference architecture

3.4 Key elements of O-RAN network

3.4.1 RAN intelligence

(1) Background

Networks will become increasingly complex with the advent of 5G, densification and richer and more

demanding applications. To tame this complexity, we cannot use traditional human intensive means of deploying,

optimizing and operating a network. Instead, networks must be self-driving and high-efficiency, they should be

able to leverage new learning based technologies to automate operational network functions and reduce OPEX. The

O-RAN alliance will strive to leverage emerging AI techniques to embed intelligence in every layer of the RAN

architecture to empower dynamic local radio resource allocation and optimize network-wide efficiency.

(2) Hierarchical RAN Intelligent Controller (RIC)

Hierarchical (Non-RT and Near-RT) RAN Intelligent Controller (RIC) with the A1 and E2 interfaces are

introduced in O-RAN to enable the embedded intelligence to offer efficient, optimized device and radio resource

management through intent/policy based closed-loop management and control.

⚫ The Non-real-time RAN Intelligent Controller and A1 Interface

The primary goal of RIC non-RT is to support non-real-time intelligent radio resource management, higher layer

procedure optimization, policy optimization in RAN, and providing AI/ML models to RIC near-RT. With the

amount of L1/L2/L3 data collected from eNB/gNB, useful data features and models can be learned to empower the

intelligent management and control in RAN. For example, network spatial-temporal traffic patterns, user mobility

patterns, service type/patterns along with the corresponding prediction models, network quality of service (QoS)

prediction patterns, massive MIMO parameters configuration, and more can be learned and trained based on the big

data analytics and machine learning. These well learned data features and models are undoubtedly helpful for

driving finegrained near-real-time network radio resource management in the RIC near-RT and non-real-time

optimization within RIC non-RT. The A1 interface supports communication & information exchange between

Orchestration/NMS layer containing RIC non-RT and eNB/gNB containing RIC near-RT. Key functions that the

A1 interface is expected to provide include:

Support for policy-based guidance of RIC near-RT functions/use-cases, and basic feedback mechanisms

from RIC near-RT.

Support for transmission of enrichment information from Non-RT RIC to Near-RT RIC.

⚫ The Near-real-time RIC and E2 interface

The O-RAN reference architecture provides next generation RRM with embedded intelligence, while optionally

accommodating legacy RRM. RIC near-RT is completely compatible with legacy RRM and begins by enhancing

well understood, but operational challenging functions such as per-UE controlled load-balancing, RB management,


13

interference detection and mitigation. In addition, it provides new functions leveraging embedded intelligence, such

as QoS management, connectivity management and seamless handover control. The RIC near-RT delivers a robust,

secure, and scalable platform that allows for flexible on-boarding of third-party control applications. RIC near-RT

functions leverages a database called the Radio-Network Information Base (R-NIB) which captures the near

real-time state of the underlying network via E2 and commands from RIC non-RT via A1. E2 is the interface

between the RIC near-RT and the Multi-RAT CU protocol stack and the underlying RAN DU. While the E2

interface feeds data, including various RAN measurements, to the RIC near-RT to facilitate radio resource

management, it is also the interface through which the RIC near-RT may initiate configuration commands directly

to CU/DU. While receiving AI models, policy-based guidance and enrichment information, RIC near-RT will

execute the new models based on the A1 policy and enrichment information to change the functional behavior of

thenetwork and applications the network supports.

(3) Key challenges and potential solutions

To achieve embeded RAN intelligence, there are still many challenges:

(1) it is necessary to improve the ability of network data acquisition and perception. The big volumes of data are

required by AI algorithms to attain the maximum performance. The wireless network data from different

sources has the characteristics of large amount, various types, large time scale differences and complex

distribution. How to efficiently acquire wireless network data is a key challenge to enable RAN intelligence.

To this end, it is necessary to strengthen data collection and sensing capabilities, and support data

hierarchical pre-processing and feature engineering to achieve on-demand subscription of data and

programmable custom feature engineering. As a matter of security, the data availability in AI applications

attracts enormous attention and controversy, which has been called for the policy and regislation. In addition

to the availability, mass data processing is a great challenge as well. The collection, storage, access of data,

together with the assessment of data quality and validation will lead to the incredible resource consuming.

(2) it is necessary to fully consider the integration of computing, storage and communication capabilities in the

network infrastructure. It is necessary to study distributed learning and collaborative decision-making

mechanisms, reduce signaling interactions, data backhaul and energy consumption, and improve data

computational efficiency. Implementation effciency is strongly restricted by the complexity of AI algorithms

and the processing of massive data. To have a breakthrough in these two limitations, both the performance of

the infrastructure and RAN architecture shall be considered.

(3) it is necessary to promote the openness of network control capabilities. Multi-dimensionally open control

functions and interfaces in network behavior, resources, configuration, etc., which can further enrich the use

cases and scenarios of network embedded intelligence.

(4) it is necessary to further explore the new learning mechanism of knowledge and data dual drive, and study

the AI algorithm framework and theory applicable to wireless networks. At present, AI technology is

relatively mature for common scenarios such as voice and image, but it cannot be carried out in wireless

networks. In order to further promote the development of wireless AI algorithm, the open wireless network


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data set construction is also an important research.

Key technology research for these challenges has been gradually carried out in O-RAN. For two typical use

cases of RF fingerprint based proactive load balancing and Real-time service QoE Cross-layer Guarantee, joint

research with a number of equipment manufacturers have been launched. Based on the O-RAN architecture, the

relevant algorithm scheme and interface design had been preliminarily completed, and some functions had

completed PoC verification. In addition, some further verifications via field trials are planned.

(4) Case study: smart load balancing

As the increasing frequency bands are deployed in commercial networks, a large number of inter-frequency

measurements are performed by UEs in the mobility load balancing (MLB).

The existing inter-frequency measurement method requires the UE to measure the relevant neighboring cell and

frequency according to the measurement configuration sent by the base station after accessing the base station. The

inter-frequency measurement report is initiated when the signal condition satisfies the relevant measurement event

according to the requirements in the measurement configuration message. When the base station determines that

the signal condition meets the inter-frequency measurement handover or redirection threshold, the base station

sends a handover indication message or a redirect message and starts the related process. A lot of inter-frequency

measurement may take long time. The delayed measurement causes the slow load balancing and decrease of

user-experienced data rate.

Smart radio finger print based load balancing can quickly and accurately balance the load among cells which are

distributed in different frequency layers. By collecting the related data of intra- and inter-frequncy measurement

report from historical users in the serving cell, a grid prediction model used to predict inter-frequency measurement

based on intra-frequency is constructed. For users who need to perform inter-frequency measurement, the RSRP of

inter-frequency neighbor cells can be obtained directly through the prediction model, so that the target carrier

selection, load balancing and handover decision can be made quickly, and the performance and efficiency of load

balancing can be greatly improved.

Inter-frequency measurement real-time

prediction module

Inter-frequency measurement estimation

model learning moduleData collection and preprocessing module

Data processing

Cell ID and

configuration info

Serving and

neighbor cell

measurement info Da

ta p

ro

cess

ing

an

d

ass

ocia

tio

n

Da

ta a

sso

cia

tio

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tab

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Real-time intra-frequency

measurement data

Inter-frequency measurement

estimation result

OAM

Base

station

Use

r a

rea

div

isio

n

Lea

rn

ing

mo

du

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ata

set

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mea

surem

en

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ima

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del

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del

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inin

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nit

Figure 3.2 The inter-frequency measurement estimation method

The inter-frequency measurement estimation method is shown in Figure 3.2. The specific functional modules

include: data collection and preprocessing module, inter-frequency measurement estimation model learning module,


15

and Inter-frequency measurement real-time prediction module.

The data collection and preprocessing module collects data of the base station or the network management

system, and generates a data association table through data processing and association. The data collection and

preprocessing module includes a data acquisition unit and a data processing and association unit. The

inter-frequency measurement estimation model learning module includes three functions, namely, the division of

the user, the establishment of the learning module data set of the sub-area, and the generation of the inter-frequency

measurement estimation model of the sub-area. The prediction model predicts the inter-frequency measurement in

the inter-frequency measurement real-time prediction module. The input data is obtained by the base station and the

inter-frequency measurement estimation model learning module, and the output is the inter-frequency measurement

estimation result of the target UE.

Operators and vendors have carried out laboratory test of smart radio finger print based load balancing. The

results show that the efficiency of user migration is improved by 56% and the duration of high load is reduced by

63% by optimizing the load balance based on smart radio fingerprint. In the future, the field test will be started, and

further consideration will be given to how to achieve proactive load balancing by combining the prediction of cell,

user service characteristics and user service perception.

3.4.2 Whitebox

(1) Background and concept

In the 5G era, wireless networks are facing challenges such as high data traffic and diverse application scenarios.

As a result, the requirements for R&D of wireless equipments and the construction of future networks have been

raised up to a new level. To cope with these challenges and promote a prosperous industry, the O-RAN alliance

established the “White-box Hardware” work group (WG7) to focus on the study of base station open reference

design and explore new evolutionary path of wireless equipments. By integrating demands from different partners

of the industry, the “White-box Hardware” work group would release public versions of base station reference

designs, in order to reduce the wasteful duplication in the R&D process and achieve convergence of component

selection as well as hardware specifications. There are three major aspects of white-box concept:

⚫ Open reference design: In accordance with different application scenarios and requirements, open reference

design is a set of public versions of base station designs that integrate demands from different participants of

the industry and can be used as reference by vendors in the R&D of base stations. Being common yet

representative, the open reference design also has enough flexibility to allow companies to integrate their own

expertise. With the research and public release of open reference design, wasteful duplication and resource

consumption in the R&D process can be reduced by sharing among various manufacturers.

⚫ Convergence of component: By releasing the open reference design, component selection and specifications

of the base station hardware can thus be converged to several models to avoid excessive divergence. This can

be beneficial to achieving a scale effect. The overall cost and R&D complexity can thus be greatly reduced.


16

Besides, different vendors can still modify their own hardware design to allow for enough flexibility.

⚫ Function Decoupling: Decoupling is another essential dimension for white-box. It firstly includes vertical

decoupling among different modules such as CU, DU, RRU and etc. Horizontal decoupling such as

hardware and software decoupling is also important. With decoupled functional modules, vendors from

different backgrounds can concentrate on their specialized areas without unnecessary investment in other

aspects, thus providing small vendors with easy access to the industry to achieve a prosperous ecosystem.

White-box hardware research adopts different approaches for three classes of base stations, i.e., Macro cell,

Micro Cell and Pico Cell. Due to their different scenarios and features, strategic approaches will be taken gradually

to fulfill a wireless network completely based on white-box hardware.

➢ Indoor Pico cell: Due to the high indoor data traffic and coverage requirements, indoor Pico cell is

predicted to play a key role in the 5G era and beyond. Meanwhile, indoor Pico cell market now suffers from

fragmentation in terms of design and requirements. Since the system requirements and complexity of indoor

Pico cell are comparatively low, it would make a good starting point for the research white-box hardware.

➢ Outdoor Micro cell: This type of base station is primarily used for outdoor hotspot that has high data

traffic demand. Compared with indoor Pico cell, the outdoor Micro cell shares very similar architecture. As

a result, white-box outdoor Micro cell can be based on the research of white-box Pico cell to achieve

smooth evolution.

➢ Outdoor Macro cell: Compared to the previous two base station classes, Macro cells have strict key

performance index (KPI). Therefore, it is often implemented with tight coupling between hardware and

software. At present, there are still a lot of challenges of white-box Macro cell and further evaluation is

needed.

(2) Implementation scheme and future trend

Currently, operators, especially Chinese operators, are focusing on Pico cells. The open reference design of the

Pico cell is mainly comprised of two categories. The first one is used for coverage and capacity requirements,

which is similar to the traditional indoor Pico cells. The second category is the enhanced indoor base stations with

extended functions that can be deployed with mobile edge computing (MEC) and radio intelligent controller (RIC)

on the same platform. In this case, cloud technology and virtualization will be used to achieve resource sharing,

pooling gain and unified management while the decoupling of hardware and software is thoroughly realized.

A general Pico cell architecture is given in the figure below with the following units: host unit (DU/CU), several

remote radio units (RRU) and an extension unit (fronthaul gateway).


17

DUFronthaulGateway

RRU

RRU

CU

RRU

...

Figure 3.3 Example of Pico Cell Architecture

⚫ DU implementation scheme:

There are mainly three potential DU open reference design schemes.

The first is based on general purpose processor with FPGA accelerator. The FPGA accelerator is used for

offloading the intensive operations of partial physical layer to reduce the workload of the processor. It can be

integrated into the DU with a PCIe interface. This is the most mainstream scheme for the industry at the current

stage.

The second one is to use ASIC chip to replace the FPGA accelerator from the first scheme. Compared with

FPGA, ASIC can provide high performance with moderate power consumption. However, since the ASIC

accelerator is still not fully ready yet, this scheme may be an important option in the future.

The last scheme is to implement the entire DU based on SoC chipset. Compared to the former two schemes, the

third one is competitive in terms of cost, performance, power consumption and reliability. However, its flexibility

is relatively limited.

⚫ RRU implementation scheme:

The RRU is mainly composed of several key components: digital frontend (DFE, with CFR, DPD, DDC/DUC,

partial PHY etc.), transceiver and RF chain (PA, LNA, filters, antenna etc.). At present, the most common scheme

is to implement the entire DFE in an FPGA chip. The transceiver is a highly integrated component with ADC/DAC

(Scheme 1). To achieve better performance, cost and reliability, other than further optimization of current chips,

one important improvement trend is to promote higher integration level of these components.

Therefore, one possible trend is that DFE is to be implemented in an ASIC (Scheme 2). Furthermore, the

transceiver function may be implemented into the entire FPGA-based RFSoC with ARM core integrated (Scheme

3). As a possible alternative, the DFE may be integrated into the transceiver chip to achieve good performance with

an ARM core (Scheme 4). In summary, the solutions of the RRU still require comprehensive evaluation to achieve

good balance between performance, cost, power efficiency, flexibility and reliability.


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DFE (DDC,DUC,CFR,DPD...)

PHYTransceiver

Scheme 1: FPGA

Scheme 2: ASIC

Scheme 3:FPGA RFSoC + ARM

Scheme 4:Tranceiver + ARM

Figure 3.4 Different candidate schemes of RRU implementation

At present, the first edition of white-box hardware open reference design is ready with the joint effort from

operators and partners. In the 3rd O-RAN Symposium in Shanghai in June of 2019, China Mobile, China Telecom

and China Unicom have joint released the first version of open reference designs. Field trials, test specifications

and vertical industry verifications are also being conducted with detailed plans. Starting from 2019, operators in

China have conducted field tests in different cities. The verification plan covers tests of basic functions as well as

enhanced O-RAN capabilities, including pooling gain assessment, co-platform deployment with MEC and vertical

industry application support abilities. In the future, complete O-RAN network performance optimization

verification will also be conducted based on pre-commercial prototypes. The O-RAN alliance also established the

Testing and Integration Focus Group (TIFG), which focuses on the test specifications of hardware and software.

TIFG is aiming at the verification, integration and testing of different functional components based on the O-RAN

architecture to provide guidance for the evolution of wireless equipments.

In the near future, the commercialization of white-box infrastructure would further accelerate an open ecosystem

and promote the convergence of ICDT.

3.4.3 Open interface

(1) Motivation and Overview

The open interfaces play an important role in building the O-RAN architecture, bringing about more efficient

introduction of services and more flexible deployment of networks, especially for small vendors and operators, and

enabling multiple RAN components to decouple and achieve real multi-vendor interoperability.

Besides F1, W1, E1, X2 and Xn, specified by 3GPP, O-RAN Alliance also specifies an open fronthaul interface

between O-RAN Distributed Unit (O-DU) and O-RAN Radio Unit (O-RU), and an A1 interface between

non-real-time RIC (RIC non-RT) function and the near-real-time RIC (RIC near-RT) function 错误!未找到引用

源。.

(2) Open Fronthaul Interface

As for the fronthaul networks, the open interface realizes the interoperability between O-DU and O-RU from


19

different vendors, which is focused by Work Group 4 (WG4) in O-RAN Alliance. In the second half of 2019, two

specifications for open fronthaul interface, “ORAN-WG4.CUS.0-v02.00” 错误 ! 未找到引用源。 and

“ORAN-WG4.MP.0-v02.00” 错误!未找到引用源。, have been published.

Differing from the concepts of 3GPP’s DU and RRH/TRP, O-RAN defines a functional split between DU and

RRU, which enables RRU to perform some functions of DU and evolves them into O-DU and O-RU. The

functional split, “Option 7-2x”, is selected by O-RAN to balance the following two competing interests:

⚫ Keeping an O-RU as simple as possible;

⚫ Setting the interface at a higher level to reduce the interface throughput, but to increase the complexity of

O-RU.

Based on Split Option 7-2x, O-DU is a logical node hosting RLC, MAC and High-PHY (including FEC,

encode/decode, scrambling and modulation/demodulation); O-RU is in charge of Low-PHY (FFT/iFFT, digital

beamforming, and PRACH extraction and filtering) and RF processing. Furthermore, O-RUs are categorized into

“Category A” without precoding and “Category B” with precoding. See Figure 3. for a depiction of PHY

functional split and the dual O-RU concept

Figure 3.5 PHY functional split point; Category A and Category B O-RU 错误!未找到引用源。

In case of lower layer functional split option 7-2x, the external data flows are required to exchange information

between O-DU and O-RU, including the frequency domain IQ data in User Plane (U-Plane), the real-time control

commands related to user data in Control Plane (C-Plane), the traffic between O-RU or O-DU to a synchronization

controller in Synchronization Plane (S-Plane), and non-real-time management operations in Management Plane

(M-Plane). In order to transfer C/U/S/M-Plane information between O-DU and O-RU, O-RAN inherits and extends

the eCPRI/1914.3 Radio over Ethernet (RoE) transport protocol stack, as shown in Figure 3..


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Ethernet PHY

Ethernet MAC

IP

UDP

eCPRI or

IEEE1914.3

U-Plane

VLAN (priority tag) MAC security

IP security

PTP

TCP

SyncE

C-Plane S-Plane M-Plane

NETCONF over

SSH

Figure 3.6 O-RAN MCUS-Plane protocol stack (dotted blocks are optional)

For CU-Plane, O-RAN allows for eCPRI and IEEE 1914.3 based transport headers within the Ethernet or

UDP/IP payload to further describe how the application data is to be handled in C-Plane and U-Plane, including

data flow type, sending and reception port identifiers, ability to support concatenation and sequence numbering.

Immediately following the transport headers, an application layer which includes the necessary fields for control

and synchronization in the common headers, along with multiple “sections” (maybe followed by “section

extensions”) that define the characteristics of U-Plane data to be transferred or received from a beam with one

pattern identifier. Figure 3. illustrates the C/U-Plane frame formats briefly.

As for S-Plane, frequency and time synchronization of O-DUs and O-RUs via Ethernet use Synchronization

Ethernet and IEEE 1588-2008 Precision Time Protocol (PTP). The transport of PTP can be over L2 Ethernet or

UDP/IP.

The specification of M-Plane is another highlight outcome from O-RAN WG4. O-RAN adopts NETCONF, a

network management protocol with the nice scalability and outstanding capabilities of data modeling, filtering, and

event handling, as the M-Plane protocol to realize that an O-DU manages and monitors an O-RU. The configurable

parameters are modelled as YANG modules stored in O-RUs. The protocol messages transferred are encoded into

XML. O-DUs perform the operation and control to O-RUs via Remote Procedure Call (RPC). Using the functions

provided by NETCONF, the following major functionalities to O-RUs are implemented：start-up installation and

the management for software, configuration, performance, fault and file 错误!未找到引用源。.


21

Preamble(8 Bytes)

Destination MAC

(6 Bytes)

Source MAC

(6 Bytes)

VLAN Tag

(4 Bytes)

Type/Length(Ethernet)(2 Bytes)

Payload(46…1500 Bytes)

FCS(4 Bytes)

IFG(12 Bytes)

Transport Layer Common Header(eCPRI/1914.3)

(8 Bytes)

Application Layer

Common Header Fields

（8 or 12 Bytes）

Section Fields(8 or 12 or

variable Bytes)

Section Extensions

(Variable Bytes)

Transport Layer Common Header(eCPRI/1914.3)

(8 Bytes)

Application Layer

Common Header Fields

（8 or 12 Bytes）

Section Header Fields

(6 Bytes)

PRB Fields(IQ data)

(Variable Bytes)

C-Plane

U-Plane

Figure 3.7 Frame formats of C/U-Plane massages

Besides the MCUS-Plane specifications for open fronthaul interface, O-RAN Alliance has also published the

first version of interoperability test (IOT) specification for the O-RAN compliant open fronthaul interface, which

provides a guideline for test set-up, test tools and test cases.

Although it may take a considerably long time to realize the openness of interfaces completely, O-RAN Alliance

has taken the first step by open fronthaul interface, which is a good beginning for exploring the next-generation

network architecture.

3.4.4 Cloud platform

With the gradual commercialization of 5G diversified business scenarios and the integration of services in the

OTT vertical industry, network reconstruction has become an evolution hotspot in the telecommunications field.

Telecom operators need to accelerate the process of network reconstruction, including building a more flexible

network to gain core competitiveness. After the commercial practice of “network cloudization” in recent years,

most of the mainstream operators in the world have been or are in the process of cloud platform deployment. The

"network cloudization" phase has typical technical features of COTS hardware, virtualized platform deployment,

and virtual resource cloudization. vEPC, vIMS, and IoT are typical representatives of network function

virtualization. Nowadays, on the basis of “network cloudization”, with the further pursuit of agile service

deployment, efficient resource utilization, low-cost operation and capability opening, “network cloud native” has

become a higher evolution goal of the deep network reconstruction stage. The "network cloud native" phase

requires infrastructure light-weighting, service oriented, and DevOps orchestration. Among many options,

container technology is inherently lightweight, agile, stateless, self-contained, and so on, making it a mainstream

technology and best practice that supports "network cloud-native."

Container technology is not an emerging technology. It has a wide range of mature applications in IT and cloud

services. The world's leading public cloud service providers have launched container services. The container


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industry ecosystem has matured further over the years.

⚫ The CNCF (Cloud Native Computing Foundation) community has made remarkable achievements in the

field of cloud native. Kubernetes has become the de facto standard in the field of container orchestration

and management, and the ecosystem around the cloud is further enriched;

⚫ OpenStack, ONAP, OPNFV, O-RAN and other open source communities have embraced containers. The

OCI (Open Container Initiative) specification has matured, and various container engines that adapt to

different scenarios have gained industry attention, such as Kata Container, gVisor, Containerd;

⚫ ETSI, SDN/NFV, CCSA and other industry standards organizations to promote the research of related

container topics to promote the standardization of container technology in the field of NFV.

⚫ In 2019, the container-based cloud platform also successfully landed in O-RAN. In Software Community,

O-RAN initiates a proof of concept for the RAN network function virtualization. Telecom operators and

vendors have participated in it, verifying the feasibility of deployment and operation based on the cloud

native platform，for network functions such as nRT RIC and O-CU, and conducting commercial pilots.

⚫ Container Cloud Platform

Carrier-class enhancements based on the open source container engine Docker and the container orchestration

management system Kubernetes, which meet the high-level and high-reliability requirements of carrier-class

services, and are currently the mainstream cloud platform solutions. Refer to Figure 3.8.

Figure 3.8. Typical container cloud platform architecture

⚫ Open Source

There are many open source communities that provide a variety of infrastructure for cloud platforms. For

example, Kubernetes supports multiple container runtime scheduling and provides CRI (container runtime interface)

standard interfaces; Docker is the mainstream container engine; Kata containers provide lightweight secure

container engine; Virtlet virtual machine runtime provides unified management of virtual machine applications, and

so on. Many cloud platforms use open source projects as embedded kernels, providing carrier-grade enhancements

through non-intrusive modification, providing native APIs and enhanced API interfaces.


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Although the open source community provides multiple out-of-the-box options for constructing the infrastructure

of the Cloud Platform, as a carrier-grade cloud platform, it is necessary to make a lot of functions and features

enhancements in the native container technology to achieve high Performance, high reliability, high security for

application requirements, and to provide rapid deployment, fast response, low cost and efficient integration

capabilities for market requirements.

⚫ High Performance

In order to further improve the performance based on the native container technology, the cloud platform needs

to support CPU Pinning, NUMA affinity and other features to improve the computing performance of the container

application. The cloud platform needs to develop network plug-ins under the Kuberenetes CNI framework to

support features such as SR-IOV and DPDK to improve the network performance of container applications. The

cloud platform needs to provide generic high-performance middleware (Message Queue, Load Balance, etc.) to

provide core components for container application orchestration. The cloud platform needs to support accelerated

hardware resources such as GPUs and FPGAs to further meet the performance improvement requirements of

container applications.

⚫ High Availability

Based on the container cluster framework provided by Kubernetes, the cloud platform needs to provide

container-level, node-level, component-level, system-level, multi-layered high availability solutions to achieve zero

loss of applications and systems.

⚫ Comprehensive Security

The cloud platform needs to strengthen the security of the system in multiple dimensions according to the

characteristics of the container technology.

⚫ support kernel capability mechanism and SElinux enhanced security mechanism, strictly control

container authority allocation, guarantee host security;

⚫ support namespace/control group, core isolation/binding, system-wide resource quota supervision and

other mechanisms to ensure resource isolation security;

⚫ support image digital signature Security scanning service to ensure application image security;

⚫ support multi-plane isolation of container network to ensure network isolation security;

⚫ support role-based access control system to provide unified rights management and user management

mechanism, distributed software firewall and other security components to prevent DDOS attacks,

Ensure access security;

⚫ support secure container technology to enhance virtualization security;

⚫ provide comprehensive log auditing and monitoring capabilities to meet GDPR privacy protection

requirements.

⚫ Integration and Decoupling

The cloud platform product should adopt the micro-service architecture and the DevOps concept, enabling the


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product to support more efficient component customization deployment and grayscale release to ensure efficient

integration of the solution. Carrier-class cloud platform products should provide OpenStack and Kubernetes

dual-core engines to fully share the data center infrastructure and management systems to provide users with an

efficient cloud network convergence solution. At the same time, cloud platform products should also support the

deployment scenarios of bare metal, third-party IaaS cloud, public cloud (AWS, Alicloud, etc.), realize

layer-by-layer decoupling of the whole system, and ensure that the manufacturer and technology stack are no

lock-in. Refer to Figure 3.9.

Figure 3.9. Layered decoupled cloud platform products

3.4.4.1 Hierarchical Cloud Architecture

The requirements for 5G networks aim to reduce latency down to a single millisecond to support tactile Internet

applications, which are characterized by real-time interaction between humans and machines. Such services are

currently not possible via today’s centralized cloud architectures. But the combination of ultra-low latency and 5G

speeds will enable remote surgeries, new levels of industrial automation, connected vehicle applications and even

autonomous vehicles whether they are drones, cars or trucks. In industrial settings, edge cloud deployments will

improve the operation of control systems in manufacturing and energy applications as well as enable better patient

monitoring in the healthcare sector. So the hierarchy cloud architecture is suitable for the 5G deployment scenarios.

The regional could, edge cloud and cell site are mapped into different network functions of 5G such as the CU, DU,

RU and other components.

Regional Cloud is a location that supports virtualized RAN functions for many cell sites in multiple Edge Clouds,

and provides high centralization of functionality.

Edge Cloud is a location that supports virtualized RAN functions for multiple cell sites, and provides


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centralization of functions for those sites and associated economies of scale. An Edge Cloud might serve a large

geographical area or a relatively small one close to its cell sites, depending on the Operator’s use case. However,

the sites served by the Edge Cloud must be near enough to the O-RUs to meet the delay requirements of the O-DU

functions.

Cell site refers to the location of Radio Units (RUs). A cell site in general will support multiple sectors and

hence multiple O-RUs.

To implement the low latency system, there are different technologies to implement including the real time

feature such as pre-emptive schedule Linux kernel, CPU affinity and isolation, huge pages related features, NUMA

awareness, and others. All these features could be combined together to implement the low latency cloud platform.

As the reference implementation, the OpenStack and Kubernetes with necessary enhancements and plugins could

meet the requirement.

Furthermore the Fronthaul connection between the O-RU/RU and O-DU requires high performance and low

latency. Typically, the SR-IOV networking interface is used for this. If only one container needs to use the

networking interface, the PCI pass-through network interface can provide high performance and low latency

without using a virtual switch.

High-performance E-W data plane throughput is a requirement for the implementation of the different near-RT

RIC, O-CU, and O-DU scenarios. One of commonly used options for E-W high-performance data plane is the use

of a virtual switch which provides basic communication capability for instances deployed at either the same

machine or different machines. It provides L2 and L3 network functions.

To get the high performance required, one of the options is to use a Data Plan Development Kit (DPDK)-based

virtual switch. Using this method, the packets will not go into Linux kernel space networking, and instead will

implement userspace networking which will improve the throughput and latency. To support this, the container or

VM instance will need to use DPDK to accelerate packet handling.

From Regional and Edge cloud perspective, the distributed cloud architecture should be implemented to meet the

requirements. The following aspects should be fulfilled.

• Centralized management of edge cloud infrastructure and workloads

Large-scale deployments of geographically dispersed edge clouds simply cannot be managed manually. It is

essential to centrally manage the configuration and status of the edge cloud infrastructure to save time and

minimize operational costs. All the components of edge cloud infrastructure need to be configured for how the

cloud will be used and what resources will be made available to users. This includes setting user login parameters,

establishing the physical nodes that the cloud software will run on, determining what software will be running and

what software images will be available to install for the applications, and configuring the storage clusters. With

centralized management tools and APIs, administrators can configure the infrastructure once and synchronize the

configuration across the distributed edge clouds. Configuration updates made on the system controller can also be

automatically applied to all edge clouds. Synchronizing the configuration data prevents administrators from having


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to configure each edge cloud separately, which can be error prone, with the same tasks (errors) potentially repeated

thousands of times depending on the size of deployment. In addition to configuring the infrastructure, the status of

the edge cloud infrastructure also needs to be managed centrally so that administrators can easily monitor the health

of the entire system as well as individual edge clouds. The system controller at the central site needs to aggregate

fault and telemetry data from all the edge clouds, including fault alarms, logs and telemetry statistics. The user

workloads running on the distributed edge clouds also need to be centrally managed. This allows users to launch

applications on VMs or containers from different edge cloud sites when needed. It also allows VMs to be migrated

from one edge cloud to another. Being able to centrally manage the edge cloud workloads also assists in fault

scenarios across edge sites and disaster recovery efforts. Software updates can be challenging in distributed cloud

environments. To make software updates easier and faster, it is necessary to orchestrate software patching across

the entire system to ensure bug fixes and new features are applied correctly on each edge cloud. Once the software

update has been applied to the system controller at the central site, the update should be automatically applied

across each node of every edge cloud. During the update process, it is also important that VMs are automatically

migrated to ensure network uptime

• Single pane of glass provides system-wide view

Centralized management capabilities must be supported by a single pane of glass view. System administrators

need a simple way to see everything that’s going on across their entire distributed edge cloud deployment, from

infrastructure data synchronization to connectivity and overall health status to software updates, without having to

access multiple different interfaces and correlate the information.

• Massive scalability is a must

The distributed edge cloud architecture provides unprecedented flexibility for network operators to deploy cloud

resources where they are needed most, to optimize existing services or support new applications. To ensure

deployment flexibility, a distributed edge cloud solution must be highly scalable to support any size of deployment.

The solution needs to be able to scale seamlessly to tens or hundreds of thousands of distributed edge clouds in

geographically dispersed locations. The edge clouds themselves need to be scalable from a single node to thousands

of nodes.

• Edge cloud autonomy

In many cases it’s critical that edge clouds are completely autonomous. If connectivity is lost between the central

site and an edge cloud site, the edge cloud still needs to perform its mission critical operations and users still need

to be able to access to the edge cloud. This is a possible scenario if, for example, an edge cloud is located where

mobile or satellite network coverage is patchy. But if the infrastructure and workload data is synchronized across

all the edge sites, then users will still be able to access their services and the edge cloud will function independently

until connectivity is restored.

• Zero touch provisioning

Installation and commissioning at the edge sites need to be as simple as possible. Beyond the physical server


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installation and power-on at the edge site, the remaining installation and commissioning tasks must be as automated

as possible, reducing the need for human interaction. From that point, the administrator back at the central site

should be able to bring up the cloud environment on the nodes at the edge sites with just one button click.

How close is the industry to meeting these requirements for distributed edge clouds? As noted above, many

initiatives at open source and industry standards groups are tackling various aspects of edge computing for network

operators. Among these efforts, the OpenStack Foundation’s StarlingX project is notable for its work on distributed

edge cloud manageability and contribution to other open source projects to broaden community engagement and

widen industry support. As part of OpenStack’s Edge Computing group, StarlingX provides a deployment-ready,

scalable, highly reliable edge infrastructure software platform.

With edge environments being unique in almost every case, distributed clouds will need to support a variety of

heterogenous deployments such as the CU as VM and DU as Container. So the centralized management system

should have the capability to manage VM based environment and Container based environment at the same time.

3.4.5 Open Source O-RAN

The O-RAN Alliance and the Linux Foundation launched active and in-depth cooperation and announced the

establishment of the O-RAN Software Community (O-RAN SC) in April 2019, the first open source project for

open and intelligent RAN. The O-RAN SC aims to provide reference implementation based on the O-RAN

architecture and standards, help the industry to reduce the cost of R&D and bring more benefits to

telecommunication enterprises. Meanwhile, in order to satisfy various intellectual property protection requirements,

the O-RAN SC provides two types of projects. One is to adopt the Apache 2.0 license and the other, called O-RAN

specification-code project, is using the O-RAN Software License. The Apache is the traditional license for open

source contributions. The O-RAN License supports software that addresses RAN essential licensing and supports

fair, reasonable, and non-discriminatory (FRAND) terms.

The O-RAN SC adopts a hierarchical organizational structure. The O-RAN SC Technical Oversight Committee

(TOC) is responsible for the overall technology realization and evolution, under which there are a dozen of projects

of NRTRIC, RIC Applications, RIC, OCU, ODUHIGH, ODULOW, ORU, OAM, SIM, INF, INT and the DOC. As

an open source project across the RAN and artificial intelligence fields, the O-RAN SC will work closely with

other open source projects such as ACUMOS, ONAP, OPNFV, etc., to build a complete RAN solution from the

RAN intelligent application model, RAN intelligent controller to the intelligent management.

4 Summary

In 2015, FuTURE FORUM published two landmark White Papers, proposing that the 5G networks

should feature “soft” and “green”. The White Paper (WP) is a continuity of previous White Papers,


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reflecting the continuous thinking, progresss and evolvement on the 5G network design philophy,

which makes itself the programmatic document for other WPs published in the same series. In this

WP, it is pointed out that the design of future wireless networks is deemed to a journey of

convergence of IT, CT and DT technologies.

The white paper gives an introduction of future 5G networks based on ICDT convergence.

Essentially, the ICDT convergence-based future RAN features two points.

• Open X. There are multiple-fold meanings, including open architecture, open interface, open

source and open hardware reference design. Open interfaces are essential to enable smaller

vendors and operators to quickly introduce their own services, or enable operators to customize

the network to suit their own unique needs. It also enables multi-vendor deployments, enabling

a more competitive and vibrant supplier ecosystem. While open source software enables faster

product development and encourages innovation, opening the reference design for hardware

could further reduces the R&D cost and the entry threshold for small vendors, which further

spurs the innovation throughout the industry and bring prosperity to the ecosystem.

• Embedded RAN intelligence. The basic idea is to endow the RAN with intelligence with the

introduction of ML/AI techniques, which could not only help optimize and simply the network

management and orchestration, but also help improve system performance optimization.

5 Reference

[1] GSMA Intelligence, “Understanding 5G: Perspectives on future technological advancements in mobile,” white

paper, 2014.

[2] IMT-2020 (5G) promotion group (PG), “5G vision and requirements,” May, 2014, available:

http://www.imt-2020.org.cn/zh/documents

[3] C.-L. I, C. Rowell, S. Han, Z. Xu, G. Li, and Z. Pan, “Towards green & soft: A 5G perspective,” IEEE

Commun. Mag., vol. 52, no. 2, pp.66-73, Feb. 2014.

[4] S. Chen and J. Zhao, “The requirements, challenges, and technologies for 5G of terrestrial mobile

telecommunication,” IEEE Commun. Mag, vol. 52, no. 5, pp: 36-43, May 2014.

[5] FuTURE FORUM, “5G: rethinking over telecommunication beyond 2020 Version 2”, 2015.

[6] FuTURE FORUM, “5G: rethinking over telecommunication beyond 2020 Version 1”, 2014.

[7] China Mobile Reserch Institute, “C-RAN: the road towards green radio access networks”, 2012

[8] C. Coletti, W. Diego, R. Duan, et al. “O-RAN: Towards an Open and Smart RAN (white paper)”, O-RAN

Alliance, Oct 2018.

[9] Massimo C , Toktam M . Softwarization and virtualization in 5G mobile networks: benefits, trends and


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challenges[J]. Computer Networks, 2018.

[10] Kan Z, Zhe Y, Zhang K, et al. Big data-driven optimization for mobile networks toward 5G[J]. IEEE Network,

2016, 30(1):44-51.

[11] Shafin, Rubayet, Liu, Lingjia, Chandrasekhar, Vikram, et al. Artificial Intelligence-Enabled Cellular Networks:

A Critical Path to Beyond-5G and 6G [J]. 2019.

[12] ORAN-WG4.CUS.0-v02 “Control, User and Synchronization Plane Specification”, O-RAN Alliance, Working

Group 4, July 2019.

[13] ORAN-WG4.MP.0-v02 “Management Plane Specification”, O-RAN Alliance, Working Group 4, July 2019.

Acknowledgement

Grateful thanks to the following leaders and contributors for their wonderful work on this whitepaper:

Leaders:

China Mobile: Chih-Lin I, Jinri Huang, Qi Sun, Boxiao Han, Yuxuan Xie, Xin Su, Weichen Ni, and Jie Wu

Contributors:

Intel: Jianfeng Wang, Xu Zhang

China Unicom: Rong Huang, Shan Liu

ZTE: Dong Chen

DT Mobile: Haichao Qin, Hao Wang, Yuanfang Huang

Abbreviation

3GPP the 3rd Generation Partner Project

5G the Fifth-Generation mobile communications

AI Artificial intelligence

CU Central Unit

DU Distributed Unit

MIMO Multiple-Input Multiple Output

NFV Network Function Virtualization

SDN Software Defined Network


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QoS Quality of Service

CBD Central Business District

MR Measurement Report

CQI Channel Quality Indication

SINR Signal to Interference plus Noise Ratio

Pb PetaByte

API Application Programming Interface

COTS Commercial Off-The-Shelf

EPC Evolved Packet Core

IMS IP Multimedia Subsystem

NFV Network Function Virtualization

RAN Radio Access Network

SDN Software Defined Network

VNF Virtualized Network Functions

VPN Virtual Private Network

VIM Virtual Infrastructure Manager

CNCF Cloud Native Computing Foundation

OCI Open Container Initiative

CRI Container Runtime Interface

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