ITU-T SG13 Non Radio Hot Topics and Recommendations related to IMT 2020/5G

IMT 2020 Related Hot Topics for ITU-T SG13:

[As per the SG13  4-14 March 2019 meeting in Victoria Falls, Zimbabwe]

1.    Intelligence for network automation, augmentation and amplification [TSAG TD160]

  • Identify the standardization needs for intelligence in 5G systems and the telecommunications sector.
  • Automatic detection and resolution of anomalies and other incidents of inefficiency, as well as predictive maintenance will reduce the operational expenditure of network operators and service providers
  • Address the architecture, interfaces, functional entities, service scenarios and protocols required for intelligence retrieval and actuation, and the performance bench marking and certification of AI techniques

Related Work items:

Y.sfes: Smart Farming Education Service based on u-learning environment

Y.qos-ml-arc: Architecture of machine learning based QoS assurance for IMT-2020 network

Y.MecTA-ML: Mechanism of traffic awareness for application-descriptor-agnostic traffic based on machine learning

Y.MLaaS-reqts: Cloud computing – Functional requirements for machine learning as a service

Y.IMT2020-ML-arch: Architectural framework for machine learning in future networks including IMT-2020.  This is now  ITU Y.3172SEE 20 AUG 2019 UPDATE BELOW.

2.  Realizing 5G/ IMT-2020 vision [TSAG TD101, TSAG TD160, TSAG C27-R2, TSAG C29]

  • Unified access-independent network management
  • Standardization roadmap on IMT-2020
  • ICN (Information Centric Networks) with scalability, mobility and security
  • Open-source software and standards for 5G
  • Software-based networking functions to optimize a per-session based performance
  • Emerging fronthaul and midhaul technologies to support the 5G deployment
  • Large-bandwidth backhaul and fronthaul solutions
  • Concrete strategies for the migration from 4G to 5G systems.
  • End-to-end network orchestration, control and management
  • Service-based network architecture
  • Open service management APIs for the Internet of Things
  • Electromagnetic field (EMF) studies around 5G beam-forming capabilities
  • Interoperability of services supporting public safety.

Related Work items:

Y.NGNe-O-arch: Functional architecture of orchestration in NGNe

Y.IMT2020-qos-fa: QoS functional architecture for IMT-2020 networks

Y.IMT2020-qos-req; QoS requirements for IMT-2020 network

Y.qos-ml-arch: Architecture of machine learning based QoS assurance for IMT-2020 network

Y.IMT2020.qos-mon: IMT-2020 network QoS monitoring architectural framework

Y.IMT2020-CEF: Network capability exposure function in IMT-2020 networks

Y.3MO: Requirements and Architectural Framework of Multi-layer, Multi-Domain, Multi-Technology Orchestration

Y.IMT2020-ADPP: Advanced Data Plane Programmability for IMT-2020 (renamed- see below)

Y.NetSoft-SSSDN: High level architectural model of network slice support for IMT-2020 – Part: SDN (renamed- see below)

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IMT 2020 non radio recommendations developed by ITU-T SG13:

Y.3112: Framework for the support of network slicing in the IMT-2020 network (Revised)

Draft  Recommendation ITU-T Y.IMT2020-NSAA-reqts: “Requirements for network slicing with AI-assisted analysis in IMT-2020 networks”

Draft Recommendation ITU-T Y.IMT2020-CEF: “Network capability exposure function in the IMT-2020 networks”

Draft Recommendation ITU-T Y.qos-ec-vr-req: ” QoS requirements and architecture for virtual reality delivery using edge computing in IMT-2020″ 

Draft Recommendation ITU-T Y.3072 (formerly Y.ICN-ReqN): “Requirements and Capabilities of Name Mapping and Resolution for Information Centric Networking in IMT-2020” 

Draft Recommendation ITU-T Y.3151 (formerly Y.NetSoft-SSSDN): “High level architectural model of network slice support for IMT-2020 – part: SDN”

Draft Recommendation ITU-T Y.3152(formerly Y.IMT2020-ADPP): “Advanced Data Plane Programmability for IMT-2020”

Draft Recommendation ITU-T Y.3172 (formerly Y.IMT2020-ML-Arch): “Architectural framework for machine learning in future networks including IMT-2020

Draft Recommendation ITU-T Y.3106 (formerly Y.IMT2020-qos-req): “QoS functional requirements for the IMT-2020 network”

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ITU-T SG13/WP1 work related to IMT-2020:

Question (Co-) Rapporteur

(Associate Rapporteur)

Title
Q6/13 Taesang CHOI (Korea) Quality of service (QoS) aspects including IMT-2020 networks
Guosheng ZHU (China)
Q20/13 Nam Seok KO (Korea) IMT-2020: Network requirements and functional architecture
Marco CARUGI (Huawei, China)
Q21/13 Kazunori TANIKAWA (Japan)

Yushuang HU (China)

Network softwarization including software-defined networking, network slicing and orchestration
Sangwoo KANG (Korea)
Q22/13 Jiguang CAO (China)

Ved P. KAFLE (Japan)

Upcoming network technologies for IMT-2020 and Future Networks
Q23/13 Jeong Yun KIM (Korea)

Nauxiang Shi (China)

Fixed-Mobile Convergence including IMT-2020

 

Question 21 of ITU-T SG13 is studying network softwarization including: network slicing, SDN, and orchestration which are highly expected to contribute to IMT-2020.  Question 21 met during the SG13 meeting, from 4 to 14 March 2019 at Victoria Falls, Zimbabwe under the chairmanship of co-Rapporteur Ms.Yushuang Hu (China Mobile, China) and Mr. Kazunori TANIKAWA (NEC, Japan).

On March 14, 2019, ITU-T SG13 has consented to two new Recommendations:

  1. ITU-T Y.IMT2020-ML-Arch “Architectural framework for machine learning in future networks including IMT-2020” (Ref. SG13-TD355/WP1)
  2. ITU-T Y.3115 (formerly Y.NetSoft-SSSDN). It describes SDN control interfaces for network slicing, which especially focuses on the control of front haul networks such as PON.

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20 August 2019 Update:  New ITU standard has established a basis for the cost-effective integration of Machine Learning into 5G and future networks.

The standard – ITU Y.3172 –  describes an architectural framework for networks to accommodate current as well as future use cases of Machine Learning.  “Machine Learning will change the way we operate and optimize networks,” says Slawomir Stanczak, Chairman of the ITU-T Focus Group on ‘Machine Learning for Future Networks including 5G’.  ITU Y.3172 is under the responsibility of the Focus Group’s parent group, ITU-T Study Group 13 (Future networks and cloud).

“Every company in the networking business is investigating the introduction of Machine Learning, with a view to optimizing network operations, increasing energy efficiency and curtailing the costs of operating a network,” says Stanczak. “This ITU Y.3172 architectural framework provides a common point of reference to improve industry’s orientation when it comes to the introduction of Machine Learning into mobile networks.”

Machine Learning holds great promise to enhance network management and orchestration.  Drawing insight from network-generated data, Machine Learning can yield predictions to support the optimization of network operations and maintenance.  This optimization is becoming increasingly challenging, and increasingly important, as networks gain in complexity to support the coexistence of a diverse range of information and communication technology (ICT) services.

Network operators aim to fuel Machine Learning models with data correlated from multiple technologies and levels of the network.  They are calling for deployment mechanisms able to ‘future-proof’ their investments in Machine Learning. And they are in need of interfaces to transfer data and trained Machine Learning models across Machine Learning functionalities at multiple levels of the network.

The ITU Y.3172 architectural framework is designed to meet these requirements.  The standard includes a unique focus on the future.

“ITU Y.3172 provides for the declarative specification of Machine Learning applications, making it the first mechanism to meet industry’s need for a standard method of including future use cases,” says Vishnu Ram, the lead editor of the standard.

“This is the first time that a Study Group has approved a Focus Group deliverable as an ITU standard before the conclusion of the Focus Group’s lifetime,” says Leo Lehmann, Chairman of ITU-T Study Group 13. This represents an important achievement in ITU’s work to expedite the transition from exploratory studies to the agreement of new ITU standards.

ITU-T Focus Groups are open to all interested parties. These groups accelerate ITU studies in fields of growing strategic relevance to ITU membership, delivering base documents to inform related standardization work in membership-driven ITU-T Study Groups.

“I would like to commend the many experts participating in both the Focus Group and ITU-T Study Group 13,” says Lehmann. “This early approval required a considerable amount of planning and extremely close collaboration, which could only have been achieved with dual participation and common interest.”

How the standard works:

The standard offers a common vocabulary and nomenclature for Machine Learning functionalities and their relationships with ICT networks, providing for ‘Machine Learning Overlays’ to underlying technology-specific networks such as 5G networks. It describes a ‘loosely coupled’ integration of Machine Learning and 5G functionalities, minimizing their interdependencies to account for their parallel evolution.

The components of the architectural framework include ‘Machine Learning Pipelines’ – sets of logical nodes combined to form a Machine Learning application – as well as a ‘Machine Learning Function Orchestrator’ to manage and orchestrate the nodes of these pipelines.

‘Machine Learning Sandboxes’ are another key component of the framework, offering isolated environments hosting separate Machine learning pipelines to train, test and evaluate Machine Learning applications before deploying them in a live network.

“This combination of an architectural framework for Machine Learning and this declarative language to specify new use cases will give network operators complete power over the extension of Machine Learning to new use cases, the deployment and management of Machine Learning in the network, and the correlation of data from sources at multiple levels of the network,” says Ram.

The ITU Y.3172 architectural framework is the first of a nascent series of ITU standards addressing Machine Learning’s contribution to networking.

“A range of ITU standards under development will complement and complete the architectural framework described by ITU Y.3172,” says Ram. “Collectively these standards will provide a full toolkit to build Machine Learning into our networks.”

Two draft ITU standards will propose mechanisms for data handling and specify the design of the ‘Machine Learning Function Orchestrator’.

“If data is the blood flowing through the heart that is Machine Learning, this function orchestrator can be considered the brain,” says Ram.

Another standard will support the assessment of intelligence levels across different parts of the network.

“Different parts of the network will be supplied by different vendors,” says Ram. “We are developing a standard way for different parties to look the intelligence level of the network, helping operators to evaluate vendors and regulatory authorities to evaluate the network.”

The series of ITU standards will be completed by a standard supporting the interoperability of Machine Learning marketplaces, marketplaces hosting repositories of Machine Learning models.

“The standard would assist potential adopters both in selecting a Machine Learning model capable of addressing their specific needs and in integrating the model into the network,” says Ram.

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The Focus Group’s next meeting is scheduled for 5-8 November 2019 in Berlin, Germany.

Join the group’s mailing list, request access to documents and sign-up to a working group on the homepage of the ITU Focus Group on Machine Learning for Future Networks including 5G.

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I believe the following ITU-T Technical Report was developed by ITU-T SG15:

Technical Report (GSTR-TN5G) on “Transport network support of IMT-2020/5G”

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Previous Techblog post on this topic:

New ITU-T Standards for IMT 2020 (5G) + 3GPP Core Network Systems Architecture

 

 

5 thoughts on “ITU-T SG13 Non Radio Hot Topics and Recommendations related to IMT 2020/5G

  1. Draft Recommendation ITU-T IMT2020-NSAA-reqts: Requirements for network slicing with AI-assisted analysis in IMT-2020 networks

    1. Scope:
    The Draft Recommendation describes the requirements for supporting AI-assisted analysis of network slice in IMT-2020 networks, which supports the AI-assisted analysis of network slice deployment and management.
    2. References
    The following ITU-T Recommendations and other references contain provisions which, through reference in this text, constitute provisions of this Recommendation. At the time of publication, the editions indicated were valid. All Recommendations and other references are subject to revision; users of this Recommendation are therefore encouraged to investigate the possibility of applying the most recent edition of the Recommendations and other references listed below. A list of the currently valid ITU-T Recommendations is regularly published. The reference to a document within this Recommendation does not give it, as a stand-alone document, the status of a Recommendation.
    [ITU-T X.yyy] Recommendation ITU-T X.yyy (date), Title.
    [ITU-T Y.3100] Recommendation ITU-T Y.3100 Corrigendum 1 (2018), Terms and definitions for IMT-2020 network.
    [TU-T Y.NetSoft-SSSDN] Draft Recommendation ITU-T Y.NetSoft-SSSDN (2019), High level technical charactristics of network slice support for IMT-2020 – part: SDN

    3 Definitions

    3.1 Terms defined elsewhere

    This Recommendation uses the following terms defined elsewhere:
    3.1.1 network slice [ITU-T Y.3100]: A logical network that provides specific network capabilities and network characteristics.
    NOTE 1 – Network slices enable the creation of customized networks to provide flexible solutions for different market scenarios which have diverse requirements, with respect to functionalities, performance and resource allocation.
    NOTE 2 – A network slice may have the ability to expose its capabilities.
    NOTE 3 – The behaviour of a network slice is realized via network slice instance(s).
    3.1.2 mobile network [Q.1762/Y.2802]: A network that provides wireless access to its services and supports mobility.optional quoted definition>.
    3.2 Terms defined in this Recommendation
    This Recommendation defines the following terms:
    3.2.1 :

    4 Abbreviations and acronyms:
    This Recommendation uses the following abbreviations and acronyms:

    5. Conventions used in this Recommendation:
    The keywords “is required to” indicate a requirement which must be strictly followed and from which no deviation is permitted, if conformance to this Recommendation is to be claimed.
    The keywords “is recommended” indicate a requirement which is recommended but which is not absolutely required. Thus, this requirement need not be present to claim conformance.
    The keywords “can optionally” indicate an optional requirement which is permissible, without implying any sense of being recommended. This term is not intended to imply that the vendor’s implementation must provide the option, and the feature can be optionally enabled by the network operator/service provider. Rather, it means the vendor may optionally provide the feature and still claim conformance with this Recommendation.

    6. Overview of network slicing with AI-assisted analysis in IMT-2020 networks:
    – Slice quality of experience (QoE) calculation: training business mean opinion score (MOS) [b-ITU-T P.800] model based on service MOS and network data, calculating slice service MOS
    – Network could take into account slice sevice level agreement (SLA) fulfilment to schedule new slice Resource within the resource configured by Management system.
    – Management could do resource Adjustment:based on slice QoE to adjust resource configuration

    7. Requirements for network slicing with AI-assisted analysis in IMT-2020 networks
    7.1 AI-assisted analysis for network slice’s requirement mapping:
    provide intelligent customer service and slice guidance to network slice customers to help them select and generate optimal and optimal customized network slice services.

    7.2 AI-assisted analysis for network slice’s deployment:
    By using AI to analyze network data, combined with the management of virtualized network resources, output slice management policy rules or slice optimization deployment templates.

    7.3 AI-assisted analysis for network slice’s scheduling management:
    By using AI to analyze network data, output the slice management strategy and realize the slice self-healing and self-optimization.

  2. The following table provides the schedule of the planned major deliverables of ITU-R WP 5D:
    Date Meeting Anticipated Milestones

    July 2019 Brazil WP 5D #32
    • Finalize draft new Report ITU-R M.[IMT.1 452-1 492 MHz]
    • Consider further work regarding new Recommendation ITU-R M.[MT.3300 MHz FSS]
    • Progress draft new Report ITU-R M.[IMT.EXPERIENCES]
    • Finalize revision of Recommendation ITU-R M.1036
    • Finalize draft new Report ITU-R M.[IMT.MS/MSS.2GHz]
    • Update/Finalize draft new Report/Recommendation ITU-R M.[IMT.1518 MHz COEXISTENCE]
    • Finalize draft new Report/Recommendation ITU-R M.[IMT.3300 MHz RLS]
    • Finalize Doc. IMT-2020/YYY Input Submissions Summary
    • Finalize revision of Recommendation M.2012
    • Finalize Addendum 4 to Circular Letter IMT 2020
    • Finalize draft new Question ITU-R [IMT.Specific industrial applications]/5
    • Finalize draft revisions to existing ITU-R Resolutions, Questions and Opinion

    December 2019 Geneva WP 5D #33 (max 4 day meeting)
    • Focus meeting on evaluation – review of external activities in Independent Evaluation groups through interim evaluation reports
    • Workshop on evaluation of IMT-2020 terrestrial radio interfaces
    February 2020 [TBD] WP 5D #34 • Finalize Doc. IMT-2020/ZZZ Evaluation Reports Summary
    • Finalize Doc. IMT-2020/VVV Process and use of GCS
    • Finalize Addendum 5 to Circular Letter IMT 2020
    • Finalize draft new Report M.[IMT.AAS]
    • Finalize draft new Report ITU-R M.[HAPS-IMT]

    June 2020 [TBD] WP 5D #35
    • Finalize draft new Report ITU-R M.[IMT-2020.OUTCOME]
    • Finalize Addendum 6 to Circular Letter IMT 2020

    October 2020 [TBD] WP 5D #36
    • Finalize draft new Recommendation ITU-R M.[IMT 2020.SPECS]
    • Finalize Addendum 7 to Circular Letter IMT 2020

  3. From the ITU-R 5D Chairman’s report (18 March 2019) of their 11-15 February 2019, Geneva, Switzerland meeting:

    IMT-2020/VVV
    This meeting reviewed the received a number of input contributions on “Process and use of the Global Core Specification (GCS), references, and related certifications in conjunction with Recommendation ITU-R M.[IMT 2020.SPECS]” and there continues to be two diverse philosophies on how to proceed with the document – one desiring to significantly alter the process to support specific national needs in the transposition phase of the process and the other demonstrating how the same objective could be accomplished with the existing process remaining unaltered in its scope, steps, and procedures.

    In the WP 5D Plenary, one Administration requested that for clarity and transparency, and to assist further discussions, that the two alternate views provided on the SharePoint during the course of the 31 bis meeting (based on contributions to meetings Nos. 31 and 31 bis) be referenced in the Chairman’s Report. The two views are Indian Way Forward (provided by TSDSI) and Summary of a Proposed IMT-2020 VVV Way Forward (AT&T v3 2-14-19) (provided by AT&T).
    ………………………………………………………………….
    Doc. IMT-2020/YYY Input Submissions Summary

    Based on the input contributions, the working document towards an acknowledgment template was further developed. This acknowledgement is to be developed for each candidate technology submission that ITU-R WP 5D determines is a “complete” submission in Step 3. This template also contains a checklist which could assist in determination of “completeness. This document will need to be completed at Meeting #32 to capture the final RIT/SRIT submissions submitted from the proponents.
    …………………………………………………………………………………………..
    ITU-R Working Party 5D Workshop on IMT-2020 Terrestrial Radio Interfaces Evaluation

    WP 5D will hold a workshop on IMT-2020 focussing on the evaluation of the candidate terrestrial radio interfaces in conjunction with the 33rd meeting in December 2019 (shifted from the prior plan of holding this at Meeting #32), in which interim evaluation reports are expected. This will facilitate the possibility on the IEGs to understand the details of the proposed candidate technologies, and to interact with WP 5D and other IEGs participating in the ITU-R evaluation process on IMT 2020. This workshop is a continuation of the previous workshop on IMT-2020 held in 2017, Munich, which addressed the process, requirements, and evaluation criteria for IMT-2020 as well as views from proponents on the developments on IMT-2020 radio interface(s) and IEGs activities.

  4. by Marco Carugi, ITU Expert for many years

    Network softwarization [Y.3100]: Overall approach for designing, implementing, deploying, managing and
    maintaining network equipment and/or network components by software programming
    Various drivers of Network softwarization:
    o cheap HW performance, powerful terminals and things
    o Open Source SW availability
    o actionable Big Data and AI/ML advances
    Network softwarization is paving the way towards X-as-a-Service
    o SDN Controllers, Virtual Network Functions and End Users’ apps as “services” Network functions become flexible
    o New components can be instantiated on demand (e.g. dedicated network dynamic setup)
    o Components may change location or size (e.g. deployment at edge nodes, resource reallocation)
    o Communication paths may change (e.g. service aware networking, chained user plane functions)
    o “Network services” are provisioned by using network functions instantiated at the right time and right location
    o Enabling network/service architectures (re-)design, cost and process optimization, self-management
    o Network programability but also increased complexity [impact on network management]

    Softwarization is embedded across all network layers by leveraging SDN, NFV, Edge and Cloud Computing
    See also ITU-T Y.3150

    https://www.itu.int/en/ITU-D/Regional-Presence/ArabStates/Documents/events/2019/ET/S1-%20ITU%20Reg%20Forum-Tunis-5G%20IMT2020-presentation-Marco-Carugi-v1.pdf

  5. The ITU-T Joint Coordination Activity for IMT2020 (JCA IMT2020) is preparing, progressing and maintaining a roadmap to support IMT-2020 standardization coordination. IMT-2020 is an important topic for our industry, and many standardization-related activities are held in various entities.

    JCA-IMT2020 will keep updating this roadmap which may be viewed at: https://www.itu.int/en/ITU-T/jca/imt2020/Pages/default.aspx

    Mr Scott Mansfield (Ericsson Canada) was appointed as the Chairman and Ms Ying Cheng (China Unicom) as the Vice-chairman of JCA-IMT2020. JCA-IMT2020 reports to the SG13 as its parent group.

    JCA-IMT2020 will have its next meeting in Geneva, on 17 October 2019 (18:00 – 19:30) during the ITU-T Study Group 13 meeting 14 – 25 October 2019.

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