Presentations from 2nd IEEE Open RAN Summit

The second IEEE SA (Standards Association) Open RAN summit, hosted by the Johns Hopkins University Applied Physics Lab, took place on 9-10 Aug 2023. It covered the topics related to the standardization of Open RAN including O-RAN Alliance, 3GPP, IEEE, various deployment scenarios, testing and integration, Open RAN security, RAN slicing, and RAN optimization among others. 

The videos of the presentations can be viewed on the summit page here or though the video playlist here.

The talk from Dr. Chih-Lin I, O-RAN Alliance TSC Co-Chair and CMCC Chief Scientist, Wireless Technologies on 'AI/ML impact, from 5.5G to 6G' is embedded below:

Related Posts: 

• The 3G4G Blog: Open RAN Terminology and Players
• The 3G4G Blog: O-RAN Introduction for Beginners
• The 3G4G Blog: Accelerating O-RAN Adoption Through Open Source
• The 3G4G Blog: 5G RAN Functional Splits
• The 3G4G Blog: Open RAN Explanation, Videos, White papers and Other Resources 

AI/ML Enhancements in 5G-Advanced for Intelligent Network Automation

Artificial Intelligence (AI) and Machine Learning (ML) has been touted to automate the network and simplify the identification and debug of issues that will arise with increasing network complexity. For this reason 3GPP has many different features that are already present in Release-17 but are expected to evolve further in Release-18. 

I have already covered some of this topics in earlier posts. Ericsson's recent whitepaper '5G Advanced: Evolution towards 6G' also has a good summary on this topic. Here is an extract from that:

Intelligent network automation

With increasing complexity in network design, for example, many different deployment and usage options, conventional approaches will not be able to provide swift solutions in many cases. It is well understood that manually reconfiguring cellular communications systems could be inefficient and costly.

Artificial intelligence (AI) and machine learning (ML) have the capability to solve complex and unstructured network problems by using a large amount of data collected from wireless networks. Thus, there has been a lot of attention lately on utilizing AI/ML-based solutions to improve network performance and hence providing avenues for inserting intelligence in network operations.

AI model design, optimization, and life-cycle management rely heavily on data. A wireless network can collect a large amount of data as part of its normal operations. This provides a good base for designing intelligent network solutions. 5G Advanced addresses how to optimize the standardized interfaces for data collection while leaving the automation functionality, for example, training and inference up to the proprietary implementation to support full flexibility in the automation of the network.

AI/ML for RAN enhancements

Three use cases have been identified in the Release 17 study item related to RAN performance enhancement by using AI/ML techniques. Selected use cases from the Release 17 technical report will be taken into the normative phase in the next releases. The selected use cases are: 1) network energy saving; 2) load balancing; and 3) mobility optimization.

The selected use cases can be supported by enhancements to current NR interfaces, targeting performance improvements using AI/ML functionality in the RAN while maintaining the 5G NR architecture. One of the goals is to ensure vendor incentives in terms of innovation and competitiveness by keeping the AI model implementation specific. As shown in Fig.2 (on the top) an intent-based management approach can be adopted for use cases involving RAN-OAM interactions. The intent will be received by the RAN. The RAN will need to understand the intent and trigger certain functionalities as a result.

Ericsson White Paper - 5G Advanced: Evolution towards 6G - https://t.co/9ZKnC8PDxX#Free5Gtraining #Ericsson #5G #B5G #5GAdvanced #6G #5GNR #5GC #Timeline #XR #RedCap #NetworkAutomation #EnergySavings #DeterministicNetworking #IoT pic.twitter.com/eeI4SKvIRC

— Free 5G Training (@5Gtraining) July 21, 2022

AI/ML for physical layer enhancements

It is generally expected that AI/ML functionality can be used to improve the radio performance and/or reduced the complexity/overhead of the radio interface. 3GPP TSG RAN has selected three use cases to study the potential air interface performance improvements through AI/ML techniques, such as beam management, channel state information feedback enhancement, and positioning accuracy enhancements for different scenarios. The AI/ML-based methods may provide benefits compared to traditional methods in the radio interface. The challenge will be to define a unified AI/ML framework for the air interface by adequate AI/ML model characterization using various levels of collaboration between gNB and UE.

AI/ML in 5G core

5G Advanced will provide further enhancements of the architecture for analytics and on ML model life-cycle management, for example, to improve correctness of the models. The advancements in the architecture for analytics and data collection serve as a good foundation for AI/ML-based use cases within the different network functions (NFs). Additional use cases will be studied where NFs make use of analytics with the target to support in their decision making, for example, network data analytics functions (NWDAF)- assisted generation of UE policy for network slicing.

If you are interested in studying this topic further, check out 3GPP TR 37.817: Study on enhancement for data collection for NR and ENDC. Download the latest version from here.

Related Posts: 

• The 3G4G Blog: What Is the Role of AI and ML in the Open RAN and 5G Future?
• The 3G4G Blog: 3GPP Release-18 Work Moves Into Focus as Release-17 Reaches Maturity
• The 3G4G Blog: NWDAF in 3GPP Release-16 and Release-17
• The 3G4G Blog: 5G-Advanced Flagship Features
• The 3G4G Blog: 3GPP's 5G-Advanced Technology Evolution from a Network Perspective Whitepaper
• Free 6G Training: Qualcomm Pushing the boundaries of AI research 

What Is the Role of AI and ML in the Open RAN and 5G Future?

Artificial Intelligence and Machine Learning have moved on from just being buzzwords to bringing much needed optimization and intelligence in devices, networks and infrastructure; whether on site, on the edge or in the cloud.

New Study in 3GPP RAN3 on Artificial Intelligence and Machine Learning in NG-RAN - https://t.co/o4o4kq6Wpp via @3GPPLive #Free5Gtraining #3GPP #5G #ArtificialIntelligence #MachineLearning #AIML #RAN #Algorithms #SON #5GC #5GS pic.twitter.com/pvPlG9HpPC

— Free 5G Training (@5Gtraining) October 15, 2021

Qualcomm has been very active in talking about AI/ML in webinars and on their site. A detailed blog post looking at 'What’s the role of artificial intelligence in the future of 5G and beyond?' is available here. It was posted in time for a Light Reading webinar where Gabriel Brown, Principal Analyst – Mobile Networks and 5G, Heavy Reading and Tingfang Ji, Senior Director, Engineering - Wireless R&D, Qualcomm discuss the topic. The video is embedded below and slide deck is available here.

Standardizing AI/ML in cellular communication systems. From #Qualcomm presentation, The essential role of AI in the 5G future - How machine learning is accelerating wireless innovations in the new decade and beyond - https://t.co/fwxtoKIZKR#Free5Gtraining #3G4G5G #5G #AI #ML pic.twitter.com/YS5w8KXQuw

— Free 5G Training (@5Gtraining) September 27, 2021

Louis Scialabba, Senior Director of Marketing at Mavenir, looking at AI and Analytics spoke at Layer 123 conference on the topic, 'AI/ML for Next Gen 5G Mobile Networks'. His talk is embedded below and a blog post by him on the topic, 'The RIC Opens a New World of Opportunities for CSPs' is available here.

Related Posts: 

• Free 6G Training: Qualcomm Pushing the boundaries of AI research
• The 3G4G Blog: 3GPP Release-18 Work Moves Into Focus as Release-17 Reaches Maturity
• The 3G4G Blog: 5G-Advanced Flagship Features
• Connectivity Technology Blog: 5G Indoor Precise Positioning
• The 3G4G Blog: ITU Standardization Bureau on Machine Learning for 5G
• The 3G4G Blog: AI your Slice to 5G Perfection
• The 3G4G Blog: Qualcomm Explains 5G Millimeter Wave (mmWave) Future & Integrated Access and Backhaul (IAB)
• The 3G4G Blog: Qualcomm Demoes Sub-band Half Duplex (SBHD)
• Connectivity Technology Blog: Super Uplink to improve 5G Coverage and Speeds
• Free 6G Training: One XR Device to Rule Them All! 

5G-Advanced Flagship Features

I am starting to get a feeling that people may be becoming overwhelmed with all the new 5G features and standards update. That is why this presentation by Mikael Höök, Director Radio Research at Ericsson, at Brooklyn 6G Summit (B6GS) caught my attention. 

The talk discusses the network infrastructure progress made in the previous two years to better illustrate the advanced 5G timeline to discovering 6G requirements. At the end of the talk, there was a quick summary of the four flagship features that are shown in the picture above. The talk is embedded below, courtesy of IEEE TV

In addition to this talk, October 2021 issue of Ericsson Technology Review covers the topic "5G evolution toward 5G advanced: An overview of 3GPP releases 17 and 18". You can get the PDF here.

I have covered the basics of these flagship features in the following posts:

• AI/ML (link)
• RedCap (link)
• XR (link)
• Network Energy Efficiency (link)

Please feel free to add your thoughts as comments below.

Related Posts: 

• The 3G4G Blog: An Early View of 3GPP Release-18 5G-Advanced Topics
• The 3G4G Blog: 3GPP's 5G-Advanced Workshop Summary
• The 3G4G Blog: 3GPP's 5G-Advanced Technology Evolution from a Network Perspective Whitepaper
• The 3G4G Blog: '5G RAN Release 18 for Industry Verticals' Webinar Highlights
• The 3G4G Blog: 3GPP RAN Plenary Update and Evolution towards 5G-Advanced
• The 3G4G Blog: Initiative to Remove Non-inclusive terms from 3GPP Specifications
• The 3G4G Blog: 3GPP Release 16 Description and Summary of Work Items
• The 3G4G Blog: 3GPP Standards on Edge Computing
• The 3G4G Blog: Artificial Intelligence (AI) / Machine Learning (ML) in 5G Challenge by ITU
• The 3G4G Blog: ITU Standardization Bureau on Machine Learning for 5G
• The 3G4G Blog: Introduction to 5G Reduced Capability (RedCap) Devices
• The 3G4G Blog: 5G eXtended Reality (5G-XR) in 5G System (5GS)
• The 3G4G Blog: Energy Consumption in Mobile Networks and RAN Power Saving Schemes 

3GPP's 5G-Advanced Technology Evolution from a Network Perspective Whitepaper

China Mobile, along with a bunch of other organizations including China Unicom, China Telecom, CAICT, Huawei, Nokia, Ericsson, etc., produced a white paper on what technology evolutions will we see as part of 5G-Advanced. This comes not so long after the 3GPP 5G-Advanced Workshop which a blogged about here.

The abstract of the whitepaper says:

The commercialization of 5G networks is accelerating globally. From the perspective of industry development drivers, 5G communications are considered the key to personal consumption experience upgrades and digital industrial transformation. Major economies around the world require 5G to be an essential part of long-term industrial development. 5G will enter thousands of industries in terms of business, and technically, 5G needs to integrate DOICT (DT - Data Technology, OT - Operational Technology, IT - Information Technology and CT - Communication Technology) and other technologies further. Therefore, this white paper proposes that continuous research on the follow-up evolution of 5G networks—5G-Advanced is required, and full consideration of architecture evolution and function enhancement is needed.

This white paper first analyzes the network evolution architecture of 5G-Advanced and expounds on the technical development direction of 5G-Advanced from the three characteristics of Artificial Intelligence, Convergence, and Enablement. Artificial Intelligence represents network AI, including full use of machine learning, digital twins, recognition and intention network, which can enhance the capabilities of network's intelligent operation and maintenance. Convergence includes 5G and industry network convergence, home network convergence and space-air-ground network convergence, in order to realize the integration development. Enablement provides for the enhancement of 5G interactive communication and deterministic communication capabilities. It enhances existing technologies such as network slicing and positioning to better help the digital transformation of the industry.

The paper can be downloaded from China Mobile's website here or from Huawei's website here. A video of the paper launch is embedded below:

Nokia's Antti Toskala wrote a blog piece providing the first real glimpse of 5G-Advanced, here.

Related Posts: 

• The 3G4G Blog: '5G RAN Release 18 for Industry Verticals' Webinar Highlights
• The 3G4G Blog: 3GPP's 5G-Advanced Workshop Summary
• Operator Watch Blog: China Mobile on The Next Phase of 5G: Progress, Challenges, and Opportunities
• Connectivity Technology Blog: Ericsson Explains Internet of Drones and 3GPP UAV Roadmap
• The 3G4G Blog: 3GPP RAN Plenary Update and Evolution towards 5G-Advanced
• The 3G4G Blog: Introduction to 5G Reduced Capability (RedCap) Devices
• The 3G4G Blog: 3GPP Standards on Edge Computing
• Connectivity Technology Blog: 5G Drone Cell Towers
• Free 6G Training: Pre-Study of B5G and 6G in China
• Free 6G Training: China's IMT-2030 (6G) Promotion Group Releases 6G Whitepaper
• The 3G4G Blog: Real-life 5G Use Cases for Verticals from China
• The 3G4G Blog: 5G Remote Surgery and Telehealth Solutions
• Free 6G Training: China Mobile on the 5G/6G Connectivity Transformation for Sustainability
• Free 6G Training: Nokia Bell Labs on Augmenting Human Potential in the 6G Era
• Free 6G Training: Huawei talks about Beyond 5G, 5.5G and 6G
• The 3G4G Blog: Nokia veterans Harri Holma and Antti Toskala explain 5G Basics 

ITU Standardization Bureau on Machine Learning for 5G

Last year I blogged about Global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks".  The grand challenge finale happened in December. All the recording and presentations are available here.

Back in October, Bilel Jamoussi from ITU presented a keynote to the 2020 IEEE 5G World Forum plenary session where he addressed the challenges of applying machine learning in networks, ITU’s ML toolkit, and ITU’s AI/ML in 5G Competition. IEEE Tv shared the presentation only in April so the competition part is a bit outdated. It does nevertheless an interesting 20 minute talk.

ITU Recommendation Y.3174, Framework for data handling to enable machine learning in future networks including IMT-2020 is available here.

Related Posts: 

• The 3G4G Blog: Artificial Intelligence (AI) / Machine Learning (ML) in 5G Challenge by ITU
• The 3G4G Blog: IMT-2020 (5G) Requirements 

NWDAF in 3GPP Release-16 and Release-17

We looked at Network Data Analytics Function, NWDAF, in detail here. While the 3GPP Release-16 work just starting back then, we have now completed Rel-16 and looking at Release 17. 

The 5G Core (5GC) supports the application of analytics to provide Intelligent Automation of the network, In Rel-16 the set of use cases that are proposed for the NWDAF has been widely expanded. 

In an earlier post, we looked at the ATIS webinar discussing Release-16 & forthcoming features in Rel-17. Puneet Jain, Director of Technical Standards at Intel and 3GPP SA2 Chairman talked briefly about NWDAF. The following is from his talk:

Release-16 provides support for Network Automation and Data Analytics.  Network Data Analytics Function (NWDAF) was defined to provide analytics to 5G Core Network Functions (NFs) and to O&M. It consists of several services that were defined in 3GPP Rel-16 and work is now going in Release 17 to further extend them. 

In release 16 Slice load level related network data analytics and observed service experience related network data analytics were defined. NF load analytics as well Network Performance analytics was also specified. NWDAF provides either statistics or prediction on the load communication and mobility performance in the area of interest. 

Other thing was about the UE related analytics which includes UE mobility analytics, UE communication analytics, Expected UE behavior parameter, Related network data analytics and abnormal behavior related network data analytics.

The NWDAF can also provide user data congestion related analytics. This can be done by one time reporting or continuous reporting in the form of statistics or prediction or both to any other network function. 

QoS sustainability analytics, this is where the consumer of QoS sustainability analytics may request NWDAF analytics information regarding the QoS change statistic for a specific period in the past in a certain area or the likelihood of QoS change for a specific period in future, in certain areas. 

In Release 17, studies are ongoing for network automation phase 2. This includes some leftover from Release 16 such as UE driven analytics, how to ensure that slice SLA is guaranteed and then also new functionality is being discussed that includes things like support for multiple NWDAF instance in one PLMN including hierarchies, how to enable real-time or near-real-time NWDAF communications, how to enable NWDAF assisted user pane optimization and last which is very interesting is about interaction between NWDAF and AI model and training service owned by the operator.

This article on TM Forum talks about NWDAF deployment challenges and recommendations:

To deploy NWDAF, CSPs may encounter these challenges:

• Some network function vendors may not be standards compliant or have interfaces to provide data or receive analytics services.
• Integrating NWDAF with existing analytics applications until a 4G network is deployed is crucial as aggregated network data is needed to make decisions for centralized analytics use cases.
• Many CSPs have different analytics nodes deployed for various use cases like revenue assurance, subscriber/marketing analytics and subscriber experience/network management. Making these all integrated into one analytics node also serving NWDAF use cases is key to deriving better insights and value out of network data.
• Ensuring the analytics function deployed is integrated to derive value (e.g., with orchestrator for network automation, BI tools/any UI/email/notification apps for reporting).

Here are some ways you can overcome these challenges and deploy efficient next-generation analytics with NWDAF:

• Mandate a distributed architecture for analytics too, this reduces network bandwidth overhead due to analytics and helps real-time use cases by design.
• Ensure RFPs and your chosen vendors for network functions have, or plan to have, NWDAF support for collecting and receiving analytics services.
• Look for carrier-grade analytics solutions with five nines SLAs.
• Choose modular analytics systems that can accommodate multiple use cases including NWDAF as apps and support quick development.
• Resource-efficient solutions are critical for on-premise or cloud as they can decrease expenses considerably.
• Storage comes with a cost, store more processed smart data and not more raw big data unless mandated by law.
• In designing an analytics use case, get opinions from both telco and analytics experts, or ideally an expert in both, as they are viewed from different worlds and are evolving a lot.

This is such an important topic that you will hear more about it on this blog and elsewhere.

Related Posts:

• The 3G4G Blog: ATIS Webinar on '5G Standards Developments in 3GPP Release 16 and Beyond'
• The 3G4G Blog: Automating the 5G Core using Machine Learning and Data Analytics
• The 3G4G Blog: Artificial Intelligence - Beyond SON for Autonomous Networks
• 3G4G: 5G Service Based Architecture (SBA) 

Artificial Intelligence (AI) / Machine Learning (ML) in 5G Challenge by ITU

ITU is conducting a global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks". If you don't know the difference between AI & ML, this picture from the old blog post may help.

The ITU website says:

Artificial Intelligence (AI) will be the dominant technology of the future and will impact every corner of society. In particular, AI / ML (machine learning) will shape how communication networks, a lifeline of our society, will be run. Many companies in the ICT sector are exploring how to make best use of AI/ML. ITU has been at the forefront of this endeavour exploring how to best apply AI/ML in future networks including 5G networks. The time is therefore right to bring together the technical community and stakeholders to brainstorm, innovate and solve relevant problems in 5G using AI/ML. Building on its standards work, ITU is conducting a global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks". 

Participants will be able to solve real world problems, based on standardized technologies developed for ML in 5G networks. Teams will be required to enable, create, train and deploy ML models (such that participants will acquire hands-on experience in AI/ML in areas relevant to 5G). Participation is open to ITU Member States, Sector Members, Associates and Aca​demic Institutions and to any individual from a country that is a member of ITU. ​ ​

There are also some cash prizes, etc. There are various topics with presentation slides & recordings freely available. 

I found the slides from ITU AI/ML in 5G Challenge —”Machine Learning for Wireless LANs + Japan Challenge Introduction” (link) very interesting. There are many other topics including AR / VR / XR, etc, as well.

Have a look at the ITU website here.


Related Posts:

• The 3G4G Blog: An Introduction to Vehicle to Everything (V2X) and Cellular V2X (C-V2X)
• The 3G4G Blog: 5G Remote Surgery and Telehealth Solutions
• The 3G4G Blog: 5G eXtended Reality (5G-XR) in 5G System (5GS)
• The 3G4G Blog: Real-life 5G Use Cases for Verticals from China

AI in 5G – the why and how

IET recently held the 6th Annual 5G conference bringing together key players in the 5G world. You can watch the videos for that event here (not all have been uploaded at the time of writing this post).

We reached out to Dr. Yue Wang to share her presentation with us and she has kindly done so. The presentation and video are embedded below.

Yue Wang: AI in 5G –the Why and How - Jan 2019 from 3G4G

Where can #AI be used in #5G infrastructure- presentation by @YueWUK - Energy Saving, Cell Selection, Fronthaul Management, Orchestration & more... @IETevents @IETCommunities #5GTheAdvent Very insightful presentation pic.twitter.com/zJMiry9ksE

— Mark Gilmour (@mark_gilmour) January 30, 2019

Related posts:

• The Yin and the Yang of AI & Blockchain
• Automated 4G / 5G HetNet Design
• Benefits and Challenges of Applying Device-Level AI to 5G networks
• AI and Analytics Based Network Designing & Planning
• Artificial Intelligence - Beyond SON for Autonomous Networks
• Telefonica: Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to Connect the Unconnected

Telefonica: Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to Connect the Unconnected

Earlier, I wrote a detailed post on how Telefonica was on a mission to connect 100 Million Unconnected with their 'Internet para todos' initiative. This video below is a good advert of what Telefinica is trying to achieve in Latin America


I recently came across a LinkedIn post on how Telefónica uses AI / ML to connect the unconnected by Patrick Lopez, VP Networks Innovation @ Telefonica. It was no brainer that this needs to be shared.

Big data for social good from Patrick Lopez

In his post, Patrick mentions the following:

To deliver internet in these environments in a sustainable manner, it is necessary to increase efficiency through systematic cost reduction, investment optimization and targeted deployments.

Systematic optimization necessitates continuous measurement of the financial, operational, technological and organizational data sets.

1. Finding the unconnected

The first challenge the team had to tackle was to understand how many unconnected there are and where. The data set was scarce and incomplete, census was old and population had much mobility. In this case, the team used high definition satellite imagery at the scale of the country and used neural network models, coupled with census data as training. Implementing visual machine learning algorithms, the model literally counted each house and each settlement at the scale of the country. The model was then enriched with crossed reference coverage data from regulatory source, as well as Telefonica proprietary data set consisting of geolocalized data sessions and deployment maps. The result is a model with a visual representation, providing a map of the population dispersion, with superimposed coverage polygons, allowing to count and localize the unconnected populations with good accuracy (95% of the population with less than 3% false positive and less than 240 meters deviation in the location of antennas).


2. Optimizing transport

Transport networks are the most expensive part of deploying connectivity to remote areas. Optimizing transport route has a huge impact on the sustainability of a network. This is why the team selected this task as the next challenge to tackle.

The team started with adding road and infrastructure data to the model form public sources, and used graph generation to cluster population settlements. Graph analysis (shortest path, Steiner tree) yielded population density-optimized transport routes.


3. AI to optimize network operations

To connect very remote zones, optimizing operations and minimizing maintenance and upgrade is key to a sustainable operational model. This line of work is probably the most ambitious for the team. When it can take 3 hours by plane and 4 days by boat to reach some locations, being able to make sure you can detect, or better, predict if / when you need to perform maintenance on your infrastructure. Equally important is how your devise your routes so that you are as efficient as possible. In this case, the team built a neural network trained with historical failure analysis and fed with network metrics to provide a model capable of supervising the network health in an automated manner, with prediction of possible failure and optimized maintenance route.

I think that the type of data driven approach to complex problem solving demonstrated in this project is the key to network operators' sustainability in the future. It is not only a rural problem, it is necessary to increase efficiency and optimize deployment and operations to keep decreasing the costs.


Finally, its worth mentioning again that I am helping CW (Cambridge Wireless) organise their annual CW TEC conference on the topic 'The inevitable automation of Next Generation Networks'. There are some good speakers and we will have similar topics covered from different angles, using some other interesting approaches. The fees are very reasonable so please join if you can.

Related posts:

• Internet para todos: Telefonica and Parallel Wireless on a mission to connect 100 Million Unconnected
• Automating the 5G Core using Machine Learning and Data Analytics
• ITU 'Network 2030': Initiative to support Emerging Technologies and Innovation looking beyond 5G advances
• Artificial Intelligence - Beyond SON for Autonomous Networks
• Twitter et al. for Small Cell Planning

Automating the 5G Core using Machine Learning and Data Analytics

One of the new entities introduced by 3GPP in the 5G Core SBA (see tutorial here) is Network Data Analytics Function, NWDAF.

3GPP TR 23.791: Study of Enablers for Network Automation for 5G (Release 16) describes the following 5G Network Architecture Assumptions:

1 The NWDAF (Network Data Analytics Function) as defined in TS 23.503 is used for data collection and data analytics in centralized manner. An NWDAF may be used for analytics for one or more Network Slice.
2 For instances where certain analytics can be performed by a 5GS NF independently, a NWDAF instance specific to that analytic maybe collocated with the 5GS NF. The data utilized by the 5GS NF as input to analytics in this case should also be made available to allow for the centralized NWDAF deployment option.
3 5GS Network Functions and OAM decide how to use the data analytics provided by NWDAF to improve the network performance.
4 NWDAF utilizes the existing service based interfaces to communicate with other 5GC Network Functions and OAM.
5 A 5GC NF may expose the result of the data analytics to any consumer NF utilizing a service based interface.
6 The interactions between NF(s) and the NWDAF take place in the local PLMN (the reporting NF and the NWDAF belong to the same PLMN).
7 Solutions shall neither assume NWDAF knowledge about NF application logic. The NWDAF may use subscription data but only for statistical purpose.

Picture Source: Application of Data Mining in the 5G Network Architecture by Alexandros Kaloxylos

Continuing from 3GPP TR 23.791:

The NWDAF may serve use cases belonging to one or several domains, e.g. QoS, traffic steering, dimensioning, security.
The input data of the NWDAF may come from multiple sources, and the resulting actions undertaken by the consuming NF or AF may concern several domains (e.g. Mobility management, Session Management, QoS management, Application layer, Security management, NF life cycle management).
Use case descriptions should include the following aspects:
1. General characteristics (domain: performance, QoS, resilience, security; time scale).
2. Nature of input data (e.g. logs, KPI, events).
3. Types of NF consuming the NWDAF output data, how data is conveyed and nature of consumed analytics.
4. Output data.
5. Possible examples of actions undertaken by the consuming NF or AF, resulting from these analytics.
6. Benefits, e.g. revenue, resource saving, QoE, service assurance, reputation.

Picture Source: Application of Data Mining in the 5G Network Architecture by Alexandros Kaloxylos

3GPP TS 23.501 V15.2.0 (2018-06) Section 6.2.18 says:

NWDAF represents operator managed network analytics logical function. NWDAF provides slice specific network data analytics to a NF. NWDAF provides network analytics information (i.e., load level information) to a NF on a network slice instance level and the NWDAF is not required to be aware of the current subscribers using the slice. NWDAF notifies slice specific network status analytic information to the NFs that are subscribed to it. NF may collect directly slice specific network status analytic information from NWDAF. This information is not subscriber specific.

In this Release of the specification, both PCF and NSSF are consumers of network analytics. The PCF may use that data in its policy decisions. NSSF may use the load level information provided by NWDAF for slice selection.

NOTE 1: NWDAF functionality beyond its support for Nnwdaf is out of scope of 3GPP.
NOTE 2: NWDAF functionality for non-slice-specific analytics information is not supported in this Release of the specification.

3GPP Release-16 is focusing on 5G Expansion and 5G Efficiency, SON and Big Data are part of 5G Efficiency.

3GPP Release-16 is going to focus on 5G expansion & efficiency. Listen to the @3GPPLive Webinar here: https://t.co/tKzXJ3cUFj pic.twitter.com/1xCZfpT9lt

— 3G4G (@3g4gUK) July 14, 2018

Light Reading Artificial Intelligence and Machine Learning section has a news item on this topic from Layer123's Zero Touch & Carrier Automation Congress:

The 3GPP standards group is developing a machine learning function that could allow 5G operators to monitor the status of a network slice or third-party application performance.

The network data analytics function (NWDAF) forms a part of the 3GPP's 5G standardization efforts and could become a central point for analytics in the 5G core network, said Serge Manning, a senior technology strategist at Sprint Corp.

Speaking here in Madrid, Manning said the NWDAF was still in the "early stages" of standardization but could become "an interesting place for innovation."

The 3rd Generation Partnership Project (3GPP) froze the specifications for a 5G new radio standard at the end of 2017 and is due to freeze another set of 5G specifications, covering some of the core network and non-radio features, in June this year as part of its "Release 15" update.

Manning says that Release 15 considers the network slice selection function (NSSF) and the policy control function (PCF) as potential "consumers" of the NWDAF. "Anything else is open to being a consumer," he says. "We have things like monitoring the status of the load of a network slice, or looking at the behavior of mobile devices if you wanted to make adjustments. You could also look at application performance."

In principle, the NWDAF would be able to make use of any data in the core network. The 3GPP does not plan on standardizing the algorithms that will be used but rather the types of raw information the NWDAF will examine. The format of the analytics information that it produces might also be standardized, says Manning.

Such technical developments might help operators to provide network slices more dynamically on their future 5G networks.

Generally seen as one of the most game-changing aspects of 5G, the technique of network slicing would essentially allow an operator to provide a number of virtual network services over the same physical infrastructure.

For example, an operator could provide very high-speed connectivity for mobile gaming over one slice and a low-latency service for factory automation on another -- both reliant on the same underlying hardware.

However, there is concern that without greater automation operators will have less freedom to innovate through network slicing. "If operators don't automate they will be providing capacity-based slices that are relatively large and static and undifferentiated and certainly not on a per-customer basis," says Caroline Chappell, an analyst with Analysys Mason .

In a Madrid presentation, Chappell said that more granular slicing would require "highly agile end-to-end automation" that takes advantage of progress on software-defined networking and network functions virtualization.

"Slices could be very dynamic and perhaps last for only five minutes," she says. "In the very long term, applications could create their own slices."

Despite the talk of standardization, and signs of good progress within the 3GPP, concern emerged this week in Madrid that standards bodies are not moving quickly enough to address operators' needs.

Caroline Chappell's talk is available here whereas Serge Manning's talk is embedded below:



I am helping CW organise the annual CW TEC conference on the topic The inevitable automation of Next Generation Networks

#CWTEC 2018 planning committee working hard to bring an exciting conference - explores the role of #AI in delivering Next Gen. Network performance; & bridges the gap between #AI/#ML & Telecos communities! @zahidtg @Bob_CWCEO @swunger Paul Ceely @bt_uk , John Haine @BristolCSN pic.twitter.com/4uGprC1usg

— Sylvia Lu (@SylviaLuUk) July 23, 2018

Communications networks are perhaps the most complex machines on the planet. They use vast amounts of hardware, rely on complex software, and are physically distributed over land, underwater, and in orbit. They increasingly provide essential services that underpin almost every aspect of life. Managing networks and optimising their performance is a vast challenge, and will become many times harder with the advent of 5G. The 4th Annual CW Technology Conference will explore this challenge and how Machine Learning and AI may be applied to build more reliable, secure and better performing networks.

Is the AI community aware of the challenges facing network providers? Are the network operators and providers aware of how the very latest developments in AI may provide solutions? The conference will aim to bridge the gap between AI/ML and communications network communities, making each more aware of the nature and scale of the problems and the potential solutions.

I am hoping to see some of this blog readers at the conference. Looking forward to learning more on this topic amongst others for network automation.

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