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 

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)