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.
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