Tuesday, 16 August 2022

Managing 5G Signalling Storms with Service Communication Proxy (SCP)

When we made our 5G Service Based Architecture (SBA) tutorial some four years back, it was based on Release-15 of the 3GPP standards. All Network Functions (NFs) simply sent discovery requests to the Network Repository Function (NRF). While this works great for trials and small scale deployments it can also lead to issues as can be seen in the slide above.

In 3GPP Release-16 the Service Communication Proxy (SCP) has now been introduced to allow the Control Plane network to handle and prioritize massive numbers of requests in real time. The SCP becomes the control point that mediates all Signalling and Control Plane messages in the network core.

SCP routing directs the flow of millions of simultaneous 5G function requests and responses for network slicing, microservice instantiation or edge compute access. It also plays a critical role in optimizing floods of discovery requests to the NRF and in overall Control Plane load balancing, traffic prioritization and message management.

A detailed whitepaper on '5G Signaling and Control Plane Traffic Depends on Service Communications Proxy (SCP)' by Strategy Analytics is available on Huawei's website here. This report was a follow on from the 'Signaling — The Critical Nerve Center of 5G Networks' webinar here.

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Wednesday, 10 August 2022

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.

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.

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Tuesday, 2 August 2022

GSMAi Webinar: Is the Industry Moving Fast Enough on Standalone 5G?

I recently participated in a webinar, discussing one of my favourite topics, 5G Standalone (5G SA). If you do not know about 5G SA, you may want to quickly watch my short and simple video on the topic here.

Last year I blogged about GSA's 5G Standalone webinar here. That time we were discussing why 5G SA is taking time to deliver, it was sort of a similar story this time. Things are changing though and you will see a lot more of these standalone networks later this year and even early next year. 

The slides of the webinar are available here and the video is embedded below:

Here are some of my thoughts on why 5G SA is taking much longer than most people anticipated:

  • 5G SA will force operators to move to 5G core which is a completely new architecture. The transition to this is taking much longer than expected, especially if there are a lot of legacy services that needs to be supported.
  • Many operators are moving towards converged core with 4G & 5G support to simply the core. This transition is taking long.
  • For taking complete advantage of 5G architecture, cloud native implementation is required. Some operators have already started the transition to cloud native but others are lagging.
  • 5G SA speeds will be lower than NSA speeds hence some operators who don't have a lot of mid-band spectrum are delaying their 5G SA rollouts.
  • Many operators have managed to reduce their latency as they start to move to edge datacentres, hence the urgency for 5G standalone has reduced.
  • Most operators do not see any new revenue opportunities because of 5G SA, hence they want to be completely ready before rolling out 5G SA
  • Finally, you may hear a lot about not enough devices supporting 5G SA but that's not the device manufacturers views.  See this tweet from GSA 👇

Do you agree with my reasoning? If not, please let me know in the comments.

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