Ericsson Q&A: Navigating community knowledge challenges and AI/ML


There’s a dizzying array of information accessible to telecom operators on the standing, efficiency and well being of their networks; at varied speeds and ranges of granularity; targeted on end-user-experience, peak community functionality, or dozens of different potential indicators. 

Ayodele Damola, director of AI/ML technique, Ericsson

Operators face appreciable challenges in making the very best use of that knowledge and translating it into actionable intelligence to validate, guarantee and optimize community operations. RCR Wi-fi Information reached out to Ayodele Damola, director of synthetic intelligence/machine studying technique at Ericsson, for his perspective on these challenges and the present panorama and developments in gathering, analyzing and leveraging community knowledge in addition to the usage of AI and ML in telecom networks.

This Q&A was carried out through electronic mail and has been frivolously edited.

RCR: Because the trade strikes additional into 5G and 5G Standalone deployments, in addition to disaggregation and cloud-native networks, how would you describe the challenges surrounding navigating network-related knowledge? Is it a matter of recent sources (i.e., microservices resulting in extra knowledge granularity), new quantity or scale of information, or the rate/pace at which knowledge is offered — or another issue? 

The principle problem navigating community knowledge is the rise in complexity. As 5G positive aspects traction throughout the globe, communication service supplier (CSP) networks have gotten much more advanced. The complexity is because of the new set of companies being supplied, the rise in quantity and forms of units within the community, availability of recent spectrum frequencies and bands, and the evolution of bodily networks to virtualized networks. This complexity equates to a rise within the quantity of community knowledge generated; community knowledge generated in numerous time frames along with the presence of extra number of community knowledge. If we take, for instance, the reference parameter rely evolution in 3GPP radio entry networks (RAN) – we noticed that with 2G networks once we had just some hundred reference parameters that wanted to be configured. In 5G, that quantity has grown to a number of thousand parameters. In 6G, we anticipate a good bigger variety of reference parameters. Improve in complexity can be mirrored in ‘overabundance of information’ – it turns into tough to seek out related knowledge with out progressive filtering and aggregation. One other concern is the shortage of standardization of information throughout proprietary vendor tools resulting in difficulties in leveraging the information for insights. One more concern is knowledge administration – immediately, it’s an afterthought consisting of brittle system info (SI) pushed pipelines that are inefficient. Principally, knowledge is duplicated and exhausting to control. All these elements put new calls for on the extent of effort wanted to handle and management the community, and thereby result in an elevated CSP operation expense (OpEx) and ultimately elevated capital expense (CapEx). It’s changing into clear that it’ll now not be attainable to handle networks in a legacy method the place community engineers take a look at dashboards and make adjustments manually to the community. That is the place a expertise like AI enters the image, a expertise that permits automation and thereby reduces complexity.

RCR: Do you suppose that telecom operators, by and enormous, have a superb deal with on their community knowledge? What do you suppose they do effectively, and the place is there room for enchancment? 

Entry to community knowledge is a vital element of most CSP methods immediately. Community knowledge from multi-vendor networks has been considerably of a problem for CSPs as a result of every vendor has barely completely different community node interfaces – therefore, the format and syntax of the information could also be completely different throughout completely different distributors. Typically, CSPs lack a constant technique for knowledge persistence and publicity. Completely different storage mechanisms (e.g., knowledge lakes) and retention insurance policies complicate the scalable dealing with of information volumes. Sooner or later, open requirements promise to alleviate this problem, particularly by the introduction of the Service Administration and Orchestration (SMO) framework outlined by the ORAN Alliance, which offers a set of well-defined interfaces enabling CSPs to entry and act on knowledge from each purpose-built and virtualized multi-vendor and multi-technology networks. Community knowledge is uncovered through open interfaces (e.g., R1, A1, O1, O2 and so on.) to the completely different SMO features.  

Inside the SMO (our implementation is named the Ericsson Clever Automation Platform) the Knowledge administration and ingest perform allows CSPs to effectively and securely ingest and handle knowledge. The AI/ML and perception era perform allows the flexibility to course of and analyze the information, deriving insights and facilitating actuations.

RCR: What do community operators wish to use their knowledge for? Are you able to give the highest three makes use of for network-related knowledge which can be the first curiosity of MNOs?

Market analysis earlier commissioned by Ericsson exhibits that CSPs leverage community knowledge throughout many use instances, with the highest three being: 

  • Buyer Expertise Administration: Resolution helps CSPs predict buyer satisfaction, detect expertise points, perceive root causes, and mechanically takes the subsequent greatest motion to enhance expertise and operational effectivity resulting in churn discount and elevated buyer adoption.
  • Safety/Fraud & Income Assurance: Addresses safety administration and income assurance together with billing and charging.
  • Cloud & IT Operations: Automation of cloud and IT administration operations together with administrative processes with assist for {hardware} and software program, and the speedy isolation of faults.

Moreover, different vital use instances embody Community Administration & Operations, Enterprise Operations, Service Assurance for RAN & Core, Community Design and Optimization, RAN Spectrum and Site visitors Administration. 

RCR: What position is AI/ML enjoying in networks at this second in time? Persons are very within the potential — what’s the present actuality of sensible AI/ML use in telecom networks, and will you give some real-world examples? 

AI/ML guarantees big potential in relation to optimizing and automating CSP networks. The trade continues to be in its early days, however some issues which were solved with AI/ML present substantial positive aspects. The complete systemization of CSP networks based mostly on AI/ML, additionally referred to as AI-native networks, continues to be some years away. In relation to the RAN particularly, Ericsson believes that AI/ML will play a key position within the following areas: 

  • Community evolution: Enhances community planning with extra environment friendly RF planning, website choice and capability administration.  Improves community and repair efficiency and allows new revenues by knowledge pushed and intent-based insights and suggestions.
  • Community deployment: Handles provisioning and life cycle administration of advanced networks with optimum prices and pace to market.
  • Community optimization: Clever autonomous features to optimize buyer expertise and return on investments, e.g., RF shaping, site visitors and mobility administration, power effectivity and so on.
  • Community therapeutic: Service continuity and backbone of each primary and sophisticated incidents, delivering excessive availability whereas protecting the operation prices at a minimal.
  • AI and automation basis: Allows sooner TTM for – and belief in – excessive efficiency AI and automation use instances by way of openness and suppleness.

As well as, Ericsson believes CSPs will profit from an end-to-end managed companies operations answer enabling the clever administration of CSP networks and companies to supply superior connectivity and consumer expertise. Powered by superior analytics and machine studying algorithms, CSPs will profit from the flexibility to foretell potential community points attributable to {hardware}, software program, or exterior elements reminiscent of climate disturbances or buyer habits patterns.

Sensible AI/ML use in telecom networks immediately:

  • Capability Planning: Gives the flexibility to carry out proactive planning based mostly on site visitors forecast together with AI/ML prediction of utilization KPIs. The end result would be the optimum capability Expenditure to fulfill a sure QoS stage. Forecast predictions, bottleneck identification and community dimensioning are the principle use instances. Advantages embody 20-40% CapEx financial savings much less provider expansions in comparison with conventional strategy, and 83% elevated operational effectivity elevated operational effectivity on dimensioning duties.
  • Efficiency Diagnostics: An answer that analyzes CSPs’ RAN to detect and classify cell points. Recognized points are additional investigated all the way down to root trigger stage, enabling quick and correct optimization of end-user efficiency. Advantages embody: 30% enhance in capability per optimization full time worker (FTE), and 15% higher downlink pace in cells with points.
  • Improved spectrum effectivity: By gathering adjoining cell knowledge in actual time, it’s attainable to optimize radio hyperlink efficiency utilizing sample recognition. Advantages embody: 15% improved spectrum effectivity and 50% elevated cell edge DL throughput.
  • Sustainability: An autonomous mechanism utilizing AI/ML applied sciences and closed-loop automation to cut back every day radio community power consumption by as much as 25% with zero influence on consumer expertise.

In relation to the potential of AI/ML in CSP networks, we envision a journey with a number of steps: 

Within the earliest step, there was full human intervention because the community was manually configured. Within the subsequent step, the community configuration was nonetheless carried out by people, however the effort stage was lowered to the configuration of a beneficial set of parameters. Within the rule-based step, the human position was to create a algorithm which then managed the community; this required that the human builders have a deep understanding of how the community features. We’re at present transitioning to the step with autonomous options the place we’ve got AI/ML fashions adapting to new conditions and thereby giving CPS extra automated management. Going ahead, the imaginative and prescient is that the community will evolve to totally autonomous with no human intervention apart from setting intents by which the community operates – mainly, offering the ‘what’ necessities to the community with the community performing the wanted ‘how’ actuation and management. It’s a journey just like the evolution of automobiles from manually managed to totally self-driving.

RCR: When it comes to edge vs. centralized cloud, is most knowledge processing nonetheless centralized or do you see issues really changing into extra distributed and taking place on the edge? Is it completely different for telco workloads vs. enterprise workloads? 

It is very important distinguish between knowledge assortment, usually for the aim of coaching an AI/ML mannequin, and inference, which entails appearing on new knowledge by a skilled mannequin. The method of gathering knowledge will entail cleansing and sorting the information after which utilizing this knowledge to coach fashions. Whereas the information assortment will occur out within the community in a distributed method, the information processing will usually be completed centralized. After the mannequin is skilled, the deployment of the mannequin within the community might be both distributed or centralized, relying on the use case. Given the big quantity of community knowledge and given its real-time nature, inferencing of telco workloads will usually be extra distributed in comparison with enterprise workloads. 

RCR: There was for some years a giant push towards “knowledge lakes” and storing as a lot knowledge as attainable to sift via for enterprise intelligence. Is that this the case for community knowledge as effectively, or is there extra desire for real-time intelligence? What does “actual time” really imply proper now, how shut can it get? 

The desire for actual time intelligence will rely upon the use case. Community knowledge is generated throughout the community in numerous time frames, and we are able to classify use instances based mostly on the community knowledge into fast-loop use instances (microsecond timeframe) and slow-loop use instances (timeframe of days and even weeks) and all of the in-between. The character of the use instances throughout completely different timeframes will differ considerably.  An actual time or fast-loop use case is, for instance, radio scheduling made in microseconds executed domestically inside a community node and usually absolutely automated i.e., completed with no human within the loop. Gradual-loop use instances shall be targeted extra on long run community developments like community benchmarking, requiring community extensive coordination with extra time accessible to decide and can doubtless entail interplay with a human. Knowledge lakes are then fitted to the 2 higher loops within the image beneath, whereas the time sensitivity of each the information and the choices made within the two decrease loops communicate towards persistence of the decision-bearing knowledge in any sort of information lake.

RCR: What knowledge wants, challenges or adjustments ought to telecom operators be planning for now that you just see coming within the subsequent 3-5 years?  

I see three issues:

  • High quality knowledge: CSPs are challenged with find out how to outline and develop a high quality knowledge strategy from which all AI options may be delivered. The difficulty is that knowledge continues to be contained in silos throughout most CSPs, from legacy programs to new programs. AI/ML will solely make a distinction if clear knowledge is offered from all sources. Therefore, investing the time in after which coaching of AI algorithms with the fitting knowledge shall be important to lowering false alarms and in bettering AI effectiveness. An adjoining downside is the difficulty of related knowledge – among the knowledge generated has little or no to no entropy (i.e., its usefulness is proscribed) and figuring out such knowledge is an ongoing problem. 
  • Knowledge platform: All CSPs require a sturdy platform to mixture, sanitize, and analyze the information. Whereas there are a number of potential options out there, there are considerations of privateness, safety, and vendor lock-in that make knowledge platform choice tough.
  • Knowledge technique: Many CSPs lack an end-to-end knowledge technique which covers knowledge governance, simplification and automatic assortment, and knowledge evaluation. Whereas some CSPs have a Chief Knowledge Officer (CDO), many CSPs haven’t but been in a position to implement a companywide knowledge technique throughout varied organizations resulting in disconnected islands of information.