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There are many parameters for a company to be successful in telecoms, and in the RIC and Apps area, there are at least three key skill sets that are necessary to make it.
Artificial Intelligence is a popular term many in the industry use as a shorthand for their excel macros linear projection and forecast mastery. Data literacy is crucial here, as big data / machine learning / deep learning / artificial intelligence terms are bandied around for marketing purposes. I am not an expert in the matter, but I have a strong feeling that the use cases for algorithmic fall into a few categories. I will try to expose them in my terms, apologies in advance to the specialists as the explanation will be basic and profane.
- Anomaly / pattern detection provide a useful alarming system if the system's behavior has a sufficiently long time series and the variance is somewhat reduced or predictable. This does not require more than data knowledge, it is a math problem.
- Optimization / correction should allow, provided the anomaly / pattern detection is accurate to pinpoint specific actions that would allow a specific outcome. This where RAN knowledge is necessary. It is crucial to be able to identify from the inputs whether the output is accurate and to which element it corresponds. Again, a long time series of corrections / optimizations and their impact / deviation is necessary for the model to be efficient.
- Prediction / automation is the trickiest part. Ideally, given enough knowledge of the system's patterns, variances and deviations, one can predict with some accuracy its behavior over time in steady state and when anomalies occur and take a preemptive /corrective action. Drawn to its logical conclusion, full automation and autonomy would be possible. This is where most companies overpromise in my mind. The system here is a network. Not only is it vast and composed of millions of elements (after all that is just a computing issue), it is also always changing. Which means that there no steady state and that the time series is a collection of dynamically changing patterns. Achieving full automation under these conditions seems impossible. Therefore, it is necessary to reframe expectations, especially in a multi vendor environment and to settle for pockets of automation, with AI/ML augmented limited automation.
Platform and developer ecosystem management is also extremely important in the RIC and Apps segment if one wants to deploy multi vendor solutions. The dream of being able to instantiate Apps from different vendors and orchestrate them harmoniously is impossible without a rich platform, with many platform services attributes (lifecycle management, APIs, SDK, Data / messaging bus, orchestration...). This does not necessarily require much RAN knowledge and this why we are seeing many new entrants in this field.
The last, but foremost, in my mind, is the RAN knowledge. The companies developing RAN Intelligent Controllers and apps need to have deep understanding of the RAN, its workings and evolution. Deep knowledge may probably not necessary for the most pedestrian use cases around observability and representation of the health and performance of the system or the network, but any App that would expect a retro feedback and to send instruction to the lower elements of the architecture needs understanding of not only of the interfaces, protocols and elements but also their function, interworking and capabilities. If the concept of RICs and Apps is to be successful, several Apps will need to be able to run simultaneously and ideally from different vendors. Understanding the real-life consequences of an energy efficiency App and its impact on quality of service, quality of experience, signaling is key in absolute. It becomes even more crucial to understand how Apps can coexist and simultaneously, or by priority implement power efficiency, spectrum optimization and handover optimization for instance. The intricacies of beamforming, beam weight, beam steering in mMIMO systems, together with carrier aggregation and dynamic spectrum sharing mandate a near / real time control capability. The balance is delicate and it is unlikely that scheduler priorities could conceivably be affected by an rApp that has little understanding of these problematics. You don't drive a formula one car while messing about the gear settings.
If you want to know how I rank the market leaders in each of these categories, including Accelleran, Aira technologies, Airhop, Airspan, Capgemini, Cohere technologies, Ericsson, I-S Wireless, Fujitsu, Juniper, Mavenir, Nokia, Northeastern University, NTT DOCOMO, Parallel Wireless, Radisys, Rakuten, Rimedo Labs, Samsung, VIAVI, vmware and others, you'll have to read my report or register for my workshop.
