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Machine Learning Requirements for 5G and Private Wireless Networks

Machine Learning Requirements for 5G and Private Wireless Networks | Wallaroo.AI Blog
Machine Learning Requirements for 5G and Private Wireless Networks | Wallaroo.AI Blog

In the rapidly evolving landscape of telecommunications, a new frontier is emerging, driven by the convergence of cutting-edge technology and network infrastructures. Welcome to the world of Edge AI, where machine learning meets the network edge. As 5G networks and IoT devices proliferate, the need for real-time data processing and decision-making has never been more paramount. Edge AI offers a solution, enabling swift, low-latency machine learning operations right where the data originates—at the network’s edge. Join us as we delve into its possibilities, challenges, and the profound impact it promises to deliver in telecom space.

The Potential of Private Networks

Network slicing is one of the most promising functionalities of 5G for its new revenue possibilities. Network slicing allows spectrum owners to create private wireless networks which they can then rent or lease to enterprise users for short or long durations. By some estimates, the private 5G network market could reach up to $30 billion in annual revenue.

During the 2023 Super Bowl, equipment maker JMA designed and built a private network for broadcaster Fox using Dish Network spectrum and CP Communication as the systems integrator. Though this network was mainly used for internal production communications (e.g., producers and stage managers talking to camera operators), in the future it could use private networks like these for the transmission of audio and video content. 

But the applications for private networks spans industries. For example, in rural areas spectrum owners can allocate underutilized spectrum to autonomous farm equipment. Or in financial districts that are crowded during the day with workers (and their devices) but then empty out in the evenings and at night, spectrum owners can rent portions of the spectrum to commercial IoT equipment to backhaul the day’s sensor data or fan out new algorithms wirelessly to that same equipment. 

In order to achieve this vision, communication service providers (CSPs) have to ensure that even as they explore new 5G business models they continue to provide minimum levels of service quality or else face increased regulatory scrutiny. In order to offer both a flexible network that can dynamically allocate spectrum bandwidth based on demand while also ensuring service levels to consumers, CSPs will need to deploy low latency network analytics to thousands of edge locations to understand and predict real-time network quality. 

The Two Main Requirements for Running ML at the Network Edge

CSPs are not new to data science. According to Analytics Insight, the telecommunications sector is the largest investor in data science, already comprising about a third of the big data market. That spend is expected to double from 2019 to 2023, reaching over $105 billion in 2023.

That said, taking complex machine learning models trained in cloud or on-prem environments and deploying them at the edge is still relatively new. The dev environment can be so different from the live production environment that it could take months of reengineering before a single model can be successfully deployed at the edge. And once live, the data scientists who developed the model often have no view into the ongoing performance of their model until something goes wrong.

To run a successful edge AI program for private networks, CSPs will need to plan for two major requirements:

1) Monitoring the ongoing accuracy of live models:

Data science teams can focus so much on just deploying models and running models to the edge, that they forget to think about the day after a model is deployed. The network environment is continually changing (e.g., a new class of devices can come online) and so past network quality and demand models quickly degrade. Does your edge AI operations have the ability to monitor performance, push updated models, run A/B tests across portions of your fleet or do shadow testing?

Ongoing model monitoring is critical for detecting drift and providing feedback to data scientists in order to optimize model performance in production.

2) Edge environment compute constraints:

Most modern 5G network architectures rely on tens of thousands of small cells with the goal of centralizing as much of the network management functions to the cloud while limiting edge applications to those that absolutely require low latency (e.g., optimizing local bandwidth allocation). What this means for deploying edge AI is a highly constrained device in terms of compute and power, making it difficult to run complex ML models. There are several different methods for reducing the compute load required by your machine learning models, like quantization & pruning and knowledge distillation, but perhaps the simplest is to use a specialized engine for running ML at the edge.

Commercializing the Potential of Edge AI

Overall, CSPs need to reconsider their approach to machine learning not as a one-time deployment of a model, but rather as a cycle of deploying, managing and monitoring models based on ongoing performance. There is amazing potential for 5G to generate new revenue streams, but use cases like private networks rely on low latency ML models deployed at the edge. Given their investment in data science, CSPs will often default to building in-house production ML solutions but soon find these solutions are more costly to maintain, have lower performance and fail to scale. 

At Wallaroo we have been working with several players in the telecommunications space to operationalize ML in an agile, scalable manner for all sorts of network use cases. If you are interested to see what we can do for you, reach out to us to request a personalized demo.  

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