Funding opportunity for 5G to push AI to the Edge

So what is 5G exactly?

With the announcement of the first 5G European trial and other field tests that are happening worldwide, interest around 5G is growing. Although 5G deployments are not planned to be ready until at least the next Olympics in 2020, companies are starting to look at what opportunities this new technology can open for them. But what exactly is it 5G? For a start, is much more than just a faster 4G, it is a step change in communications technology and infrastructure. It will allow a range of applications that are currently not practical with existing technologies.

Interestingly, AI is not only a key enabler for how 5G will function, but important for new applications that will benefit from the future networks.

What will it mean for existing applications?

Most importantly, with more bandwidth, come a new breed of data hungry applications that need feeding. At the moment it is not clear specifically how much more, as it is still quite early in standardisation, but there are high figures such as 10Gbps being mentioned. This will be a great boost for applications such as 4k (or even 8k) video streaming and for gaming.

In the past technologies such as LTE used in 4G could theoretically reach high numbers, but, in practice, most consumers never see these kind of data rates when walking down the high street. 5G requires mobile operators to do more within their internal network infrastructure to manage the available bandwidth based on the needs of the application. Technologies such as SDN and NFV are transforming how these needs are met and are likely to underpin the implementation of networks to feed the bandwidth hogging applications of the future. AI is playing a role in helping operators to effectively manage their networks intelligently, adapting networks in response to demand patterns.

New application areas enabled by 5G

What is in many ways more interesting is the low-latency applications that will be supported by 5G, such as realtime control of drones and cars, and highly responsive AR and VR applications. These needs cannot be adequately served by existing infrastructure, as the delay in data being sent before it is acted upon is too great for these types of applications and devices.

The other end of the scale are the millions of device that will need very low bandwidth and are not concerned with latency, the Internet Of Things devices. The small, and sometimes not so small, ‘things’ that will use 5G and likely a myriad other technologies such as LoRa, NWave, Sigfox or NB-IoT to communicate. 5G will need to deal with the needs of the data hungry devices while still supporting a massive number of connected things while trying to balance these very different needs.

How is AI relevant to 5G?

One thing is clear, 5G will enable the carrying of more and more data from devices, applications and sensors. This will place even higher demands on systems to handle the flood of data, and to store, analyse and process this data. This will have an impact on applications that need to mine data or to take action in response to data, the implications for the ML and AI world is massive and research interest is gaining momentum.

One particular area of interest is the trend towards moving processing closer to the source and consumer of the data, even to the base station itself. The field of Mobile Edge Computing (MEC) is a growing area of interest and Machine Learning is playing a huge part in this domain. Moving processing closer to the applications and into the network has huge benefits in improving latency and overall application performance. Obvious benefits such as content caching are clear, moving popular content to a small base station to support a commuters on a busy train for example.

Mobile Edge Computing can open new opportunities for AI

A major benefit of using MEC is in reducing data that needs to be sent to a backend server. One way that it achieves this can be to pre-processing data to determine what needs to be sent to a backend server. As a practical example, Nokia have developed a system running on their Liquid MEC product (Intel also has a similar offering) that analysed video streams recorded on a number of surveillance cameras, the data was then processed by a neural network executing on a MEC server, deployed at the base station. The MEC application examined the video streams, classified what were normal and abnormal patterns, and then only needed to send the stream to the backend when a potential security issue was identified.

This type of application approach is different to centralised cloud approaches and has the advantage of only needing to deal with a localisation of data. In some cases this local area could be limited to an individual shop, building, stadium, event venue, factory, motorway section or train. An MEC application using AI can consider rules that are specific to the conditions that exist in a particular area and time frame., while sharing data with other MEC applications and traditional cloud hosted services.

Forrester has identified MEC as an emerging area of innovation, with potential growth and new opportunities for AI companies to process data to meet the demanding needs of a new breed of applications and devices in the future. Big hitters such IBM, Intel, Cisco and Nokia, Ericsson and others are already creating solutions in this space, however MEC is an open standard. Find out more about it you can find more information from ETSI including a list of demonstration applications using MEC.

Triangle 5G project

In order to help companies to determine what 5G may be able to do for their applications and devices, UCL is helping deliver a new European Horizon 2020 funded project named TRIANGLE, the focus of which is helping support the development of new mobile applications and devices designed to operate in the future 5G mobile broadband networks.

The support framework will include testbeds which will comprise test equipment and test software, formal test specifications and test methodology and will exploit existing FIRE facilities. In addition to testing applications and devices, the project aims to extend and improve the testbed by funding extensions for adding new capabilities, such as Mobile Edge Components.

The primary motivation of the TRIANGLE project is to promote the testing and benchmarking of mobile applications and devices in Europe as the industry moves towards 5G and to provide a pathway towards certification in order to support qualified mobile developments.

How can I get funding from Triangle?

UCL will help coordinate an Open Call for companies to apply for funding in the region of £25,000 for projects using elements of 5G that can make a significant impact to their products or services. Qualifying applicants will need to identify appropriate uses for 5G technology that should be aligned with recognised 5G Use Cases. A detailed list of identified key 5G Use Case are available here.

The first TRIANGLE Open call is open until October 31st and potential applicants can apply here. Additional Open Calls will be available in early 2017.

If anyone has further questions on any of the points raised in this article, please feel free to contact me.

Note the featured image for this article is taken from

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