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QoS, traffic steering, dimensioning, security. Use case descriptions should include the following aspects: Nature of input data e. Possible examples of actions undertaken by the consuming NF or AF, resulting from these analytics. NWDAF represents operator managed network analytics logical function. NWDAF provides network analytics information i. This information is not subscriber specific. The PCF may use that data in its policy decisions. NWDAF functionality for non-slice-specific analytics information is not supported in this Release of the specification. The 3GPP standards group is developing a machine learning function that could allow 5G operators to monitor the status of a network slice or third-party application performance. Speaking here in Madrid, Manning said the NWDAF was still in the "early stages" of standardization but could become "an interesting place for innovation. You could also look at application performance. The format of the analytics information that it produces might also be standardized, says Manning. Such technical developments might help operators to provide network slices more dynamically on their future 5G networks. Generally seen as one of the most game-changing aspects of 5G, the technique of network slicing would essentially allow an operator to provide a number of virtual network services over the same physical infrastructure. For example, an operator could provide very high-speed connectivity for mobile gaming over one slice and a low-latency service for factory automation on another -- both reliant on the same underlying hardware. However, there is concern that without greater automation operators will have less freedom to innovate through network slicing. In a Madrid presentation, Chappell said that more granular slicing would require "highly agile end-to-end automation" that takes advantage of progress on software-defined networking and network functions virtualization. Caroline Chappell's talk is available here whereas Serge Manning's talk is embedded below: They use vast amounts of hardware, rely on complex software, and are physically distributed over land, underwater, and in orbit. They increasingly provide essential services that underpin almost every aspect of life. Managing networks and optimising their performance is a vast challenge, and will become many times harder with the advent of 5G. The 4th Annual CW Technology Conference will explore this challenge and how Machine Learning and AI may be applied to build more reliable, secure and better performing networks. Is the AI community aware of the challenges facing network providers? Are the network operators and providers aware of how the very latest developments in AI may provide solutions? I am hoping to see some of this blog readers at the conference. Looking forward to learning more on this topic amongst others for network automation.
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