US Patent Application 18202041. COMPUTE-AWARE RESOURCE CONFIGURATIONS FOR A RADIO ACCESS NETWORK simplified abstract

COMPUTE-AWARE RESOURCE CONFIGURATIONS FOR A RADIO ACCESS NETWORK

Organization Name

Microsoft Technology Licensing, LLC==Inventor(s)==

[[Category:Manikanta Kotaru of Kenmore WA (US)]]

[[Category:Arjun Varman Balasingam of San Jose CA (US)]]

[[Category:Paramvir Bahl of Bellevue WA (US)]]

COMPUTE-AWARE RESOURCE CONFIGURATIONS FOR A RADIO ACCESS NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18202041 titled 'COMPUTE-AWARE RESOURCE CONFIGURATIONS FOR A RADIO ACCESS NETWORK

Simplified Explanation

This patent application is about using a machine learning model to allocate resources in a radio access network (RAN) based on compute power needs. Here are the key points:

• The patent application focuses on allocating RAN resources among RAN slices using a machine learning model.
• The machine learning model determines the optimal RAN resource configuration based on compute power needs.
• This approach improves RAN resource allocation and compute power requirements, even in situations with changing or unknown network conditions.
• A prediction engine receives communication parameters and/or requirements associated with service-level agreements (SLAs) for applications running on a device connected to the RAN.
• The RAN generates one or more RAN resource configurations for implementation among RAN slices.
• If there is a change in network conditions or SLA requirements, the optimal RAN configuration is determined based on the required compute power.

Original Abstract Submitted

Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.