US Patent Application 17825596. WIRELESS PARAMETER LIMITS FOR PREDICTED VRAN RESOURCE LOADS simplified abstract
Contents
WIRELESS PARAMETER LIMITS FOR PREDICTED VRAN RESOURCE LOADS
Organization Name
Microsoft Technology Licensing, LLC==Inventor(s)==
[[Category:Yu Yan of Kirkland WA (US)]]
[[Category:Anuj Kalia of Newcastle WA (US)]]
[[Category:Sanjeev Mehrotra of Kirkland WA (US)]]
[[Category:Paramvir Bahl of Bellevue WA (US)]]
WIRELESS PARAMETER LIMITS FOR PREDICTED VRAN RESOURCE LOADS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17825596 titled 'WIRELESS PARAMETER LIMITS FOR PREDICTED VRAN RESOURCE LOADS
Simplified Explanation
- The patent describes a method for efficiently using computing resources in a virtualized Radio Access Network (vRAN). - A trained neural network model is used to predict the resource load for processing data traffic in wireless communication channels served by the vRAN. - The data traffic processing includes tasks like PHY data processing and MAC processing for a 5G RAN. - Based on the predicted resource load, computing resources are allocated for the data traffic processing. - Wireless parameter limits are determined for the wireless communication channels, which restrict the utilization of the allocated computing resources. - The trained neural network model helps in setting these limits, such as the maximum number of radio resource units per timeslot or the maximum MCS index. - By using these wireless parameter limits, the method aims to reduce load spikes that can lead to violations of real-time deadlines for the data traffic processing.
Original Abstract Submitted
A method for utilizing computing resources in a vRAN is described. A predicted resource load is determined for data traffic processing of wireless communication channels served by the vRAN using a trained neural network model. The data traffic processing comprises at least one of PHY data processing or MAC processing for a 5G RAN. Computing resources are allocated for the data traffic processing based on the predicted resource load. Wireless parameter limits are determined for the wireless communication channels that constrain utilization of the allocated computing resources using the trained neural network model, including setting one or more of a maximum number of radio resource units per timeslot or a maximum MCS index for the wireless parameter limits. The data traffic processing is performed using the wireless parameter limits to reduce load spikes that cause a violation of real-time deadlines for the data traffic processing.
