US Patent Application 17714977. SERVICE DISCOVERY AND SESSION ESTABLISHMENT FOR MACHINE-LEARNING-BASED BEAM PREDICTION IN WIRELESS COMMUNICATIONS simplified abstract
SERVICE DISCOVERY AND SESSION ESTABLISHMENT FOR MACHINE-LEARNING-BASED BEAM PREDICTION IN WIRELESS COMMUNICATIONS
Inventors
Kyle Chi Guan of New York NY (US)
Himaja Kesavareddigari of Bridgewater NJ (US)
Qing Li of Princeton Junction NJ (US)
Kapil Gulati of Belle Mead NJ (US)
Junyi Li of Fairless Hills PA (US)
Hong Cheng of Basking Ridge NJ (US)
SERVICE DISCOVERY AND SESSION ESTABLISHMENT FOR MACHINE-LEARNING-BASED BEAM PREDICTION IN WIRELESS COMMUNICATIONS - A simplified explanation of the abstract
- This abstract for appeared for patent application number 17714977 Titled 'SERVICE DISCOVERY AND SESSION ESTABLISHMENT FOR MACHINE-LEARNING-BASED BEAM PREDICTION IN WIRELESS COMMUNICATIONS'
Simplified Explanation
This abstract discusses the use of dynamic and interactive machine learning techniques for managing beam interference in wireless communication networks. It describes how a user equipment (UE) device can transmit a discovery message to find machine learning services, and then send a session request to establish a data service session with a network node based on the discovered services and other factors such as extracted features. The network node receives the ML discovery data and extracted features, and combines this information with other intelligent network devices to predict beam blockages during the data service session. The network node can adjust its predictions by changing the timing and direction of communications between network entities.
Original Abstract Submitted
Aspects of dynamic and interactive machine learning and feature extraction techniques for performing beam interference management are disclosed. In one aspect, upon entering a coverage area, a UE may transmit a discovery message including one or more machine learning (ML) services for ML service discovery. Based on the ML service discovery, the UE may transmit a session request to establish a data service session between the UE and a network node based on the ML service discovery and other criteria such as extracted features. The network node may receive the ML discovery data and extracted features and aggregate this information with other intelligent network devices to enable the network node to predict a beam blockage during the ML inference data service session. The network node can adapt beam blockage predictions by changing the timing and direction of communications between network entities.
