METHOD FOR TRANSMITTING MEASUREMENT REPORT AND AN APPARATUS THEREOF

Smart overview of the Invention

The patent application describes a method for transmitting a measurement report (MR) in a radio communication system. It involves collecting radio signal measurement data through reference signals (RSs) over a set period. This data is used to predict radio signal measurement information via a trained model. The prediction helps detect a pre-event that determines whether an MR event should be triggered. Upon detection, the MR is sent to a base station (BS).

Background

Within radio communication systems, especially with the advent of 5G, managing radio resources effectively is crucial. This management ensures network quality, optimizes resources, and enhances user experience. The introduction of new technologies like beam management and dynamic resource sharing in recent standards has increased the importance of sophisticated radio resource management (RRM) algorithms.

Technical Details

The method leverages a trained model, possibly utilizing AI/ML, to predict future radio signal measurements. This prediction is based on data obtained from RSs over a defined time interval. The process involves identifying a pre-event that could trigger an MR event. If conditions are met, the MR is generated based on predicted Reference Signal Received Power (RSRP) values and transmitted to the BS.

Operational Mechanism

• RSs are received at a first time interval to gather measurement data.
• Prediction of RSRP is done at a second, shorter time interval to enhance accuracy.
• MR transmission is contingent on detecting a pre-event, which is based on predicted RSRP values at designated timepoints.
• Transmission includes setting information indicating whether RSRP is predicted or directly measured.

Applications and Benefits

The approach enhances the reliability of MR-related operations by improving AI/ML-based RSRP prediction stability. It allows the terminal to efficiently manage power and reduce delays in environments with high AI/ML accuracy. Conversely, in less reliable settings, the method minimizes errors in MR transmissions, ensuring robust performance.