Temporal RSRP Beam Prediction for Faster Beam Reporting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional beam management procedures in wireless communication are time-consuming and resource-intensive due to the need to measure and report on all beams, which can be optimized using predicted beam reporting techniques.
Innovation Solution
Implementing artificial intelligence and machine learning to predict the best beams based on measured and unmeasured beam qualities, allowing for efficient reporting of future beam qualities and qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all beams are measured and reported in traditional beam management procedures, then complete beam quality information is obtained, but time consumption and resource utilization increase excessively
Solution Approach 1:
The system performs preliminary measurements on a subset of beams and uses machine learning models to predict the quality of remaining beams in advance, rather than measuring all beams completely. This preliminary action on partial data enables time-efficient predictions while maintaining measurement precision through AI-based inference.
Solution Approach 2:
Instead of directly measuring all beams, the system creates predictive copies of beam quality information using machine learning models trained on partial measurement data. These predicted beam quality copies replace the need for exhaustive actual measurements, reducing time consumption while preserving information completeness.
2Measurement precision
If all beams are measured and reported in traditional beam management procedures, then complete beam quality information is obtained, but resource utilization increases excessively
Solution Approach 1:
The system extracts and measures only the essential subset of beams that are most likely to be optimal, then uses machine learning to infer the quality of remaining beams. This extraction approach obtains complete beam quality information through intelligent sampling rather than exhaustive measurement, significantly reducing resource utilization.
Solution Approach 2:
The machine learning model serves itself by learning from partial measurement data and automatically generating predictions for unmeasured beams. This self-service capability eliminates the need for resource-intensive complete measurements, as the system uses its learned knowledge to fill in missing beam quality information.
3Productivity
If beam management procedures are simplified to reduce time consumption, then time efficiency improves, but measurement precision and beam quality information completeness deteriorate
Solution Approach 1:
The system changes the parameter representation from direct beam quality measurements to machine learning model predictions. This parameter transformation allows the system to work with simplified input data (partial measurements) while generating comprehensive output information (predicted beam qualities), thereby maintaining measurement precision despite procedural simplification.
Solution Approach 2:
The machine learning model acts as an intermediary between partial beam measurements and complete beam quality information. This intermediary processes the simplified measurement input and generates comprehensive beam quality predictions, enabling time-efficient procedures to maintain measurement precision through intelligent data transformation.
Data Source
AI summary
Beams may be predicted. One or more beams may be determined to be the best beams among a set of beams. For example, in downlink (DL) Transmit ( ) Tx beam prediction, reference signals (RSs) for selected beams may be transmitted and qualities of other beams may be estimated based on measurements of the selected beams. Best beams may be determined based on measured beams, unmeasured beams, or any appropriate combination thereof. Reference signal received power (RSRP) may be used to predict best beams.