AI-Assisted Beam Management Using Partial Reference Signal Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing beam management techniques in communication systems face high overhead due to the increasing number of transmit and receive beams, necessitating more efficient methods for beam management.
Innovation Solution
Implementing AI/ML-based beam management that includes receiving and measuring a subset of reference signals, transmitting a channel status report, and predicting future reference signals, utilizing an AI/ML engine for efficient beam prediction and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of transmit and receive beams is increased to improve coverage and capacity, then beam management capability is enhanced, but overhead increases significantly
Solution Approach 1:
The system performs preliminary beam identification by having the UE measure and report only a subset of CSI-RS resources (e.g., 4 out of 16) that are most likely to contain the best beam. This preliminary action reduces the overhead required for complete beam management while still enabling effective beam selection through AI/ML-based prediction of the remaining resources.
2Measurement precision
If all CSI-RS resources are measured to ensure accurate beam identification, then beam selection accuracy is improved, but measurement time and processing overhead increase
Solution Approach 1:
The UE performs preliminary measurements on a selected subset of CSI-RS resources and reports these measurements to the gNB. The gNB then uses AI/ML models to predict the measurements of the remaining resources, thereby achieving accurate beam selection without measuring all resources, thus reducing measurement time while maintaining precision.
Solution Approach 2:
An AI/ML model acts as an intermediary between the measured subset of CSI-RS resources and the complete set of beam selections. The model predicts the measurements of unmeasured resources based on the reported subset, enabling accurate beam identification without direct measurement of all resources, thereby reducing time overhead while maintaining measurement precision.
3Adaptability or versatility
If traditional beam management methods are used to maintain compatibility with existing systems, then system compatibility is preserved, but efficiency decreases due to high overhead
Solution Approach 1:
The solution maintains universality by being compatible with existing NR beam management frameworks and CSI reporting mechanisms. The UE and gNB continue to use standard CSI-RS resources and reporting procedures, while the AI/ML-based subset measurement approach adds efficiency without requiring complete system redesign, thus preserving compatibility while improving productivity.
Data Source
AI summary
A method performed by at least one processor in a user equipment (UE), the method including receiving, from a base station over a channel, a plurality of reference signals corresponding to a state of the channel. The method further including measuring a subset of the received plurality of reference signals. The method further including transmitting, to the base station within a first time interval, a channel status report corresponding to the measuring of the subset of the received plurality of reference signals and a prediction of one more reference signals received by the UE after the first time interval.