SSB Measurement Prediction for Low-Latency Beam Selection
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Solution Overview
Problem
Existing wireless communication systems face increased overhead and latency in beam management procedures due to the need for UEs to measure and select beams from a large number of synchronization signal blocks (SSBs), which can be mitigated by implementing a machine learning model to predict measurements based on a subset of SSBs using multi-dimensional indices.
Innovation Solution
A machine learning model is employed by UEs to predict measurements of a set of SSBs based on a subset of SSBs, utilizing multi-dimensional indices for beam parameters such as azimuth and elevation directions, beam width, and peak gain, reducing the need for exhaustive measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UEs measure and select beams from a large number of SSBs, then beam selection accuracy is improved, but signaling overhead and processing time increase
Solution Approach 1:
The network pre-calculates and provides beam mapping relationships and SSB parameter information to UEs before actual beam management operations. This allows UEs to predict measurements of remaining SSBs based on measurements of a subset of SSBs, reducing the need to measure all SSBs and thereby decreasing beam management latency while maintaining selection accuracy
Solution Approach 2:
A machine learning model serves as an intermediary between SSB measurements and beam selection decisions. The model takes measurements from a subset of SSBs and predicted beam information as input, then outputs predicted measurements for remaining SSBs, enabling accurate beam selection without exhaustive measurement of all SSBs
2Measurement precision
If UEs measure and select beams from a large number of SSBs, then beam selection accuracy is improved, but signaling overhead increases
Solution Approach 1:
The patent extracts and provides only the essential beam management information needed for accurate beam selection, rather than transmitting complete measurement data for all SSBs. The network provides a compact representation including beam mapping relationships and key SSB parameters, which UEs use with the ML model to infer measurements for remaining SSBs, thereby reducing signaling overhead while maintaining selection accuracy
Solution Approach 2:
The patent changes the parameter representation from complete SSB measurement sets to a compressed form using ML model inputs. By providing beam mapping relationships and key parameters instead of full measurement data, the system reduces the amount of information that needs to be signaled while enabling accurate beam selection through prediction
3Reliability
If UEs measure a large number of SSBs, then beam selection reliability is improved, but power consumption increases
Solution Approach 1:
Instead of requiring UEs to measure all SSBs, the patent enables reliable beam selection by measuring only a subset of SSBs and using an ML model to predict measurements for the remaining SSBs. This partial measurement approach, combined with prediction, maintains beam selection reliability while significantly reducing the power consumption associated with exhaustive measurement
Data Source
AI summary
Methods, systems, and devices for wireless communications are described for a machine learning (ML) model to predict measurements of synchronization signal blocks (SSBs). A user equipment (UE) may receive an indication of a serving cell configuration information element including a multi-dimensional field indicating a set of multi-dimensional indices associated with a first set of SSBs, where a first dimension of a multi-dimensional index indicates a first beam parameter and a second dimension indicates a second beam parameter. The UE may predict one or more measurements associated with a second set of SSBs based on parameters and measurements of the first set of SSBs. The UE may transmit a message indicating a beam for communications between the UE and a network entity based on the one or more measurements associated with the first set of SSBs and the predicted one or more measurements associated with the second set of SSBs.