Predictive Beam Management Using Beam Set Correspondence
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Solution Overview
Problem
Existing beam management and prediction techniques in 5G NR systems result in significant overhead and power consumption due to frequent reception of reference signals and excessive beam sweepings, which affect the efficiency and latency of user equipment (UE) mobility.
Innovation Solution
The UE obtains a configuration from a network entity indicating a first set of resources, identifies information between this set and a second set of resources, and outputs a channel measurement report with predicted channel metrics, thereby reducing overhead and power consumption while improving beam prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent reception of reference signals and excessive beam sweepings are performed for beam management, then beam prediction accuracy is improved, but overhead and power consumption increase significantly
Solution Approach 1:
The network entity pre-configures correspondence information between first set resources (actual reference signals) and second set resources (virtual reference signals) before beam prediction is needed. This preliminary setup allows the UE to perform predictions without requiring actual transmission of all reference signals, thereby reducing power consumption while maintaining prediction accuracy through the pre-established resource mappings.
Solution Approach 2:
The patent uses virtual reference signals (second set resources) that are copies or representations of actual reference signals (first set resources). The UE measures channel metrics on the virtual resources based on correspondence information, creating a simplified model that avoids the need to receive and process all actual reference signals, thus reducing overhead and power consumption while preserving beam prediction capability.
2Measurement precision
If frequent reception of reference signals and excessive beam sweepings are performed for beam management, then beam prediction accuracy is improved, but system overhead increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for beam prediction by establishing correspondence relationships between first set resources and second set resources. Instead of transmitting and processing all reference signals, the system extracts and transmits only the configuration indicating resource correspondences, significantly reducing overhead while enabling the UE to derive beam prediction information through local measurements and calculations.
Solution Approach 2:
The correspondence information configuration serves multiple functions simultaneously: it defines resource mappings, enables channel measurement, supports beam prediction, and reduces overhead. This multi-functional approach allows a single configuration mechanism to achieve beam management objectives without requiring separate signaling for each function, thereby minimizing overall system overhead.
3Loss of information
If traditional beam management techniques are used, then complete channel information is obtained, but processing time and latency increase
Solution Approach 1:
The network entity pre-configures correspondence information between resource sets before the UE needs to perform beam prediction. This preliminary configuration allows the UE to immediately use the established mappings for channel measurements and predictions without waiting for extensive reference signal transmissions, thereby reducing processing time while ensuring complete channel information is obtained through the configured resource relationships.
Data Source
AI summary
Certain aspects relate to correspondence information between beam sets for beam prediction. For example, an apparatus may obtain, from a network entity, a configuration indicating a first set of resources. The apparatus may identify information between the first set of resources and a second set of resources. The apparatus may output, for transmission to the network entity, a channel measurement report indicating one or more predicted channel metrics for at least the second set of resources, wherein the one or more predicted channel metrics are predicted based on the information.


