RF Sensing Measurement Report Segmentation for 5G Networks
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently multiplexing and reporting radio frequency (RF) sensing measurements, particularly in 5G and beyond systems, where larger bandwidths and diverse use cases require enhanced spectral efficiency and accurate sensing information, but existing methods struggle with signaling overhead and use-case specific measurement reporting.
Innovation Solution
The implementation of systems and techniques that enable network devices to receive RF sensing resources, determine use-case specific RF sensing measurements, and transmit comprehensive measurement reports to network entities, incorporating advanced methods like machine learning and AI to extract rich sensing information, thereby reducing signaling overhead and improving RF sensing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive RF sensing measurements are collected for multiple use cases, then sensing performance and accuracy are improved, but signaling overhead and message size increase
Solution Approach 1:
The measurement report is segmented into use case-specific components. Each use case (e.g., positioning, radar, beam management) has its own dedicated measurement fields, allowing the network to include only the necessary measurements for each use case. This segmentation enables comprehensive sensing capability while reducing overhead by excluding unnecessary measurement types from the signaling message.
Solution Approach 2:
The measurement report structure is made dynamic and adaptable. The network device can adjust the measurement report content based on the specific use case requirements, including only the necessary measurement parameters for each use case. This dynamic approach allows the system to maintain high measurement accuracy for active use cases while minimizing signaling overhead for inactive or less critical use cases.
2Adaptability or versatility
If multiple RF sensing use cases are supported simultaneously, then system versatility is improved, but device complexity increases
Solution Approach 1:
The network device implements a universal measurement report framework that can handle multiple RF sensing use cases through a common processing architecture. The same basic measurement collection and report generation mechanisms are reused across different use cases (positioning, radar, beam management), reducing the need for separate dedicated processing paths for each use case and thereby managing device complexity while maintaining versatility.
Solution Approach 2:
The processing complexity is segmented by organizing measurements and processing logic into distinct use case modules. Each use case has its own dedicated processing chain, allowing the network device to handle multiple use cases independently and in parallel. This modular segmentation prevents a single complex processing path and enables manageable complexity through structured organization.
3Measurement precision
If use case specific measurement reporting is implemented, then measurement relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary identification and classification of measurement types based on the active use cases before actual measurement collection. By pre-determining which measurements are relevant for each use case, the network device can efficiently filter and select only the necessary measurements during the measurement reporting process, reducing processing time while ensuring measurement relevance.
Solution Approach 2:
The measurement reporting process is made dynamic by adjusting the scope and depth of measurements based on real-time use case requirements. The system can quickly switch between different measurement sets corresponding to different use cases, enabling relevant measurements to be reported efficiently without the overhead of processing all possible measurements. This dynamic adaptation reduces processing time while maintaining high measurement relevance.
Data Source
AI summary
Disclosed are systems, apparatuses, processes, and computer-readable media for wireless communications. For example, a network device can receive one or more radio frequency (RF) sensing resources. The network device can determine RF sensing measurements based on the one or more RF sensing resources. For instance, the RF sensing measurements may be based on at least one use case for RF sensing. The network device can transmit, to a network entity, a measurement report comprising the RF sensing measurements for the at least one use case for the RF sensing.