Radio Sensing Configuration Using CSI Domain Reduction
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Solution Overview
Problem
Current radio sensing technologies in wireless communications systems face inaccuracies and processing latency due to the lack of utilization of radio sensing intelligence for processing data, leading to inefficiencies and increased power consumption.
Innovation Solution
The implementation of radio sensing nodes configured with a priori known features and CSI-based procedures, including CSI domain translation and reduction, to enhance sensing accuracy and reduce processing complexity by focusing on relevant signal spaces and distributing computation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radio sensing processes all available signal data without selective processing, then comprehensive sensing coverage is achieved, but processing complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the sensing data processing into multiple stages: initial signal reception, selective domain translation (time-frequency-spatial) based on object characteristics, and targeted feature extraction. This segmentation allows the system to process only relevant portions of the signal data in detail while coarsely processing or skipping irrelevant portions, thereby reducing overall processing complexity while maintaining sensing accuracy for objects of interest.
Solution Approach 2:
The patent applies local quality by adapting the processing intensity and method to different regions of the signal space based on where objects are likely to be present. Instead of uniformly processing all signal data with the same complexity, the system concentrates computational resources on specific time-frequency-spatial regions that contain relevant sensing information, reducing overall processing complexity while maintaining high sensing accuracy in critical regions.
2Measurement precision
If radio sensing processes all available signal data without selective processing, then comprehensive sensing coverage is achieved, but power consumption increases
Solution Approach 1:
The patent segments the sensing data processing into multiple stages: initial signal reception, selective domain translation (time-frequency-spatial) based on object characteristics, and targeted feature extraction. This segmentation allows the system to process only relevant portions of the signal data in detail while coarsely processing or skipping irrelevant portions, thereby reducing overall processing complexity while maintaining sensing accuracy for objects of interest.
Solution Approach 2:
The patent applies local quality by adapting the processing intensity and method to different regions of the signal space based on where objects are likely to be present. Instead of uniformly processing all signal data with the same complexity, the system concentrates computational resources on specific time-frequency-spatial regions that contain relevant sensing information, reducing overall processing complexity while maintaining high sensing accuracy in critical regions.
3Loss of information
If radio sensing transmits all measured data for reporting, then complete sensing information is provided, but reporting overhead increases
Solution Approach 1:
The patent extracts only the essential sensing information from the processed signal data for reporting. Instead of transmitting all measured data, the system identifies and extracts key features such as object presence, location, velocity, and relevant characteristics based on the sensing configuration and objects of interest. This extraction process maintains information completeness for the intended purpose while significantly reducing reporting overhead.
4Measurement precision
If radio sensing processes all signal data without domain translation, then all potential objects are detected, but processing time increases
Solution Approach 1:
The patent performs preliminary domain translation of the signal data into time, frequency, and spatial domains before detailed processing. This preliminary action organizes the data in a structure that enables faster identification of objects of interest and their characteristics. By pre-organizing the data in multiple domains, the system reduces the time required for subsequent detailed analysis while maintaining detection accuracy for all potential objects.
Data Source
AI summary
Various aspects of the present disclosure relate to methods, apparatuses, and systems that support configuration for radio sensing. For instance, implementations provide configuration of radio sensing nodes including information elements defining a priori known features of objects and/or scenarios of interest for radio sensing. Further, channel state information (CSI)-based procedures are described including CSI domain translation and CSI domain reduction. Implementations also include the introduction of conditioned reference signal received power (RSRP), reference signal reception quality (RSRQ), and reference signal strength indicator (RSSI) measurements within a configured region of a defined CSI domain as part of radio sensing.