Compressed Beamforming Report Reconstruction for Wi-Fi Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Wi-Fi sensing systems heavily rely on channel state information (CSI), which is not widely available on commodity Wi-Fi devices due to proprietary chipset designs, limiting their ubiquity and deployment.
Innovation Solution
The method and systems enable general sensing applications using compressed beamforming reports (CBR) by performing channel sounding, sniffing Wi-Fi traffic, and performing multi-path estimation, allowing for the reconstruction of CSI from CBR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Wi-Fi sensing systems rely on channel state information (CSI), then sensing accuracy is improved, but device compatibility deteriorates because CSI is not widely available on commodity Wi-Fi devices
Solution Approach 1:
The patent creates a compressed representation (copy) of the channel state information that can be derived from widely available beamforming reports. Instead of requiring the original full CSI data, the system creates a compressed beamforming report that captures essential channel characteristics in a standardized format accessible on all 802.11ac devices, thereby maintaining sensing accuracy while improving device compatibility
Solution Approach 2:
The patent introduces compressed beamforming reports as an intermediary between the available beamforming feedback data and the required channel state information for sensing applications. This intermediary format serves as a bridge that translates proprietary CSI formats into a standardized representation that can be uniformly processed across different device manufacturers while preserving the essential sensing capabilities
2Adaptability or versatility
If compressed beamforming reports are used for sensing, then device compatibility is improved, but information completeness deteriorates due to compression
Solution Approach 1:
The patent extracts only the essential channel characteristics needed for sensing applications from the full channel state information and encodes them in the compressed beamforming report format. By selectively extracting and encoding only the critical components (such as dominant eigenvalues and eigenvectors), the system maintains device compatibility while minimizing information loss for the specific sensing tasks at hand
3Measurement precision
If proprietary CSI extraction methods are used, then sensing performance is improved on supported devices, but ease of deployment deteriorates due to chipset-specific requirements
Solution Approach 1:
The patent develops a universal sensing framework that operates on the standardized compressed beamforming report format defined in the 802.11ac specification, which is supported by all compliant Wi-Fi devices regardless of manufacturer. This universal approach eliminates the need for chipset-specific extraction code and proprietary interfaces, thereby dramatically improving ease of deployment while maintaining sensing performance through mathematically equivalent processing of the standardized format
Data Source
AI summary
A wireless sensing method and systems based on compressed beamforming reports (CBR) are provided. The method includes performing channel sounding and transmit (TX) beamforming; performing sniffing or extracting information from Wi-Fi traffic; and performing multi-path estimation. The performing multi-path estimation includes performing multi-path modeling with CBR to analyze relationship between signal propagation characteristics and information in CBR by modeling a multi-path channel based on uplink and downlink steering matrices. The performing multi-path modeling with CBR includes performing multi-path modeling with CBR from physical paths to channel state information (CSI) and subsequently from CSI to CBR. The performing multi-path estimation further includes performing maximum likelihood multi-path estimation. The performing maximum likelihood multi-path estimation includes analyzing multi-path fingerprint in CBR, performing fingerprint matching, and performing maximum-likelihood estimation (MLE)-based multi-path reconstruction.