ReWiS Wi-Fi Sensing Robustness via Multi-Antenna Diversity
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Solution Overview
Problem
Existing Wi-Fi sensing technologies face challenges in generalizing to multiple environments and improving robustness against noise and interference, leading to poor accuracy when detecting movements or activities in new settings.
Innovation Solution
The Reliable Wi-Fi Sensing (ReWiS) framework leverages multi-antenna, multi-receiver diversity, fine-grained frequency resolution, and few-shot learning to enhance the robustness of Wi-Fi sensing operations, using singular value decomposition to reduce data complexity and employing ProtoNets for rapid adaptation to new environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-antenna low-resolution approaches are used, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The system segments the sensing function across multiple receivers, each equipped with multiple antennas. Instead of using a single complex antenna system, the patent divides the sensing task into multiple independent measurement channels (receivers × antennas), where each channel contributes partial information that is later integrated to achieve high-precision sensing with manageable device complexity at each node
Solution Approach 2:
The patent transitions from single-antenna (1D) to multi-antenna (2D/3D) spatial sampling by deploying receivers at different locations with multiple antennas each. This dimensional expansion in space allows the system to capture channel state information from multiple perspectives, improving measurement precision through spatial diversity without requiring each individual device to be overly complex
2Reliability
If multi-antenna multi-receiver diversity is implemented, then reliability and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent makes standard Wi-Fi receivers perform multiple functions: their primary communication function plus secondary sensing function. By extracting channel state information from existing Wi-Fi signals used for data transmission, the system achieves reliable sensing without adding dedicated sensing hardware, thus improving reliability while avoiding proportional increases in device complexity
Solution Approach 2:
The system uses the Wi-Fi communication signals themselves to carry sensing information. The channel state information that naturally arises during Wi-Fi communication is harvested and repurposed for sensing applications, allowing the system to improve reliability through multi-antenna diversity without requiring separate sensing transmitters or additional complex infrastructure
3Measurement precision
If fine-grained frequency resolution is used, then measurement precision improves, but loss of information increases due to data complexity
Solution Approach 1:
The patent extracts only the essential sensing-relevant features from the full channel state information spectrum. Instead of processing all frequency components with equal detail, the system identifies and extracts the specific frequency-domain characteristics that contain motion and activity information, achieving fine-grained frequency resolution where needed while discarding redundant data to reduce processing burden
Data Source
AI summary
A system and corresponding method sense an environment. The system comprises a wireless transmitter device that transmits a wireless signal through the environment. The system further comprises a plurality of wireless receivers that each 1) receive the wireless signal at a distinct location within the environment via at least one respective antenna and 2) generate a channel state information (CSI) packet indicating a state of a wireless communications channel associated with the wireless signal. The system still further comprises a computing device and classifier. The computing device processes the CSI packets from the plurality of wireless receivers and generates a CSI dataset as a function of the CSI packets processed. The classifier determines at least one class for the CSI dataset. The system and corresponding method improve robustness of sensing operations, such as robustness of Wi-Fi sensing operations to noise and interference.