Wireless Sensing Windowing for Noise-Resilient Joint Spectrum Detection
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Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in improving target object sensing accuracy due to noise and interference in RF sensing signals, which affect the reliability and precision of detection and parameter estimation.
Innovation Solution
Implementing a system that utilizes different windowing functions to process RF sensing signals, allowing for the calculation of joint spectra and reports, thereby reducing noise and interference in target detection and parameter estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RF sensing signals are used for target detection and parameter estimation, then sensing capability is provided, but noise and interference in the measured sensing spectrum reduce detection accuracy
Solution Approach 1:
The patent introduces joint spectrum as an intermediary representation that processes the raw sensing spectrum. By transforming the measured sensing signals into joint spectrum domain, the system can separate target-related information from noise and interference, thereby improving detection accuracy without directly filtering the original signals
Solution Approach 2:
The patent applies windowing functions to modify the parameters of the joint spectrum calculation. By changing the windowing function parameters (such as rectangular, Hamming, or Dolph-Chebyshev windows), the system optimizes the trade-off between main lobe width and side lobe levels, thereby enhancing target detection accuracy while suppressing noise and interference
2Measurement precision
If multiple windowing functions are applied to calculate joint spectra, then target detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies multiple windowing functions to calculate multiple joint spectra, which is an excessive action beyond the single-window approach. This partial redundancy allows the system to select or combine results from different windowing functions to improve parameter estimation accuracy, accepting the additional computational complexity as a trade-off for enhanced performance
Data Source
AI summary
A sensing node may receive a set of sensing signals. The sensing node may calculate a first joint spectrum based on a first windowing function and the set of sensing signals. The sensing node may calculate a second joint spectrum based on a second windowing function and the set of sensing signals, wherein the first windowing function is different than the second windowing function. The sensing node may transmit a first joint spectrum report based on the first joint spectrum and a second joint spectrum report based on the second joint spectrum to a sensing entity. The sensing node may estimate a parameter of a target object based on an optimal spectrum ID received from the sensing entity, where the optimal spectrum ID may be associated with at least one of the first joint spectrum or the second joint spectrum.