Adaptive Sample Quantization for RF Interference Mitigation
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Solution Overview
Problem
Existing RF receiver systems, such as GPS receivers, face challenges in detecting weak signals due to noise and interference, leading to data loss or suppression due to limited dynamic range and automatic gain control (AGC) mechanisms.
Innovation Solution
An adaptive sample quantization system that includes an antenna, high-resolution analog-to-digital converter (ADC), and a quantizer that generates output samples with reduced bit depth based on interference analysis, using histogram-based thresholding and blanking control to mitigate interference and preserve signal fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If AGC circuitry reduces analog dynamic range to fit ADC dynamic range, then interference is suppressed, but encoded data is lost or severely limited
Solution Approach 1:
The system dynamically adjusts the quantization threshold based on the statistical properties (histogram) of the received signal samples. Instead of using a fixed threshold, the quantizer adapts its threshold level to match the actual signal distribution, allowing it to preserve weak signal data while still suppressing interference effectively.
Solution Approach 2:
The invention changes the quantization parameter (threshold level) based on the signal conditions. By computing the histogram of input samples and determining an optimal threshold from this distribution, the system transforms the fixed-parameter quantization into a variable-parameter system that optimizes the balance between interference suppression and data preservation.
2Device complexity
If low dynamic range ADC is used, then device complexity is reduced, but ability to detect weak signals in presence of interference deteriorates
Solution Approach 1:
The histogram-based threshold determination acts as an intermediary processing step between the ADC and the quantizer. This intermediary computes the statistical distribution of the signal and uses it to set an optimal quantization threshold, thereby enabling a simple ADC to achieve effective weak signal detection through intelligent post-processing.
3Device complexity
If fixed threshold quantization is used, then device complexity is reduced, but adaptability to varying signal conditions deteriorates
Solution Approach 1:
The system implements feedback by computing the histogram of the input signal samples and using this statistical information to determine the quantization threshold. This feedback loop allows the quantizer to adapt to varying signal conditions automatically, improving its versatility without requiring complex external control circuitry.
Data Source
AI summary
One embodiment includes an adaptive sample quantization system. The adaptive sample quantization system includes an antenna to receive a radio frequency (RF) signal having data encoded therein, and analog antenna electronics configured to convert the RF signal to an analog electrical signal. The system also includes an analog-to-digital converter (ADC) directly coupled to the antenna and configured to generate a plurality of consecutive digital samples of the RF signal. The system further includes a quantizer to determine a mode based on the plurality of consecutive digital samples and to select at least one threshold based on the determined mode. The quantizer can further compare each digital sample with the at least one threshold to generate a corresponding one of a plurality of output samples having a reduced number of bits relative to the respective digital sample to substantially mitigate potential interference and facilitate extraction of the data.