Sequential AI Models for FHSS Signal Demodulation
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Solution Overview
Problem
Conventional digital signal processor (DSP)-based techniques face challenges in consistently demodulating frequency-hopping spread spectrum (FHSS) signals, struggling with bit recovery due to the signals' shifting carrier frequencies, and require pre-existing knowledge of modulation techniques and frequency-hopping patterns.
Innovation Solution
The use of sequential artificial intelligence/machine learning (AI/ML) models, where a first AI/ML model identifies occupied frequency channels and a second AI/ML model recovers data from baseband signals, enabling effective demodulation of FHSS signals without prior knowledge of modulation techniques or frequency-hopping patterns, even in cases of high signal overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional DSP-based techniques are used to demodulate FHSS signals, then the system has simple architecture and requires pre-existing knowledge of modulation techniques, but the system fails to consistently recover bits due to shifting carrier frequencies
Solution Approach 1:
The demodulation system is divided into multiple specialized AI/ML models, each handling specific aspects of FHSS signal processing. The first model identifies frequency channels, the second generates baseband signals, and the third recovers data. This segmentation allows each model to specialize in a particular task, improving overall reliability while managing complexity through functional decomposition.
Solution Approach 2:
The patent replaces conventional DSP-based mechanical signal processing methods with AI/ML-based computational models. Instead of using traditional filters, mixers, and demodulators that require precise knowledge of modulation techniques, the system uses trained neural networks that can adaptively process FHSS signals without prior knowledge of the specific hopping patterns or modulation schemes.
2Adaptability or versatility
If conventional DSP-based techniques are used, then the system requires pre-existing knowledge of modulation techniques and frequency-hopping patterns, but this limitation reduces adaptability to different signal types
Solution Approach 1:
The AI/ML-based demodulation system is designed to be universal and adaptable to different FHSS signal types without requiring pre-existing knowledge of specific modulation techniques or frequency-hopping patterns. The trained models can process various FHSS signals by identifying occupied frequency channels and recovering data adaptively, making the system versatile across different communication scenarios while capturing complete signal intelligence.
3Reliability
If sequential AI/ML models are used for FHSS demodulation, then consistent bit recovery is achieved, but the computational complexity and processing requirements increase
Solution Approach 1:
The AI/ML models are trained in advance on comprehensive datasets that include various FHSS signal characteristics, frequency-hopping patterns, and modulation techniques. This preliminary training equips the models with the knowledge needed to accurately demodulate FHSS signals during operation, reducing the need for complex real-time computations and lowering the processing power requirements during actual demodulation tasks.
Data Source
AI summary
A method includes using a first trained artificial intelligence/machine learning (AI/ML) model to identify occupied frequency channels associated with one or more incoming signals during multiple time periods, where the one or more incoming signals include a frequency-hopping spread spectrum (FHSS) signal. The method also includes generating one or more baseband signals using portions of the one or more incoming signals associated with the occupied frequency channels. The method further includes using a second trained AI/ML model to recover data from the one or more baseband signals, where the recovered data represents at least a portion of data that is encoded in the FHSS signal.