ML Interference Suppression for RF Signal Processing
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Solution Overview
Problem
Current anti-jamming techniques are ineffective in complex environments with multiple jammers, particularly when jammers have unknown properties or completely overlap the desired signal, leading to interference that cannot be easily isolated in various domains.
Innovation Solution
A trained machine learning model is used to extract and remove interference components from radio frequency signals, including jammers and noise, by applying series of weights, and can operate in multiple domains such as time, frequency, or wavelet domains, allowing for simultaneous removal of multiple jammer types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spatial techniques (multiple antennas) are used to reduce interference, then interference suppression capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical spatial processing mechanisms (multiple antennas and beamforming hardware) with a machine learning-based signal processing system. The ML model processes signals in the digital domain to achieve interference suppression without requiring complex physical antenna arrays, thus reducing device complexity while maintaining suppression capability.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the received signal and the output signal. This ML intermediary learns to identify and suppress interference components through training, acting as a smart filter that adapts to different interference types without requiring complex physical systems.
2Reliability
If narrowband notching is used to remove jammer signals, then narrowband interference is reduced, but wideband jammers cannot be removed
Solution Approach 1:
The patent employs a universal machine learning model that can handle multiple types of jammers (narrowband, wideband, frequency-hopping, chirp) through a single unified approach. The model is trained on diverse jammer types and automatically adapts to the specific interference present in the input signal, providing versatile interference suppression across different jammer categories without requiring separate processing chains.
Solution Approach 2:
The patent uses a dynamic machine learning model that adapts its processing characteristics based on the input signal properties. Rather than using fixed frequency-domain notching, the ML model dynamically adjusts its interference suppression strategy based on the learned patterns of different jammer types, enabling effective handling of both narrowband and wideband interference through adaptive processing.
3Reliability
If constant envelope techniques are used for FM chirp jammer mitigation, then single chirp jammer is reduced, but multiple chirp jammers with multipath cannot be handled
Solution Approach 1:
The patent applies preliminary training action to the machine learning model using simulated signals that represent complex channel environments with multiple jammers and multipath effects. This pre-training prepares the model to handle realistic interference scenarios, enabling it to adapt to complex environments without requiring constant envelope assumptions during actual operation.
Solution Approach 2:
The patent replaces constant envelope-based mitigation techniques with a machine learning-based approach that operates in the signal domain rather than relying on waveform property assumptions. This substitution allows the system to handle complex scenarios with multiple jammers and multipath without being constrained by the constant envelope requirement, as the ML model learns to identify interference based on patterns rather than envelope properties.
4Measurement precision
If domain-specific processing is used to isolate jammer signals, then detection precision is improved for known jammer types, but effectiveness drops when jammer properties are unknown
Solution Approach 1:
The patent implements a self-learning machine learning model that automatically adapts to unknown jammer types through its training process. The model learns to identify interference patterns and suppression strategies without requiring explicit knowledge of jammer properties, enabling it to handle unknown jammers effectively by self-adjusting based on the input signal characteristics.
Solution Approach 2:
The patent uses parameter changes in the machine learning model during training to adapt to different jammer types and conditions. By adjusting the model's internal parameters through training on diverse signals, the system achieves high detection precision for both known and unknown jammer types, as the learned parameters capture the essential characteristics of various interference patterns.
Data Source
AI summary
Methods, systems, and computer program products are described for automatically reducing interference within received signals. A first radio frequency (RF) signal having (i) a desired component and (ii) an interference component with a noise component and a jammed component is received. A trained machine learning (ML) model extracts, from the RF signal, the jammed component and a portion of the noise component. The trained ML model generates and outputs a second RF signal comprising the desired component and a reduced noise component. The reduced noise component has the portion of the noise component removed. The jammed component is removed from the second RF signal.