Waveform Agnostic Decision Engine for Radio Interference Mitigation
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Solution Overview
Problem
The rapid development of Electronic Attack (EA) techniques by adversaries, particularly with Software Defined Radios (SDRs), outpaces waveform development, making it challenging to detect and mitigate interference across various layers of radio communication systems, leading to potential devastating impacts on Blue Force Communications.
Innovation Solution
The Waveform Agnostic learning-enhanced Decision Engine (WADER) uses a deep learning component with Cross Layer Sensing (CLS) and a trained neural network to classify interference signals and determine mitigation strategies, modifying parameters at the Physical, Medium Access Control, and Network layers to restore communication system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional interference detection methods are used, then the system can detect known interference patterns, but it cannot effectively classify and mitigate sophisticated adversarial electronic attacks that span multiple network layers
Solution Approach 1:
The patent implements a universal deep learning-based classification engine that can handle multiple types of interference signals across different network layers (Physical, MAC, and Network layers) simultaneously. The system uses a unified neural network architecture that processes features from various layers to classify diverse electronic attack techniques, making the system adaptable to both known and unknown interference patterns without requiring separate detection mechanisms for each attack type.
Solution Approach 2:
The system dynamically adjusts classification parameters and mitigation strategies based on the detected interference characteristics. The deep learning model processes multiple features including signal power, spectral characteristics, and protocol-level patterns to adaptively change classification thresholds and mitigation parameters, enabling effective response to varying attack intensities and types across different operational conditions.
2Measurement precision
If the system implements comprehensive cross-layer sensing and classification, then it can identify sophisticated interference patterns, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the interference detection and classification process into distinct functional modules operating at different network layers. The Physical layer handles signal-level feature extraction, the MAC layer processes medium access patterns, and the Network layer analyzes protocol-level characteristics. This segmentation allows each module to specialize in specific feature types, reducing overall system complexity while maintaining high classification precision through coordinated multi-layer analysis.
Solution Approach 2:
The system introduces a deep learning-based classification engine as an intermediary between raw signal reception and mitigation action. This intermediary component processes complex multi-layer features and transforms them into simplified classification decisions, reducing the computational burden on individual network layers while maintaining high measurement precision through the intermediary's specialized neural network architecture.
3Speed
If real-time interference classification is performed using deep learning, then the system can respond quickly to attacks, but energy consumption increases
Solution Approach 1:
The system implements partial deep learning processing by applying neural network classification only when interference is detected, rather than continuously processing all signals. The Physical layer first performs lightweight signal analysis to detect potential interference, and only then activates the more energy-intensive deep learning classification engine. This partial action approach maintains fast response times for actual attacks while reducing overall energy consumption during normal operation.
Solution Approach 2:
The system performs preliminary interference detection at the Physical layer using low-computation signal processing techniques before engaging the deep learning classifier. This preliminary action filters out normal signals and only passes suspicious patterns to the energy-intensive classification engine, enabling fast response to real threats while minimizing energy consumption by avoiding full deep learning processing for every transmitted signal.
Data Source
AI summary
One or more aspects of the present disclosure are directed to a software-based solution that can classify interference signals in real-time affecting a radio equipment and provide/implement an interference mitigations scheme to combat the interference signal and restore communication system of the radio equipment. In one aspect, a radio equipment includes memory having computer-readable instructions stored therein and one or more processors. The one or more processors are configured to execute the computer-readable instructions to receive at least one interference signal via an antenna of the radio; determine one or more layers characteristics of one or network layers used for transmission of signals for the radio; classify the interference signal using one or more features in the interference signal and the one or more layers characteristics; and determine an interference mitigation scheme for countering the interference signal.