A Robust Jamming Signal Classification and Detection Approach Based on Multi-Layer Perceptron Neural Network
Bibliographic record
Abstract
The Quality of Service (QoS) and security of a wireless communication system can be improved using a robust Radio Frequency Interference (RFI) detection method, which could in turn lead to an effective mitigation process.Due to the vast variety of RFI signals in existence, it is crucial to deploy a spectrum signal detection technique dealing with all kinds of potential interference (non-intentional or intentional, known as jamming).To solve this problem, a robust multi-class Multi-Layer Perceptron (MLP) neural network technique has been developed in this paper to recognize jamming signals in a real-time digital video broadcasting scenario based on DVB-S2 standards.It is assumed that the communication signal can be combined with one of the three major kinds of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI) and Chirp Interference (CI).The proposed algorithm can either classify the interference under its related group or recognize the Signal of Interest (SoI).Besides analyzing different learning approaches (such as online learning, full-batch, and mini-batch), the Principal Component Analysis (PCA) technique is deployed to select more informative features to enhance the classifier's performance.More importantly, a new methodology is presented to generate a real-time jamming signal dataset that is entirely different from what is provided in other similar studies.Furthermore, the robustness of the trained classifier is assessed to detect unknown signals at different Signal to Noise Ratios (SNR).In addition, the performance of the proposed design is compared with the well-known Support Vector Machine (SVM) Machine Learning (ML) technique in terms of both jamming signal classification and detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".