Support Vector-Based Unsupervised Learning Approaches for Radio Frequency Interference Detection
Bibliographic record
Abstract
The presence of unwanted signals in the radio frequency (RF) spectrum, called RF interference (RFI), is a major drawback in wireless communication systems. The detection of REI has been dealt mainly with signal processing and supervising machine learning approaches. In this paper, we investigate two unsupervised machine learning alternatives for REI detection, the one-class support vector machine (SVM) and the support vector data description (SVDD) algorithm, which delimit the class boundaries of normal signals in high-dimensional space, and view REI contaminated signal as novelty, i.e., outsiders from unknown classes. Similar to the popular binary SVM classifier, these two algorithms can learn from a relatively small training set, and they use unsupervised training to learn from typically unbalanced RFI data sets without need for data augmentation techniques as in supervised training. The experimental results for detecting three types of RFI, using scaling features to a range as a standardization method, show that SVDD has a low computational complexity and an accuracy of 90.74 % versus 91.67 % for the One-class SVM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".