Detecting Wireless Signal Noise in Mobile Radio Communications Using Spatiotemporal AnoGAN-Based Approaches
Why this work is in the frame
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Bibliographic record
Abstract
With the development of radio modulation technologies for communication and wireless applications, several studies have been conducted to reduce and eliminate noise during signal transmission. Although the influence of noise can be effectively addressed, it has become a popular research topic in mobile communications. Moreover, in recent telecommunication systems, owing to their complexity and comprehensive protocols, which require a large number of mathematical and engineering approaches, predicting and classifying noise is difficult. Thus, to effectively address these challenges, we propose a spatiotemporal AnoGAN to detect the noise that can occur during radio modulation. In our approach, we assemble a set of AnoGANs based on convolutional neural networks (CNNs) and long short-term memory (LSTM) to enable the system to learn the time-series features of the radio modulation signal and shape expressed in complex planes. The proposed spatiotemporal AnoGAN can discriminate the interference caused by noise without any annotation of anomalies using a generator and discriminator. The proposed spatiotemporal AnoGAN achieves a 91.4% recall in digitally modulated signals that were previously difficult to identify. Through an empirical analysis of the proposed method, we observed that the spatiotemporal AnoGAN accurately identified abnormal interference signals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it