Spectrum Reconstruction via Deep Convolutional Neural Networks for Satellite Communication Systems
Bibliographic record
Abstract
Satellite based spectrum sensing is studied for a system consisting of multiple satellites and a gateway (GW), where these satellites perform spectrum sensing and send mass spectrum-sensing data to the GW. To address the challenges of mass spectrum-sensing data and limited transmission capacity of the links from spectrum-sensing satellites to the GW, we propose a method called joint anomalous data repairing and deep convolutional neural network based spectrum reconstruction (ADRD-SR), which can reconstruct the original spectrum-sensing data from the incomplete data. Specifically, the GW preprocesses the incomplete data using the anomalous data repairing algorithm. A deep convolutional neural network is constructed and well trained, then it is activated to reconstruct the preprocessed spectrum data. Additionally, to sustain good reconstruction performance by tracing the dynamical spectrum-sensing data, we design a real-time evaluation oriented spectrum reconstruction framework, through seeking the events when the mean absolute error (MAE) becomes larger than a predefined threshold. Furthermore, the ADRD-SR method can reduce the MAE by more than 68% over the conventional reconstruction methods. Moreover, the reconstructed spectrum data can be used to assist spectrum sensing, and the corresponding probability of correct detection is only degraded by 5% even when 75% of the data is discarded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".