Study on collaborative spectrum sensing of multi-satellite low orbit satellites based on multi-satellite collaborative beamforming and intelligent algorithms
Bibliographic record
Abstract
Low earth orbit satellites can help narrow the digital divide and provide low latency and high-speed Internet connections.However, they are extremely fast and cannot stay over a single place.In addition, during the process of circumnavigating the ground, it can only be exposed to a ixed location for a short period of time.In response to the above issues, a collaborative spectrum sensing algorithm based on fuzzy integration is proposed, and an intelligent algorithm is obtained by combining alliance game algorithm.Finally, a multi-satellite low orbit satellite collaborative spectrum sensing method based on multi-satellite cooperative beamforming and intelligent algorithm is designed.The research results indicated that the correct detection probability of intelligent algorithms was positively correlated with the signal-to-noise ratio.At a signal-to-noise ratio of -11dB, the probability of correct detection reached a steady state of 1.Under strong interference conditions, when the number of participating satellites in the array was 10, the detection probability of the research method approached 1, and the optimal satellite array power utilization rate obtained was 93.4%.The above results indicate that the research method can reduce the impact of strong ground interference signals and fully tap into the spatial resources available for low orbit satellites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".