Anti-jamming Transmission in NOMA-based Multi-cell Satellite–terrestrial Integrated Networks
Bibliographic record
Abstract
Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".