Improving SAGIN Resilience to Jamming with Reconfigurable Intelligent Surfaces
Bibliographic record
Abstract
This study investigates the anti-jamming space-air-ground integrated network (SAGIN) scenario wherein a reconfigurable intelligent surface (RIS) is deployed on a fixed Unmanned Aerial Vehicle (UAV) to counteract malevolent jamming attacks. In contrast to existing research, in this paper, we consider that a Low Earth Orbit (LEO) satellite is sending the signal to the user on the ground in the presence of jamming from a Geostationary Equatorial Orbit (GEO) satellite side. We aim to maximize the signal-to-jamming plus noise ratio (SJNR) by optimizing the RIS beamforming and transmit power of the LEO satellite. Assuming the availability of global channel state information (CSI) at the RIS, we propose alternating optimization (AO) and semidefinite relaxation (SDR) techniques to address the complexity. Simulation results show that the optimization schemes lead to considerable performance improvements. The results also indicate that, given the high jamming power and the relatively small number of RIS elements, deploying the RIS on UAVs near the user is more effective in mitigating the impact of jamming interferers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".