Robust Secure Beamforming for Wireless Powered Cognitive Satellite-Terrestrial Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article addresses the security problem for wireless powered cognitive satellite-terrestrial network, where a multibeam satellite sub-network shares the portion of millimeter wave bands with multiple cellular networks, each consisting of a base station, several mobile users (MUs) and energy receivers (ERs). Considering that the ERs are potential eavesdroppers of the MUs, and only imperfect knowledge of the angles of departure for the wiretap channels is available, we aim at maximizing aggregated rate of the considered network while guaranteeing the signal-to-interference-plus-noise ratio requirements of the MUs, the energy harvesting thresholds and the secrecy constraints at ERs. Since the formulated optimization problem is mathematically intractable, we exploit a discretization method and the Taylor expansion method to transform the non-convex objective and constraints into convex ones, and then propose an iterative beamforming (BF) algorithm to solve the problem. Furthermore, we present a combined multibeam scheme to obtain suboptimal BF weight vectors with low computational burden. Finally, simulation results reveal that the proposed BF schemes can efficiently improve the aggregated rate with fast convergence compared to the benchmark schemes.
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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 | 0.000 | 0.001 |
| 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 it