ZF-Based Beamforming for Wireless Powered Cognitive Satellite-Terrestrial Networks
Bibliographic record
Abstract
In this paper, we propose a novel zero-forcing (ZF)- based beamforming (BF) scheme for a wireless powered cognitive satellite-terrestrial network (CSTN) operated in the millimeter wave band. Assuming that the satellite and base station are equipped with multiple antennas, we aim at maximizing the sum rate of the CSTN while satisfying the signal-to-interference-plus-noise- ratio requirements for both the information receivers (IRs) and earth stations, the energy harvesting requirements of the energy receivers (ERs), and the secrecy constraints at the ERs. Since the resulting optimization problem is mathematically intractable, we propose a novel multi-beam-based ZF BF scheme to generate beamforming vectors to serve the IRs and ERs. Specifically, the original nonconvex problem is decomposed into two independent subproblems. The first subproblem, which features beam orthogonality constraints, leads to closed form solutions for the beamforming vectors. The second subproblem, aiming at finding the optimal power allocation, is solved via the S-procedure. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".