Angular Information Based Robust Downlink Transmission for IRS-Enhanced Cognitive Satellite-Aerial Networks
Bibliographic record
Abstract
This paper proposes a downlink transmission for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network, which can provide heterogeneous services for various users. The satellite adopts multicast transmission scheme to provide content-aware services for many satellite terminals, while the aerial platform offers connection-centric services for users having line-of-sight links through space division multiple access, and for users locating in blocked aera via IRS-enhanced non-orthogonal multiple access. Assuming that the satellite network and aerial network share the same spectrum, and only the imperfect channel state information is available, we formulate a total transmit power minimization problem subject to the outage probability constraints for users, the per-antenna transmit power budgets of satellite and aerial platform, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose an alternation-based robust transmission algorithm, combining the central limit theorem, successive convex approximation and penalty function, to optimize the beamformers of satellite and aerial platform, phase shifts and power allocation. Furthermore, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness of the proposed two algorithms and show the superiority to benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".