Delay-QoS-Aware Adaptive Modulation and Power Allocation for Dual-Channel Coherent OWC
Bibliographic record
Abstract
Statistical-delay quality of service (QoS) provides bounded link-layer delay over wireless fading channels with a certain delay-bound violation probability. We propose statistical-delay-QoS-aware adaptive modulation (AM) and power allocation for a dual-channel coherent optical wireless communication system over the atmospheric turbulence fading channels. For given statistical-delay constraints and target bit-error-rate requirements, our proposed AM and power allocation maximize the effective spectral efficiency subject to the transmit-power constraints. We develop delay-QoS-aware adaptive transmission schemes by employing independent and joint channel optimizations subject to average transmit-power constraints. We also consider independent, joint, and successive channel optimizations for developing delay-QoS-aware adaptive transmission schemes subject to peak transmit-power constraints. Numerical results demonstrate that our proposed AM and power allocation significantly outperform the conventional adaptive transmission schemes in the strict statistical-delay constraints. Numerical results also depict superiority of the joint channel optimization in the strong turbulence fading and strict statistical-delay constraints.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".