Two dimensional cross-layer optimization for packet transmission over fading channel
Bibliographic record
Abstract
In this paper a single-input-single-output wireless data transmission system with adaptive modulation and coding over correlated fading channel is considered, where run-time power adjustment is not available. Higher layer data packets are enqueued into a finite size buffer space before being released into the time-varying wireless channel. Without fixing the physical layer error probability, the objective is to minimize the average joint packet loss rate due to both erroneous transmission and buffer overflow. Two optimization techniques are incorporated to achieve the best solution. The first is policy domain optimization that formulates the data rate adaptation design as classical Markov decision problem. The second is channel domain optimization that appropriately partitions the channel variation based on particular fading environment and carried traffic pattern. The derived policy domain analytical model can precisely map any policy design into various QoS performance metrics with finite buffer setup. We then propose a tractable suboptimization framework to produce different two-dimensional suboptimal solutions with scalable complexity-optimality tradeoff for practical implementations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".