Optimal Packet Scheduling using Adaptive M-QAM and Orthogonal STBC in MIMO Nakagami-m Fading Channels
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Bibliographic record
Abstract
We study a cross-layer optimization problem of a rate-adaptive MQAM system that maximizes throughput, and minimizes packet error rate, delay and overflow using the framework of Markov decision process. We consider finite size buffer and random incoming traffic arrivals. The fading channel is assumed to be Nakagami-m and modeled with a finite state Markov channel, and transmit diversity is achieved with orthogonal space-time block code. To schedule packets, the transmitter depends on both buffer state and channel state information, and selective-repeat ARQ is used at the data link layer for error detection. Simulation results show that the throughput maximization depends not only on the received SNR but also on the number of actions and incoming traffic statistics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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