Cross-layer dynamic rate adaptations for green cognitive radio networks
Bibliographic record
Abstract
We investigate cross-layer rate adaptation techniques for a secondary user (SU) equipped with a finite buffer in green cognitive radio networks. We assume that the activity statistics of the licensed primary users (PUs) channels are independent and identically distributed, and a SU detects their states using a spectrum sensing method. We formulate the problem as an infinite-horizon partially observable Markov decision process (POMDP). The policy is obtained using maximum-likelihood heuristic policy (MLHP) technique. We assume that the transition probabilities of the PUs and the fading channel between the SU's transmitter and receiver are known, but the exact PU's states are hidden. By tracking belief of the hidden states, the SU takes decision on the rate and corresponding power to minimize energy consumption with constraints on delay and bit error rate. Numerical results are given to show the performance of the proposed MLHP, which is found to perform very close to fully observable optimal policy. We also show pointer to choose design parameter so that the scheduler becomes the most energy-efficient for given quality of service requirements of the application.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".