POMDP-based cross-layer power adaptation techniques in cognitive radio networks
Bibliographic record
Abstract
We investigate the spectrum access and power adaptation techniques in a cognitive radio network to optimize throughput of a secondary user with specified sensing error limit. Using partially observable Markov decision process framework, we first study the optimal policies, where the primary user is assumed to be in busy, concurrent or idle state, and the secondary user either stay idle or transmits with any of the two designed power level. The collision is avoided with proper reward choices. Although the primary user's states are hidden, their activity statistics, ranges of transmission, and interference thresholds are assumed to be known. The instantaneous optimal policy for each time-slot is then obtained for the current belief of the states obtained through channel sensing. We also propose a forward algorithm based technique that updates belief using the sensor output in the first slot and then using the acknowledgment feedback in the subsequent time-slots in a frame. Simulation results show that the proposed cross-layer technique is more throughput efficient than the physical layer optimal case, specially when the primary user activity is slowly varying and/or frame size is smaller.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".