Joint Channel Access and Sampling Rate Control in Energy Harvesting Cognitive Radio Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate the network utility maximization problem in energy harvesting cognitive radio sensor networks (CRSNs). Different from traditional sensor networks, sensor nodes in CRSNs are embedded cognitive radio modules, enabling them to dynamically access the licensed channels. Since the dynamic channel access is critical to guarantee the network capacity for CRSNs, existing solutions without considering the dynamic channel access cannot be directly applied into CRSNs. To this end, we aim at maximizing the network utility by jointly controlling the sampling rates and channel access of sensor nodes, under the energy consumption, channel capacity and interference constraints. With the consideration of fluctuated energy harvesting rates and channel switching costs, we formulate the network utility maximization as a mix-integer non-linear programming problem and solve it in an efficient and decoupled way by means of dual decomposition. A joint channel access and sampling rate control scheme, named JASC, is then proposed considering the real-time channel sensing results and energy harvesting rates. Extensive simulation results demonstrate that JASC can efficiently improve the network utility in CRSNs based on a realistic energy harvesting dataset.
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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 | 0.001 | 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 | 0.001 |
| 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