Robust Max–Min Fairness Resource Allocation in Sensing-Based Wideband Cognitive Radio With SWIPT: Imperfect Channel Sensing
Bibliographic record
Abstract
Fairness among different users and energy utilization are key issues in the future communication network design. Robust max-min fairness resource allocation in sensing-based wideband cognitive radio with simultaneous wireless information and power transfer is studied when spectrum sensing and channel state information are imperfect. A worst-case throughput is maximized by jointly optimizing the sensing time, transmit power, and subchannel allocation under the worst-case channel state information error model, subject to constraints on energy harvesting, interference power, and transmit power. Two operation paradigms for cognitive radio are considered, namely, opportunistic spectrum access and sensing-based spectrum sharing. The formulated robust max-min fairness resource allocation problems are mixed-integer and nonconvex programming with infinite inequality constraints. An efficient one-dimensional search algorithm is designed based on the proposed transmit power and subchannel allocation scheme. Simulation results show that the secondary user under sensing-based spectrum sharing can obtain a performance gain compared with that under opportunistic spectrum access at the cost of implementation complexity. Design tradeoffs are identified and discussed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".