Energy-Efficient QoS Based Route Management in Cognitive Radio Networks
Bibliographic record
Abstract
In this paper QoS based route management technique is proposed for Cognitive radio networks. Proposed model makes use of functionalities of profile exchange mechanism and location services. The QoS routing algorithm contains following elements: (a) each licensed user (PU) exchanges properties of the channels it owns with a Central Entity, whereas all the unlicensed users (SU) in the network due to cognitive functionality sense the environment to obtain identification information of its neighboring nodes and channel present between them. All unlicensed users share this information with Central Entity, (b) Central Entity with the help of received information and location services, and for different QoS levels supported by the network, prepares routes for all the SUs in the network to reach every other SU as its destination. Different Quality of Service metrics considered include power transmission, probability of channel availability, probability of PU presence, and Expected Transmission Count. The proposed model provides a route with minimum end-to-end transmission power, high probability of channel availability, low probability of PU presence and low value of expected transmission count, to increase life span of users in the network by selecting energy efficient route, to decrease the delay, to stabilize wireless connectivity and to increase the throughput of the communication, respectively, based on the QoS level requested by a secondary user.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".