Proactive Decision Based Handoff Scheme for Cognitive Radio Networks
Bibliographic record
Abstract
Handoff in a cognitive radio networks (CRNs) is a situation that arises whenever a secondary user (SU) has to switch from its current channel to a new target channel in case the primary user (PU) reclaims the current channel or the channel conditions get worst. Before starting the SU transmission, a proactive decision to select the prospective vacant target channel after the PU interruption to resume the unfinished transmission can save substantial sensing time. In addition, the sequence of backup target channels can help reducing the service time of a SU considerably. This paper proposes a proactive decision based handoff scheme for CRNs, in which a non- iterative greedy approach is implemented to proactively determine the optimal target channel sequence without requiring the usual brute force strategy. Simulation results show that the proposed approach outperforms the reactive approach as well as a chosen benchmark scheme in terms of service time and number of handoffs. A comparative performance is also obtained in terms of throughput achieved by the SU under varying PU traffic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".