Proactive channel access in cognitive radio networks using statistical radio environment maps
Bibliographic record
Abstract
Inclusion of statistical knowledge of the primary user (PU) channel usage had shown to be beneficial in dynamic spectrum access. Motivated by this fact, this paper investigated the importance of collecting and using statistics on neighboring secondary users (SUs) in selecting channels in addition to the knowledge of PU channel usage. The paper assumed that PU traffic characteristics of the channels are included in the radio environment map in the form of probabilistic suffix trees, which is a sequence predictor based on Markov property. In the proposed method, an intelligent sequence hopping-based common control channel and a carrier sense multiple access (CSMA)/collision avoidance (CA)-based medium access control (MAC) protocol were introduced. As shown in the paper, selecting channels using statistics of both the neighboring SUs and PUs reduced the number of packet collisions compared to a scheme which only uses PU statistics. Furthermore, the simulation results showed that the scheme proposed had better throughput performance with respect to both the random channel selection scheme and the scheme which only uses PU statistics while having less training complexity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".