A FAST CONVERGENT CHANNEL SELECTION STRATEGY IN CRSN
Bibliographic record
Abstract
Accurate and fast convergence to the optimal channel is a challenge in a cognitive radio sensor network (CRSN) when multiple cognitive wireless channels coexist.Some traditional wireless channel selection methods can be used to study the optimal channel selection.However, their convergence speed cannot meet the requirements because of vast computation and time accumulation.In this paper, a rapid channel selection strategy based on machine learning called MAB-CQ (multi-armed bandit-channel quality) is proposed.This strategy maps the channel selection problem to the improved multiarmed bandit (MAB) model.In the model, the second users (SUs) and the channels in the CRSN correspond to the players and the arms of MAB, respectively.The optimal channel is determined based on the UCB (upper confidence bound) of MAB-CQ for each player.In addition, the UCB equation is creatively defined to balance the exploration and exploitation problem.At the same time, to reduce the computation complexity, coefficients about the factors are used to narrow down the exploratory scope of our strategy.As a result, an accuracy optimal channel and a fast convergence speed are achieved by iterative execution of MAB-CQ.Extensive experimental results demonstrate that the MAB-CQ can converge to nearly 100% within the 10 5 time slots.By comparison, MAB-CQ has obvious advantages in cumulative rewards, computational complexity and convergence speed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".