Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber
Bibliographic record
Abstract
SUMMARY The Internet of Things (IoT) is the next big possibility and challenge for the future information networks. It makes the interaction between people and things more active and provides the connection among different existing networks. Ubiquitous short‐range wireless access and cognitive radio are key technologies for the IoT's realization. This paper deals with some problems in an integrated system of wireless local area network (WLAN) and cognitive radio — cognitive WLAN over fiber (CWLANoF). CWLANoF is a cost‐effective and efficient architecture that combines radio over fiber and cognitive radio technologies to provide centralized radio resource management and equal spectrum access in infrastructure‐based IEEE 802.11 WLANs. In this paper, a reinforcement learning approach is applied to implement dynamic channel selection in CWLANoF. The cognitive access points select the best channels among the industrial, scientific, and medical band for data packet transmission, given that the objective is to minimize external interference and acquire better network‐wide performance. The reinforcement learning method avoids solving complex optimization problems while being able to explore the states of a CWLANoF system during normal operations. Simulation results reveal that the proposed strategy is effective in avoiding aggregated interference, reducing outage probability, and improving network throughput. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".