Spectrum sharing in multi-service cognitive network using reinforcement learning
Bibliographic record
Abstract
In this paper the issue of spectrum sharing in multi-service cognitive wireless network is addressed. The problem is formulated as a revenue maximization problem and a framework is presented that is capable of adequately solving a class of problem where resources are shared in radio environment. Primary users (PUs) exchange channels dynamically and based on the availability of idle channels at neighbors. Secondary users (SUs) of different classes form a mesh network and rent a spectrum from primary users. For such cognitive wireless mesh networks, the main challenge facing a PU is to satisfy the following conflicting objectives: maximizing its total revenue, maintaining its quality of service (QoS) (that degrades due to renting its spectrum to SUs) and reducing secondary user delay times. In this work machine learning paradigm is presented as a means for extracting the optimal control policy for spectrum sharing. To obtain different requirements, the objective function is defined to maximize the total revenue gained by primary users. Value iteration algorithm is applied to find an optimal control policy that maximizes the difference between reward and cost (revenue). Performance evaluation of the proposed spectrum sharing approach shows that the scheme is able to find an efficient trade-off between PUs revenue and SUs delay.
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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 | 0.001 | 0.001 |
| 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".