Profit optimization in multi-service cognitive mesh network using machine learning
Bibliographic record
Abstract
Cognitive technology enables licensed users (primary users, PUs) to trade the surplus spectrum and to transfer temporarily spectrum usage right to the unlicensed users (secondary users, SUs) to get some reward. The rented spectrum is used to establish secondary network. However, the rented spectrum size influences the quality of service (QoS) for the PU and the gained rewards. Therefore, the PU needs a resource management scheme that helps it to allocate optimally a given amount of the offered spectrum among multiple service classes and to adapt to changes in the network conditions. The PU should support different classes of SUs that pay different prices for their usage of spectrum. We propose a novel approach to maximize a PU reward and to maintain QoS for the PUs and for the different classes of SUs. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed to derive resource adaptations to changing network conditions, so that PUs' profit can continuously be maximized. Available spectrum is managed by the PU that executes the optimal control policy, which is extracted using RL. Performance evaluation of the proposed RL solution shows that the scheme is able to adapt to different conditions and to guarantee the required QoS for PUs and to maintain the QoS for a multiple classes of SUs, while maximizing PUs profits. The results have shown that cognitive mesh network can support additional SUs traffic while still ensuring PUs QoS. In our model, PUs exchange channels based on the spectrum demand and traffic load. The solution is extended to the case in which there are multiple PUs in the network where a new distributed algorithm is proposed to dynamically manage spectrum allocation among PUs.
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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) | 0.001 | 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".