Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning
Bibliographic record
Abstract
In a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty‐line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.
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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 | 0.003 |
| 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) | 0.000 | 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".