Hierarchical Spectrum Sharing in Cognitive Radio: A Microeconomic Approach
Bibliographic record
Abstract
We consider the problem of hierarchical spectrum sharing in cognitive radio environment. In the system model under consideration, licensed service (i.e., primary service) can share/sell available spectrum to an unlicensed service (i.e., secondary service), and again, this unlicensed service can share/sell allocated spectrum to other service (i.e., tertiary service). We formulate the problem of hierarchical spectrum sharing as an interrelated market model in which a multiple-level market is established among the primary, secondary, and tertiary services. We use the concept of demand and supply functions in economics to obtain the partial equilibrium for which all services are satisfied with the shared spectrum size and the charging price. These functions are derived based on the utility of the connections using the different services. In addition, we consider a system for which the global information is not available. Therefore, each service needs to learn and adapt the strategies to reach an equilibrium. Two iterative algorithms (i.e., excess demand-based and successive overrelaxation (SOR)) are proposed. The stability condition for the learning rate is analyzed for these algorithms.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".