Multi-Operator Dynamic Spectrum Sharing for Wireless Communications: A Consortium Blockchain Enabled Framework
Bibliographic record
Abstract
To enable secure and efficient dynamic spectrum sharing (DSS) with guaranteed revenue and quality of service (QoS) in future wireless communications, we present a consortium blockchain-based DSS framework, where the regulators supervise the whole process of DSS, and thus the revenue of each participant can be guaranteed. Each mobile network operator (MNO) on the chain can adaptively act as a spectrum provider or spectrum requestor based on their demand, and the spectrum resource allocation is recorded on the chain with a smart contract. The optimal spectrum pricing and buying strategies are solved based on a multi-leader multi-follower (MLMF) Stackelberg game model, and the equilibrium is solved with the proposed algorithm. We then build a prototype with Hyperledger Fabric consortium blockchain, and the average {latency is} evaluated. Simulations and prototype evaluations validate the feasibility of blockchain-based DSS and show that the average latency increase with the participants, which provides useful insights for real applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".