Distributed Spectrum Sharing Using Blockchain: A Hyperledger Fabric Implementation
Bibliographic record
Abstract
Dynamic Spectrum Sharing (DSS) is proposed as a solution to the spectrum scarcity and under-utilization problem in a world of ever-increasing spectrum demand. Enabling DSS, however, requires overcoming many technical, regulatory, and economic challenges. Cognitive Radio (CR) provided a solution for some of the technical issues of DSS by equipping wireless devices with intelligent sensing and decision making capabilities to enable dynamic sharing of the surrounding spectrum between devices. However, CR alone has been unable to provide a fully dynamic ecosystem for spectrum sharing that guarantees protection for spectrum owners. This has led multiple spectrum regulators to implement frameworks that enable DSS through a centralized spectrum management system that complements the CR capabilities to ensure compliance with spectrum access policies and regulations. However, these frameworks require trusting a third party to manage spectrum access and do not provide intrinsic mechanisms to incentives spectrum owners to share their spectrum. Blockchain technology provides a distributed platform for autonomous asset trading that can be utilized to implement a fully dynamic spectrum sharing system, ensuring transparency and trust between devices without the need for a third party. This paper provides a blockchain-based model for a DSS that represents spectrum access rights as tokenized assets and enables trading of these spectrum tokens between multiple users on a distributed ledger using smart contracts. The proposed model is implemented using Hyperledger Fabric (HLF) as a permissioned blockchain network and the details of the implemented Chaincode transactions are outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".