Role of Blockchain in Spectrum Sharing in B5G Networks Service
Bibliographic record
Abstract
Beyond 5G (B5G) Networks are envisioned to support a wide range of services with varying data profiles and traffic requirements. Unlike previous generations of wireless communication where improving BW and latency was the main theme, the primary objective of B5G is to provide the flexibility needed to support diverse network services required by different sectors of the industry (also referred to as verticals). One of the key enablers to support the heterogeneous network envisioned as part of the Industry 4.0 revolution is the introduction of unlicensed spectrum bands (mmWave and Terahertz). Optimal utilization of spectrum is crucial for the seamless working of different services in the B5G network. One of the proposals to provide on-demand spectrum usage is to use Blockchain for managing the spectrum. In this paper, we focus on a Blockchain-enabled B5G network for spectrum management. First, we provide a brief introduction to different services envisioned to be part of the B5G network based on the 3GPP vision. Second, we discuss recent work on Blockchain-enabled B5G networks and highlight the pros and cons of the proposals. The objective of this paper is to position Blockchain in the B5G framework and to demystify the pros and cons of using Blockchain focusing on spectrum management. Finally, we perform a simulation to study the effects of spectrum allocation strategies on a Blockchain network size. The results suggest that the strategy of how transactions are allocated in a Blockchain network can have either a positive or negative impact on the overall QoS of a service.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".