Decentralized Coordination of Converged Tactile Internet and MEC Services in H-CRAN Fiber Wireless Networks
Bibliographic record
Abstract
In order to meet the requirements of services and applications envisioned for post-5G and 6G networks, research efforts are heading towards the convergence of architectures aiming to support the wide variety of new compute-demanding and latency-sensitive applications in the context of Tactile Internet. In this article, we study the resource allocation and association of users with different delay requirements in a shared-backhaul fiber-wireless (FiWi) enhanced Heterogeneous Cloud Radio Access Network (h-cran) with Multi-access Edge Computing (mec) and offloading. As opposed to traditional resource and association management, we propose a decentralized algorithm based on a full dual decomposition of the optimization problem to operate the network. Results show that this approach outperforms the traditional one in terms of average delay and energy consumption, achieving up to 80% average delay improvement in high-load scenarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".