Decentralized Coordination of Converged Tactile Internet and MEC Services in H-CRAN Fiber Wireless Networks
Why this work is in the frame
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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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it