SDATP: An SDN-Based Adaptive Transmission Protocol for Time-Critical Services
Bibliographic record
Abstract
In this article, a comprehensive approach is proposed for 5G communication networks. In SDATP, a slice-level customized protocol is developed for supporting time-critical services that require high reliability and low-latency, such as MTC services for industrial automation. To satisfy service quality requirements for an MTC service, we introduce in-network intelligence in the proposed protocol, by enabling the functionalities of in-path caching, in-path retransmission, in-network congestion detection and congestion control. To minimize the E2E delay, we optimize the configuration of caching functionalities, including the number of enabled caching nodes, caching node placement, and probabilistic packet caching policy. Since the optimization problem is NP-hard, we simplify the problem by reducing the number of decision variables and propose a low-complexity algorithm to solve the simplified problem. Extensive simulation results are presented to validate the effectiveness of the proposed algorithm in terms of retransmission hops and its adaptiveness to network dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".