Learning-Based Deterministic Scheduling for TSN and 5G Integrated Networks
Bibliographic record
Abstract
Integration of the fifth-generation mobile communication technology (5G) into time-sensitive networking (TSN) was first proposed in the 3GPP Release 16. However, this conceptual proposal lacks of detailed designs to guarantee bounded latency and high reliability of this integration. In this paper, we study a deterministic scheduling problem for TSN-5G integrated networks in industrial Internet of things (IIoT) scenarios, in which a unified control plane jointly allocates the time-frequency resources for TSN and 5G to support deterministic end-to-end transmission. Specifically, we design a novel control architecture, i.e., centralized network and distributed user, for the integrated networks to reduce the signaling overhead. Moreover, we formulate a stochastic optimization problem for IIoT scenarios to maximize the number of successfully scheduled flows as well as realize throughput fairness for wired and wireless equipment. Since the resource allocation of TSN and 5G are coupled, this problem is NP-hard. We propose a dueling double deep Q network (D3QN) based Joint Resource Allocation (DJRA) algorithm. By leveraging two convolution-enhanced neural networks, with their parameters periodically synchronized, the accuracy of the estimated Q-value can be increased and the convergence speed of DJRA can be accelerated. Simulation results show that the proposed algorithm can facilitate efficient cooperation between TSN and 5G as compared to the other heuristic and learning-based algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".