Formation and Assertion of Data Unit Groups in 3GPP Networks with TSN and PDU Set Support
Bibliographic record
Abstract
Industrial applications and Extended Reality vertical sectors have expressed the need for dedicated Quality of Service considerations from 3GPP to support time-sensitive, bursty and high throughput communications. Consequently, 3GPP enabled support for Time-Sensitive Networking in Release 17 and started specifying the concept around Packet Data Unit Sets in Release 18. This paper presents a novel solution for any IP-based communication enabling time-sensitive communication while utilising 3GPP Packet Data Unit Set feature. This paper proposes extensions to the IP header that can be utilised by any IP based network. The proposed solutions introduce the concept of a Data Unit Group to describe the entirety of an Application Data Unit and its fragmentation into individual IP packets to be delivered over a packet-switched network. This paper defines Data Unit Group Rules to communicate packet header detection and action rules to Time-Sensitive Networking switches. The rules can be used by Time-Sensitive Networking switches to prioritise and re-order/pre-empt packets and by User Equipments and User Plane Functions to write Packet Data Unit Set Markings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".