Bibliographic record
Abstract
The stringent quality of service (QoS) requirements in 5G networks, particularly for ultra-reliable low-latency communication (urLLC) use cases, pose significant challenges for the centralized unit user plane (CU-UP) within the Radio Access Network (RAN). Current general-purpose processor (GPP)-based CU-UP deployments struggle to meet stringent QoS requirements, such as sub-millisecond latency and high reliability, especially under high traffic loads. We propose Blink, a P4-based system that offloads CU-UP functions to programmable switch ASICs, enabling high-speed packet processing and improved QoS. Blink employs a novel trigger-based offloading mechanism that can be dynamically adapted to network conditions, ensuring seamless transitions between GPP-based and P4-based processing. In our evaluations, Blink achieves a 1,941× lower median packet processing latency compared to GPP-based CU-UP deployments, while delivering 12% higher throughput and maintaining near-zero packet loss of 0.001%, crucial for urLLC applications. These improvements are sustained across multiple distributed units (DUs) and user equipment (UEs), making Blink a scalable and flexible solution for enhancing CU-UP performance in 5G networks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".