Risk-Aware Grant-Free for B5G/6G Ultra-Reliable Low-Latency Communications
Bibliographic record
Abstract
The fifth generation (5G) of mobile communication has been rapidly deployed during the last five years, and interest in this novel ecosystem is still increasing around the world. Nevertheless, several limitations have been identified due to a few novel use cases (e.g., VR/AR/XR, tactile internet, autonomous driving, telepresence, etc.) and the growing need for ubiquitous connectivity. 5G limitations have been leading the research community and industry to make notable progress in exploring new enabling technologies and envisioning the next generation of mobile communications networks. 6G is expected to deliver never-seen performance in terms of throughput (Tbits/s), latency (100 us), reliability, and global 3D connectivity. This way, 6G will transcend the Internet of Things (IoT) to the Internet of Everything (IoE) by supporting ubiquitous wireless access. In this paper, we intend to assess the applicability of a 5G and beyond Radio Access Network in terms of end-to-end latency, using an M/M/C queueing model. Specifically, we target Ultra-Reliable Low-Latency Communications (URLLC) applications and consider Grant-Free (GF) access schemes. Then, using the Conditional Value at Risk (CVaR), we derive an expression for the average violation delay given a target delay and reliability requirement. Finally, extensive simulations are performed to assess the accuracy and applicability of the derived model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".