Maximizing URLLC Reliability Through JPSA for URLLC Services in IRS-Aided Terahertz Networks
Bibliographic record
Abstract
In this paper, we propose a novel framework to integrate the intelligent reconfigurable surface (IRS) in terahertz (THz) networks, with co-existing ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services. To meet URLLC latency constraints, the URLLC traffic is scheduled along with eMBB traffic, which poses a significant resource allocation challenge. To address this concern, in this paper, we propose a joint power and service allocation (JPSA) framework to maximize URLLC reliability while ensuring eMBB data rates. Furthermore, to address the challenging NP-hard mixed-integer nonlinear programming (MINLP) problem, we decompose the resource allocation problem into the URLLC power allocation and service allocation sub-problems. More specifically, we suggest a one-to-one matching game for service allocation. Our simulation results demonstrate that the proposed scheme outperforms baseline methods, particularly in terms of enhancing the reliability of URLLC user equipment (uUEs).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".