Multi Objective Resource Allocation for Joint eMBB and URLLC Traffic with Different QoS Requirements
Bibliographic record
Abstract
In this paper, a novel multi-objective resource allocation scheme is proposed for downlink joint enhanced mobile broadband (eMBB) and ultra reliable low latency communication (URLLC) traffic with different quality-of-service (QoS) requirements. eMBB and URLLC are two service categories in the emerging fifth generation (5G) cellular networks which encompass applications with high throughput and stringent latency and reliability demands, respectively. To meet these heterogeneous requirements, 5G networks need impressive improvements in the air interface and core network architecture. In this paper, by applying a scalarization method for multi- objective optimization, both the eMBB and URLLC performance in the downlink channel are improved. Different types of URLLC users with various latency requirements are considered. To further enhance the reliability, the URLLC users with stringent delay demands are prioritized over the users with looser latency needs. In the simulation results, the performance of the proposed resource allocation policy is assessed and it is shown that our introduced scheme can achieve higher reliability and lower delay for the URLLC traffic compared with the other approaches in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".