Joint Resource and Power Allocation for URLLC-eMBB Traffics Multiplexing in 6G Wireless Networks
Bibliographic record
Abstract
Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".