Hybrid Network Slicing Technique for Co-existence of eMBB and URLLC Services in 6G-IoT Systems
Bibliographic record
Abstract
G wireless networks will serve billions of Internet of Thing (IoT) devices with stringent latency and reliability requirements for various applications. Therefore, the three service classes introduced by 5G systems: Enhanced Mobile Broad Band (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC) and massive Machine-Type Communications (mMTC), have to meet stricter requirements in $\mathbf{6 G}$ systems. In this paper, we design a novel hybrid network slicing technique comprised of both orthogonal and non-orthogonal slicing schemes to achieve the optimal sum rates for joint eMBB and URLLC services. With the help of power inversion and Successive Interference Cancellation (SIC) in Non-Orthogonal Multiple Access (NOMA), we allow different combinations of frequency resources for Orthogonal Multiple Access (OMA) and NOMA to design frequency diversity-aided hybrid network slicing, which can accommodate multiple URLLC IoT users in the uplink communication. We perform an extensive numerical investigation to analyze the sum rates of eMBB and URLLC IoT users given average channel gain and reliability requirements for each service class, and show that the proposed hybrid technique outperforms OMA and NOMA in terms of maximum sum rates of both service classes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".