DRL-Leveraged and RIS-Assisted Hybrid Network Slicing for eMBB and URLLC Co-Existence in 6G Systems
Bibliographic record
Abstract
In this paper, we design a Reconfigurable Intelligent Surface (RIS)-assisted down-link cellular network for the co-existence of enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) services in 6G. A novel hybrid slicing technique comprising Non-orthogonal Multiple Access (NOMA) and puncturing is proposed to schedule the URLLC transmission on top of the already scheduled eMBB traffic. We propose a slot-based eMBB scheduling to schedule the eMBB flows at the beginning of the slot, and a mini-slot-based URLLC scheduling to insert the mini-slots on top of the scheduled eMBB traffic. The overall objective is to maximize the URLLC packets admission rate and minimize the eMBB rate loss while satisfying the Quality of Service (QoS) demands of both eMBB and URLLC services. The eMBB allocation problem is solved using Alternating Optimization (AO) by optimizing the RIS phase shift and eMBB power. The URLLC allocation is achieved using a Deep Reinforcement Learning (DRL)-based Proximal Policy Optimization (PPO) algorithm. The performance of the proposed technique with hybrid network slicing is compared with the NOMA-based and puncturing-based slicing, and other state-of-the-art (SOTA) techniques for URLLC allocation. The comparison results show that the proposed hybrid network slicing outperforms these techniques. Specifically, it is observed that with the worst-case URLLC load, the proposed solution provides 5.31% and 144% improvements in the URLLC packet admission rate and eMBB sum rate, respectively, compared to an optimization-based SOTA URLLC allocation technique. Similarly, there is an improvement of 78.36% in execution time compared to the SOTA URLLC allocation technique based on the heuristic algorithm.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".