Energy-Efficient Resource Allocation for FeMBB and eURLLC Coexistence in RSMA-Based Wireless Networks
Bibliographic record
Abstract
Emerging 5G-Advanced and 6G wireless networks are anticipated to support a wide array of services, including further enhanced mobile broadband (FeMBB) and extreme ultra-reliable low-latency communications (eURLLC), to meet diverse communication needs. The radio access network (RAN) slicing is a pivotal technology for enabling the delivery of these services on shared infrastructure, playing a particularly important role in 6G, where FeMBB and eURLLC services have different blocklengths. To meet varying quality of service (QoS) demands in next-generation networks, innovative multiple access techniques are required to improve interference management and optimize spectrum efficiency. Rate-splitting multiple access (RSMA) is an effective approach for achieving these objectives. This paper investigates the problem of Energy-efficient joint Resource block (RB) allocation and Power control (ERP) for the coexistence of FeMBB and eURLLC services in RSMA-based green communication networks. In this ERP problem, each FeMBB user is guaranteed a minimum data rate, while each eURLLC user must satisfy latency and reliability constraints. To address the ERP problem, we introduce a sub-optimal algorithm (SO-ERP) based on convex optimization. However, the SO-ERP algorithm has high computational complexity and requires approximations to convexify the original ERP problem, potentially moving the solution away from the optimum. To overcome these limitations, we propose a hybrid deep reinforcement learning (HDRL-ERP) algorithm that employs a dueling double deep Q-network for RB allocation and a deep deterministic policy gradient for power control. Simulation results are presented to illustrate the performance of SO-ERP and HDRL-ERP algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".