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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".