DRL-Based Green Resource Provisioning for 5G and Beyond Networks
Bibliographic record
Abstract
Networks play a crucial role in our daily lives by efficiently delivering multimedia services. However, network operators face the challenge of ensuring efficient resource provisioning while balancing profit maximization and environmental objectives. Deep reinforcement learning (DRL) has emerged as an approach for resource allocation, leveraging observed data to make near-optimal decisions. However, the dynamic and complex nature of large-scale environments, such as wireless networks, poses challenges in designing appropriate rewards for DRL agents. This study investigates the feasibility of parallelization approaches to create network environments that facilitate efficient and generalizable learning for DRL algorithms. We propose two service provisioning solutions: DRL-MCTS for wireless networks and DRL-VNF for wired networks. These solutions prioritize green network objectives, including minimizing power consumption for nodes and links and selecting low carbon-emitting links. We compare our proposals to baseline methods, including a greedy algorithm, WMMSE, and the optimal solution. Through extensive experimental evaluations, our methods demonstrate significant improvements in reducing the network’s environmental footprint while meeting Quality of Service requirements. In specific scenarios, our proposed solutions outperform the baseline approaches by up to 55%.
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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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".