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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".