Toward AI-Enabled Green 6G Networks: A Resource Management Perspective
Bibliographic record
Abstract
The development of 6G wireless networks is driven by the pressing need for reliable connectivity in the increasingly intelligent Internet of Things (IoT) ecosystem. The goal of these networks is to seamlessly connect individuals, devices, vehicles, and resources such as the cloud. However, the heterogeneity and complexity of 6G due to the proliferation of devices, diverse applications, and the need for green and sustainable communication networks, pose significant Resource Management (RM) challenges. Furthermore, the stringent requirements of 6G networks for Quality-of-Service (QoS), scalability, intelligence, and security can make traditional RM approaches ineffective, particularly considering Energy Efficiency (EE). In response to these challenges, Artificial Intelligence (AI) has been considered to provide green RM. AI techniques can be used to efficiently manage network resources, balance energy demands, optimize EE, and integrate Energy Harvesting (EH). This paper examines 6G networks from an AI perspective to optimize resource allocation, minimize energy consumption, and maximize network performance. The focus is on RM within these networks considering Radio Resource Management (RRM), Computing and Caching Resource Management (CCRM), and Communication Network Resource Management (CNRM). The emphasis is on RM within the Cellular Network Infrastructure (CNI) and Machine Type Communications (MTC). AI models for efficient resource utilization to enhance EE and network performance are investigated. It is shown that AI plays a pivotal role in achieving green RM within 6G networks. Future research directions are outlined for intelligent networks to meet the growing demands and emerging challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".