Energy-Efficient Resource Allocation in 6G Wireless Networks Using AI-Driven Optimization and Edge Intelligent
Bibliographic record
Abstract
The arrival of sixth-generation$(6 \mathrm{G})$wireless networks opens up new possibilities for very reliable, fast, and smart communication systems. At the same time, it also brings new problems related to energy use and the environment. The suggested model focusses on important factors for allocation, like bandwidth, power, and spectrum, and turns energy economy into a mathematical optimization problem. We use machine learning models for predicted distribution, reinforcement learning for flexible energy management, and federated learning for decentralized optimization without losing data protection to deal with this level of complexity. Edge-enabled designs are also used to cut down on delay and improve real-time decision-making, which moves computing chores closer to the user. When AI algorithms and edge intelligence work together, the framework not only improves energy and spectrum efficiency, but it also makes sure that quality of service stays strong even when network conditions change. Comparative research shows that these methods are much better than traditional ones in terms of using less power, making better use of bandwidth, and keeping network speed good. This study helps make 6 G systems that are sustainable by combining advanced optimization methods with energy-conscious design principles. It gives useful information on how to set up smart, scalable wireless networks for the next age of communication networks.
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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) | 0.001 | 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".