AI-Enabled Framework for Energy Sustainability Evaluation and Enhancement of Wireless Networks
Bibliographic record
Abstract
In the pursuit of sustainable wireless connectivity, this paper leverages artificial intelligence (AI) to evaluate and enhance the energy sustainability of wireless networks. To begin, energy sustainability is astutely defined, encompassing both the short-term metric and, more critically, long-term evaluations, all while taking careful consideration of the dynamics of the wireless operation environment. Then, treating the task at hand as a sequential Markov decision process, an AI-driven framework powered by quantum reinforcement learning (QRL) is proposed to evaluate the long-term energy sustainability and leverage it to maximize the short-term sustainability, particularly in terms of the instantaneous energy efficiency. A case study is also presented, where the framework is deployed within the context of an aerial base station aided wireless communication network, showcasing the effectiveness and real-world applicability of the proposed framework. The results demonstrate significant reductions in energy consumption and improvements in energy efficiency versus the approach devoid of long-term prediction, underscoring the notable advantages of the new framework. Furthermore, the QRL-based solution consistently outperforms conventional deep reinforcement learning techniques, highlighting its superior capability in balancing short-term performance with long-term sustainability objectives. It also demonstrates advantages in terms of faster convergence in dynamic wireless environments and efficient operation in high-dimensional state and action spaces.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".