Utilization of machine learning in future wireless networks for resource optimization: A survey
Bibliographic record
Abstract
Future wireless networks will play an essential role as the need for performance and feature availability grows. Most of the traffic in future wireless networks is due to increased Internet of things (IoT) devices, making resource optimization critical. Traditional optimization algorithms have limitations due to their high computational complexity, which restricts their use in modern applications. To address this, machine learning algorithms are now the preferred alternative to traditional optimization algorithms due to their improved runtime complexity. We present a comprehensive survey on the use of machine learning for resource optimization in future wireless networks. The use of machine learning is divided into three categories: (i) comprehensive solutions, where machine learning is the primary component of the solution approach; (ii) partial solutions, where machine learning is used alongside a traditional approach for optimization; and (iii) environment-only solutions, where optimization is performed in a machine-learning environment. We have further classified objective functions (e.g., energy, latency, data rate, etc.) within each category based on the pure objective function, variations on the objective function, and objective function tradeoffs with respect to other objective functions. We present objective functions and constraints used in the literature for optimization problem formulation. We provide an overview of frequently used machine learning algorithms for resource optimization, followed by a detailed survey of machine learning works in the literature in the three aforementioned categories. Finally, we discuss future research directions for utilizing machine learning to optimize resource management in future wireless 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".