An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing
Bibliographic record
Abstract
Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learning-driven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicle-to-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".