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dc.contributor.authorDahrouj, Hayssam
dc.contributor.authorAlghamdi, Rawan
dc.contributor.authorAlwazani, Hibatallah
dc.contributor.authorBahanshal, Sarah
dc.contributor.authorAhmad, Alaa Alameer
dc.contributor.authorFaisal, Alice
dc.contributor.authorShalabi, Rahaf
dc.contributor.authorAlhadrami, Reem
dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorAlnory, Malak
dc.contributor.authorKittaneh, Omar
dc.contributor.authorShamma, Jeff S.
dc.identifier.citationDahrouj, H., Alghamdi, R., Alwazani, H., Bahanshal, S., Ahmad, A. A., Faisal, A., … Shamma, J. S. (2021). An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing. IEEE Access, 1–1. doi:10.1109/access.2021.3079639
dc.description.abstractDespite 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.
dc.description.sponsorshipThis work was supported in part by the Center of Excellence for NEOM Research at the King Abdullah University of Science and Technology (KAUST). Jeff S. Shamma contributed to this work when he was with the CEMSE division at KAUST, where he still holds an adjunct professor position.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.
dc.titleAn Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
dc.identifier.journalIEEE Access
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of British Columbia, Kelowna, Canada.
dc.contributor.institutionRuhr-Universitat Bochum, 44780, Germany.
dc.contributor.institutionCollege of Engineering, Effat University, Jeddah 22332, Saudi Arabia.
dc.contributor.institutionUniversity of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois 61801-3080, USA.
kaust.personDahrouj, Hayssam
kaust.personAlghamdi, Rawan
kaust.acknowledged.supportUnitCenter of Excellence for NEOM Research

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