TY - JOUR
T1 - An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing
AU - Dahrouj, Hayssam
AU - Alghamdi, Rawan
AU - Alwazani, Hibatallah
AU - Bahanshal, Sarah
AU - Ahmad, Alaa Alameer
AU - Faisal, Alice
AU - Shalabi, Rahaf
AU - Alhadrami, Reem
AU - Subasi, Abdulhamit
AU - Al-Nory, Malak T.
AU - Kittaneh, Omar
AU - Shamma, Jeff S.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Optimization
KW - aerial BS placement
KW - convolutional neural networks
KW - deep learning
KW - echo-state networks
KW - federated learning
KW - learning-based techniques
KW - power control
KW - recurrent neural networks
KW - reinforcement learning
KW - virtual reality
KW - wireless scheduling
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U2 - 10.1109/ACCESS.2021.3079639
DO - 10.1109/ACCESS.2021.3079639
M3 - Review article
AN - SCOPUS:85105891348
SN - 2169-3536
VL - 9
SP - 74908
EP - 74938
JO - IEEE Access
JF - IEEE Access
M1 - 9429227
ER -