A review on machine learning and deep learning for various antenna design applications. Issue 4 (April 2022)

• Record Type: Journal Article
• Authors: Khan, Mohammad Monirujjaman; Hossain, Sazzad; Mozumdar, Puezia; Akter, Shamima; Ashique, Ratil H.
• Is Part Of: Heliyon. Volume 8:Issue 4(2022)
• Publication Date: 2022-04
• Subjects: Deep MIMO -- Beam-forming -- Machine learning -- LOS -- NLOS -- Antenna -- DNN -- CDF -- GSCM -- PDP -- CNN -- Millimeter wave -- THz communications -- Body-centric -- Radio frequency -- THz DL CT -- Frequency -- RFC -- Meta-material identification; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2022.e09317
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: The next generation of wireless communication networks will rely heavily on machine learning and deep learning. In comparison to traditional ground-based systems, the development of various communication-based applications is projected to increase coverage and spectrum efficiency. Machine learning and deep learning can be used to optimize solutions in a variety of applications, including antennas. The latter have grown popular for obtaining effective solutions due to high computational processing, clean data, and large data storage capability. In this research, machine learning and deep learning for various antenna design applications have been discussed in detail. The general concept of machine learning and deep learning is introduced. However, the main focus is on various antenna applications, such as millimeter wave, body-centric, terahertz, satellite, unmanned aerial vehicle, global positioning system, and textiles. The feasibility of antenna applications with respect to conventional methods, acceleration of the antenna design process, reduced number of simulations, and better computational feasibility features are highlighted. Overall, machine learning and deep learning provide satisfactory results for antenna design. Abstract : Deep MIMO, beam-forming, machine learning, LOS, NLOS, antenna, DNN, CDF, GSCM, PDP, CNN, millimeter wave, THz communications, body-centric, radio frequency, THz DL CT, frequency, RFC, meta-material identification, THz-TDS, deep learning, 6G, Satellite, artificial intelligence, Beam hopping, reflectarray, Direct Broadcast Satellite (DBS), space communications, 5G, UAV, Cellular networks, radio access network, reinforcement learning, unmanned aerial vehicle, Radio Frequency Identification (RFID), GPS receiver, GPS spoofing, GPS meaconing, textile, RT, Nyusim, deep neural network, RSU, channel estimation, large intelligent surfaces, channel extrapolation and FDD.