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Review
. 2022 Apr 22;8(4):e09317.
doi: 10.1016/j.heliyon.2022.e09317. eCollection 2022 Apr.

A review on machine learning and deep learning for various antenna design applications

Mohammad Monirujjaman Khan  1 , Sazzad Hossain  1 , Puezia Mozumdar  1 , Shamima Akter  1 , Ratil H Ashique  2
Affiliations
Review

A review on machine learning and deep learning for various antenna design applications

Mohammad Monirujjaman Khan et al. Heliyon. .

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.

Keywords: Antenna; Beam-forming; Body-centric; CDF; CNN; DNN; Deep MIMO; Frequency; GSCM; LOS; Machine learning; Meta-material identification; Millimeter wave; NLOS; PDP; RFC; Radio frequency; THz DL CT; THz communications.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Map-based model with its characteristics [25].
Figure 2
Figure 2
Performance evolution of map-based channels [25].
Figure 3
Figure 3
Three modules (signal transformation, information extraction, and the neural network)of the proposed model [102].
Figure 4
Figure 4
Three real test sceneries of the model [2]. (a) First Scenario, (b) Second Scenario, (c) Third Scenario.
Figure 5
Figure 5
Schematic diagram of THz TDS system [37].
Figure 6
Figure 6
Schematic diagram of the THz DL CT model [37].
Figure 7
Figure 7
(a) Comparison between THZ CT and THz DL- CT. (b) Numerical metrics on two algorithms, (c) Visible image and 3D THz images by THz DL-CT on a testing object [37].
Figure 8
Figure 8
Workflow of private preserving THz metamaterial identification [39].
Figure 9
Figure 9
6G based on the time-frequency-space resource utilization [40].
Figure 10
Figure 10
Some promising techniques of 6G network [40].
Figure 11
Figure 11
Antenna elements (AEs), a phased array antenna (PAA) system, and an optical beamforming network (OBFN) are all examples of optical beamforming networks [52].
Figure 12
Figure 12
The right diagram is its neural network configuration and in the left there is OBFN system (4 × 1) [52].
Figure 13
Figure 13
(A) A triangular PD, π(x), and (B) the corresponding possibility Π (solid) and necessity N (dashed) measures [78].
Figure 14
Figure 14
Flowchart of BO algorithm [78].
Figure 15
Figure 15
Proposed hybrid algorithm [78].
See this image and copyright information in PMC

References

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