# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Antenna Position, Direction, and Radiation Estimation  
ML techniques have been significantly applied in estimating antenna position and direction to achieve the maximum gain in transmission and receiver systems. It assists in the detection and control of beam phasing of antennas based on signal patterns, strength, and target location [79; 80]. The direction of arrival (DoA) estimation has become a popular application in military-civil research for remote object detection [81; 82; 83]. The fixed-antenna method for DoA estimation has limitations, hence antenna arrays have been used as an alternative in practice. The selection of suitable antennas for an antenna array and their positioning for multiple-input single-output (MISO) applications using ML [84] is gaining popularity due to its optimized computational time and performance. [PERSON] et al. [81] studied an NN-enabled algorithm to estimate DoA with lower computational complexity. A modified genetic algorithm (GA) was implemented in another study [85] to predict the optimal position of the antenna in an RF-based advanced driving assistance system. A previous article [79] described a rotating, elevated antenna technique assisted by ML to estimate the angle for a better field of view and cost-effective automation. In another study [86], ANN was used to estimate beam alignment and distribution without prior knowledge of user location information. [PERSON] et al. [87] described a directional antenna design scheme using multi-objective GA to reduce the number of antenna side lobes and the lobe level, which eventually aided directivity and security in wireless communication. In a prior report [88], GPR was used for computing the resonant frequency of a square microstrip patch antenna. ML algorithms perform better in both linear and complex problems [89], where different scattering parameters are estimated in the UHF band of an antenna operating at a resonance frequency. An earlier study [90] provided a predictive model using ANN, which can predict the resonant frequency of a flexible microstrip patch antenna considering an inserted airgap. In a past investigation [91], the Support Vector Regressor (SVR) method was introduced to predict the EM response of complex reflect-array antenna elements with complex shapes. SVR enabled reliable, accurate, and fast EM response estimation (15% more time was saved compared with the FW simulation). Furthermore, a recurrent neural network (RNN) was used to suppress harmonics in wireless communication [92]. Deep neural networks (DNN) aid in reduced computational complexity while predicting the optimized direction of arrival  
Figure 5: Secrecy Outage Probability (SOP) of single- and two-antenna selection for \(\beta=1\) and \(\mathrm{Rt}=2\,\mathrm{bps}/\mathrm{Hz}\)[73]. (DoA) [93]. The study also showed how a hybrid multilayered network can both detect the optimum antenna from an antenna array and estimate DoA at the same time. Figure 6 shows the model architecture for the complex algorithm to estimate DoA reported in that study [93]. Another study [94] proposed a deep learning model to accurately track mobile stations and point to the satellite antenna to obtain an interference-free communication link. To obtain the desired radiation pattern, [PERSON] and [PERSON] [95] used a composite ANN approach of a hypernetwork. Recurrent learning was shown to have the scope for advancing the control of antenna such as safe antenna tilting at a remote distance [96]. Reconfigurability in antenna can be achieved through different mechanisms such as electrical, optical, physical, and/or material changes [97; 98; 99; 100; 101]. ML has become popular with reconfigurable antennas, specifically frequency-reconfigurable ones [102; 103]. For example, an intelligent surrogate model-assisted differential evolution to synthesize an antenna array [24] with reconfigurable frequency was reported earlier.