# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods
#### 4.2.2 Sparse Area Array Optimization Based on Artificial Intelligence Methods  
Artificial intelligence methods have attracted great attention in recent years [59], and they are widely used in all walks of life, opening up new ways for intelligent optimization methods. Especially in mobile communication scenarios, the artificial intelligence method adaptively adjusts the weight coefficients of each element in the antenna array on the basis of the dynamic perception of the electromagnetic environment in space, and then changes the pattern shape, polarization, and other performance parameters of the smart antenna while in orbit. The ultimate goal is to enable the satellite communication load to respond effectively to the electromagnetic environment it faces, thereby achieving optimized communication performance. Some scholars have begun applying artificial intelligence methods such as deep learning (DL), artificial neural network (ANN), support vector machine (SVM), and Gaussian regression (GP) to the optimization design of intelligent antenna arrays (SAA). The advantages of deep learning (DL) are its strong learning ability and good adaptability, and it is generally used to solve the adaptive beamforming problem of sparse area arrays. For example, the authors of [60] proposed a beamforming method based on deep learning, constructed a convolutional neural network (CNN) model to calculate the optimal weight vector, and calculated the excitation weight in real time. This method could still show good beamforming performance when the array had amplitude-phase error and mutual coupling. The artificial neural network has the function of self-learning and high-speed optimization, and it is good at finding the relationship between certain variables and results from complex processes, which is often used to improve the complexity of array optimization calculation and deal with the coupling effect of sparse area arrays. For example, the authors of [61] used the artificial neural network (ANN) to construct a multilayer feedforward neural network with supervised learning by introducing the numerical electromagnetic radiation map of the coupled periodic array as a database, which effectively solved the sparse area array synthesis problem, including the coupling effect. This method considers the coupling between elements, has good directionality, and has significant advantages in speed and memory consumption, but the peak sidelobe level of this method is high, and the suppression effect is not obvious. In order to solve this problem, the authors of [62] used a neural network to carry out adaptive optimization design of array element radiation performance and array element spacing, used the convex optimization method to optimize the array comprehensively, and proposed an antenna array optimization design method based on an artificial neural network (ANN) and convex optimization. This method considers the coupling between array elements and synergistically optimizes the array factor, which can control the main lobe and obtain a lower peak sidelobe. Support vector machines (SVMs) are good at dealing with problems that require classification and decision making in sparse area array design, such as antenna array element failure problems [63], which can improve the robustness of the array. At the same time, SVM is a convex optimization problem, which can also be used to assist the optimization design of sparse area arrays [64]. In summary, the sparse area array optimization method based on artificial intelligence algorithms is more comprehensive and more robust, while having a shorter calculation time and higher calculation accuracy, which is very suitable for scenarios such as wireless communication that require real-time and adaptive optimization and adjustment of arrays. At present, in the optimization design of sparse area arrays, the application of this type of method is relatively rare. Nevertheless, in the past 1 or 2 years, the combination of artificial intelligence methods and antenna-related fields has strengthened.