# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
###### Abstract
In actual deployment, the high complexity channel estimation schemes and beamforming algorithms may cause the instantaneous CSI obtained by LEO satellites to be out of date [21], which would dramatically diminish the system's performance. Fortunately, artificial intelligence (AI) technology provides simple approaches to address such complex problems [21; 22; 23; 24]. [PERSON] et al. [22] investigated secure physical communication based on IRS under the condition of time-varying channel coefficients and proposed a deep reinforcement learning approach to jointly optimize both BS and IRS beamforming. [PERSON] et al. [23] established a deep transfer learning framework to solve the beamforming optimization problem for the IRS-assisted MISO system. [PERSON] et al. [24] trained a graph neural network (GNN) architecture to directly map the received pilots to the IRS's phase shifts and BS beamforming matrix. However, Ref. [24] merges user location and pilots directly, resulting in the location features being easily ignored. In addition, due to the long distance between the satellite and users, the pilots received by the satellite is insensitive to user position information. Therefore, this approach cannot be effectively utilized in our system. Note that existing research on IRS mainly focuses on the ground cellular network system, and most research has been based on a static environment. When the application is extended to LEO satellite and the users' location may also change, the algorithm's computational complexity has a significant impact on performance. In this paper, we commit to applying AI technology to solve the complex beamforming design problem in an IRS-assisted LEO satellite communication system. Specifically, the IRS is used to provide additional reflective links to overcome the serious attenuation caused by occlusion between the LEO satellites and users' direct links. In our work, we establish a global optimization problem for maximizing the sum-rate of mobile users by optimizing both active and passive beamforming schemes jointly. To solve this problem, we propose a robust beamforming approach based on graph attention networks (RBF-GAT). Here are the main contributions of this paper:  
First, we propose a novel architecture for LEO satellite IoT networks assisted with IRS. A deep neural network (DNN) architecture based on graph attention networks (GAT) [25]is constructed to capture dynamic network topology in real-time as a result of the mobility of the satellite and its users. Second, a composite neural network combining a GAT layer and multiple fully connected (FC) layers is used to directly map the received pilots and network topology to the satellite and IRS beamforming, eliminating the necessity for channel estimation. To reduce the complexity of the RBF-GAT model, the mapping neural networks of satellite and IRS use the same feature extraction layers before the last normalization layer. Third, we define a loss function to implement unsupervised training offline, thereby avoiding the labeling overhead that occurs in traditional supervised learning. Simulation results demonstrate that the proposed RBF-GAT approach, in well-trained conditions, will be able to approach upper-bound sum rate with a low level of computational complexity. The remainder of this paper is organized as follows. Section 2 introduces the IRS-assisted LEO satellite communication system model. Section 3 describes the detailed architecture of RBF-GAT and the training process. Section 4 gives the simulation results and the complexity analysis. Section 5 presents the concluding remarks of this paper. Notations: Boldface letters are used to denote vectors or matrices. \(\mathbb{C}^{m\times n}\), \(\mathbb{R}^{m\times n}\), and \(\mathbb{R}^{m}\) represent the \(m\times n\) complex, real matrices and m-dimensional real vector, respectively. The distribution of complex Gaussian random variables with mean \(\mu\) and variance \(\sigma^{2}\) are denoted by \((\mu,\sigma^{2})\).