# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
###### Abstract  
Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite's precoding matrix and IRS's phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS's beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity. intelligent reflecting surface; low Earth orbit satellite; graph attention networks; unsupervised learning; beamforming +
Footnote †: journal: _Entropy_  
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Footnote 1: [[https://www.mdpi.com/journal/entropy](https://www.mdpi.com/journal/entropy)]([https://www.mdpi.com/journal/entropy](https://www.mdpi.com/journal/entropy))  
2022243266103390/202224326103390/20222432610390/20222432610390/20222432610390/2022243261039  
areas with no terrestrial infrastructure. Moreover, in practical satellite systems, line-of-sight (LoS) communication between satellite and terrestrial users is difficult to maintain due to obstacles and shadowing [8]. In [9], the authors investigated unmanned aerial vehicle (UAV) swarms and LEO satellite constellation-assisted data collection for IoRT networks, where UAV swarms were used as relays to improve the channel environment. UAVs have the benefits of high mobility, flexible deployment, and LoS transmission [10; 11]. However, when introducing UAVs in LEO-assisted IoRT networks, data transmission becomes more challenging because of the different channel characteristics of UAV-ground and UAV-satellite links, as well as the battery and cache capacity limitations of UAVs. Considered promising technology, intelligent reflecting surfaces (IRSs) have recently received substantial attention [12; 13; 14; 15; 16]. IRS consists of a large number of passive elements which can introduce controllable phase shifts. Intelligently adjusting these phases can change the reflected signal propagation. Therefore, it has been widely deployed in wireless communication systems to enhance the intended signal power at the receiver or mitigate the cochannel interference [14; 15; 16]. Refs. [14; 15] investigated the weighted sum rate and transmitted power in an IRS-aided MISO system, respectively, by jointly optimizing the transmit beamforming vectors at the base stations (BSs) and the reflective beamforming vector at the IRS. Ref. [16] studied the secure transmission optimization for IRS-assisted STINs. Most of the existing literature assumes that the perfect channel state information (CSI) is known. However, this assumption is impractical because the number of IRS reflecting elements is large and not capable of performing active transmission/reception and signal processing [17]. Previous studies proposed various channel estimation schemes in the IRS-assisted multiuser system [17; 18; 19; 20]. However, the abovementioned methods are based on the BSs have fixed locations, and the variation of BS-IRS common channel is feeble. These approaches cannot be efficiently applied to the IRS-assisted LEO satellite system since the high-speed scenarios with fast time-varying channels would be updated more frequently.