GNN-Transformer for Real-Time Power-Constrained Active RIS Configuration in Terahertz Communications

Abstract

Terahertz (THz) communication is an essential component of sixth-generation (6G) wireless networks; however, its use is constrained by molecular absorption, ultrawideband beam squint, high- $\kappa $ Rician fading, and beam misalignment. The multiplicative fading penalty of passive RIS design can be avoided, and reliable coverage can be extended into the THz band using active, reconfigurable intelligent surfaces (A-RISs) that integrate per-element amplifiers. However, the simultaneous optimization of discrete phase shifts and continuous per-element amplification factors is a nonconvex, high-dimensional problem that is difficult to solve with conventional iterative solvers for the submillisecond coherence times of mobile THz users. A hardware-aware A-RIS-assisted THz communication framework is presented in this article, with an extensive channel model that accounts for frequency-selective molecular absorption, ultrawideband beam squint, high- $\kappa $ Rician fading ( $\kappa \in [{10,25}]$ dB), and stochastic beam misalignment. We present an unsupervised graph neural network-Transformer (GNN-Transformer) architecture to solve the resulting joint optimization problem in real time. Graph convolutional layers exploit local spatial dependencies among RIS elements, while Transformer attention layers capture global channel dependencies across the entire RIS panel. The network is trained end-to-end on channel realizations but, unlike optimal labeling, does not require optimal discrete-phase and continuous-amplification configurations, which are obtained in a single forward pass. The results of the simulations show that the proposed method, which performs an exhaustive search, achieves a higher SNR, meets the power budget without violating it, and reduces inference latency compared to successive convex approximation (SCA), thereby enabling large-scale deployments of THz A-RIS in real time.