Joint Spectrum, Precoding, and Phase Shifts Design for RIS-Aided Multiuser MIMO THz Systems

Terahertz (THz) wireless systems aim to support content-rich applications with ultra-high data rate. Due to high molecular absorption, THz signals experience severe path loss over long distance. To alleviate distance limitation, reconfigurable intelligent surface (RIS) can improve the coverage range. Adaptive sub-band bandwidth (ASB) allocation can mitigate absorption attenuation by allocating THz sub-bands with variable bandwidth to the users. However, in ASB allocation, since the bandwidth of sub-bands may not be known
a priori
, accurate channel estimation is challenging. To overcome this issue, in this paper, we propose a metapath-based heterogeneous graph-transformer network (MHGphormer) to bypass the channel estimation phase. We formulate a sum-rate maximization problem with quality-of-service (QoS) constraints in a RIS-aided multiuser multiple-input multiple-output (MU-MIMO) THz system to optimize the precoding, phase shifts, and ASB allocation. The proposed MHGphormer parameterizes the mapping from input (e.g., location information, users’ minimum data rate) to the optimized system parameters via unsupervised learning. The proposed MHGphormer has the permutation invariance/equivariance property. It can be applied to systems with different number of users. Simulation results show that our proposed MHGphormer achieves a higher system sum-rate when compared with the homogeneous graph neural network, unsupervised deep neural network, and alternating optimization baseline algorithms.