Adaptive Bandwidth Allocation in Multiuser MIMO THz Systems with Graph-Transformer Networks

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. 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 heterogeneous graph-transformer network (HGTN) to bypass the channel estimation phase. We formulate a sum-rate maximization problem with quality-of-service (QoS) constraints in a multiuser multiple-input multiple-output (MU-MIMO) THz system to optimize the precoding and ASB allocation. The proposed HGTN parameterizes the mapping from input features (e.g., location information, users' minimum data rate) to the optimized system parameters via unsupervised learning. The proposed HGTN can be applied to systems with different number of users once it is trained. Simulation results show that our proposed HGTN achieves a higher system sum-rate with faster convergence when compared with the unsupervised deep neural network learning algorithm.