Stochastic Geometric Analysis of the Terahertz (THz)-mmWave Hybrid Network With Spatial Dependence

The Terahertz (THz) band (0.1-10 THz) contains abundant spectrum resources that can offer ultra-high data rates. Despite these potential benefits, the adoption of THz communication has been stagnant until very recently due to the poor penetrability and limited coverage of the THz links. To overcome the aforementioned obstacles and take full advantage of the THz band, we introduced a hybrid network consisting of THz and millimeter-wave (mmWave) nodes deployed within a finite area. Furthermore, the mmWave nodes are spatially distributed by a Poisson Point Process (PPP), whereas the THz nodes are clustered around the mmWave nodes, forming a Poisson Cluster Process (PCP) with the parent process of mmWave tier. We derive the Laplace transform of the interference in a closed form and evaluate the coverage probability (CP) based on the maximum biased power (Max-BRP) association strategy. The proposed framework provides insights into how the spatial dependence between THz and mmWave tier and clustering setting affects network performance. We quantitatively reveal its impact on the network performance. It is revealed that this inter-tier spatial dependence introduces the flexibility of nodes deployment by tuning the scattering variance and the number of nodes per cluster. Furthermore, we use numerical simulation to demonstrate the significant impact of bias ratio, density, and blockage on the CP, indicating the importance of choosing the optimal combination of the parameters.

Automated summary

The Terahertz band offers abundant spectrum resources for ultra-high data rates, but its adoption has been limited due to poor penetrability and coverage. By combining Terahertz and millimeter-wave nodes in a hybrid network, we can overcome these obstacles and enhance network performance. Our analysis of interference and coverage probability reveals how spatial dependence and clustering settings affect network performance, and numerical simulations demonstrate the importance of optimizing parameters like bias ratio, density, and blockage.