Enhancing Next-Generation Urban Connectivity: Is the Integrated HAPS-Terrestrial Network a Solution?

TL;DR

This paper proposes an integrated HAPS-terrestrial network for urban areas, addressing interference challenges in spectrum sharing, and introduces an iterative algorithm to optimize MIMO beamforming and user association, improving spectral efficiency.

Contribution

It formulates a fairness optimization problem for integrated HAPS-terrestrial networks and develops a rapid converging algorithm to enhance network performance.

Findings

The proposed algorithm outperforms standalone terrestrial networks.

Optimizing both beamforming and user association yields better spectral efficiency.

Simulation results validate the effectiveness of the integrated network approach.

Abstract

Located in the stratospheric layer of Earth's atmosphere, high altitude platform station (HAPS) is a promising network infrastructure, which can bring significant advantages to sixth-generation (6G) and beyond wireless communications systems by forming vertical heterogeneous networks (vHetNets). However, if not dealt with properly, integrated networks suffer from several performance challenges compared to standalone networks. In harmonized spectrum integrated networks, where different tiers share the same frequency spectrum, interference is an important challenge to be addressed. This work focuses on an integrated HAPS-terrestrial network, serving users in an overlapped urban geographic area, and formulates a fairness optimization problem, aiming to maximize the minimum spectral efficiency (SE) of the network. Due to the highly nonconvex nature of the formulated problem, we develop a rapid converging iterative algorithm that designs the multiple-input multiple-output (MIMO) beamforming weights and the user association scheme such that the propagated inter- and intra-tier interference is managed. Simulation results demonstrate the proposed algorithm's superiority over standalone terrestrial networks and scenario where only the beamforming weights are optimized.