HAPS Selection for Hybrid RF/FSO Satellite Networks

TL;DR

This paper proposes a hybrid satellite communication model using HAPS nodes with FSO/RF links, deriving outage probability expressions and analyzing factors affecting system performance to guide practical HAPS-aided SatCom design.

Contribution

Introduces a novel HAPS selection model for hybrid FSO/RF satellite networks with performance analysis and practical design guidelines.

Findings

HAPS improves satellite communication performance.

The proposed model outperforms existing models.

Aperture averaging and environmental factors significantly impact system reliability.

Abstract

Non-terrestrial networks have been attracting much interest from the industry and academia. Satellites and high altitude platform station (HAPS) systems are expected to be the key enablers of next-generation wireless networks. In this paper, we introduce a novel downlink satellite communication (SatCom) model where free-space optical (FSO) communication is adopted between a satellite and a HAPS node. A hybrid FSO/radio-frequency (RF) transmission model is used between the HAPS node and ground station (GS). In the first phase of transmission, the satellite selects the HAPS node that provides the highest signal-to-noise ratio (SNR). In the second phase, the selected HAPS decodes and forwards the signal to the GS. To evaluate the performance of the proposed system, outage probability expressions are derived for exponentiated Weibull (EW) and shadowed-Rician fading models while considering the atmospheric turbulence, stratospheric attenuation, and attenuation due to scattering, path loss, and pointing errors. Additionally, asymptotic analysis is carried out and diversity gain is provided. Furthermore, the impact of aperture averaging technique, temperature, and wind speed are investigated. We also provide some important guidelines that can be helpful for the design of practical HAPS-aided SatCom. Finally, the results show that the use of HAPS improves the system performance and that the proposed model performs better than all other existing models.