CNN-aided Channel and Carrier Frequency Offset Estimation for HAPS-LEO Links

TL;DR

This paper proposes a CNN-based method for channel and frequency offset estimation in HAPS-LEO satellite links, improving data throughput and service quality in dynamic, high-mobility environments.

Contribution

It introduces an AI-driven approach for channel estimation and synchronization tailored for HAPS-LEO networks, enhancing spectral efficiency and signal reconstruction.

Findings

High data throughput achieved

Improved service quality observed

Effective Doppler effect mitigation

Abstract

Low Earth orbit (LEO) satellite mega-constellation networks aim to address the high connectivity demands with a projected 50,000 satellites in less than a decade. To fully utilize such a large-scale dynamic network, an air network composed of stratospheric nodes, specifically high altitude platform station (HAPS), can help significantly with a number of aspects including mobility management. HAPS-LEO network will be subject to time-varying conditions, and in this paper, we introduce an artificial intelligence (AI)-based approach for the unique channel estimation and synchronization problems. First, channel equalization and carrier frequency offset with residual Doppler effects are minimized by using the proposed convolutional neural networks based estimator. Then, the data rate is compounded by increasing spectral efficiency using non-orthogonal multiple access method. We observed that the proposed AI-empowered HAPS-LEO network provides not only a high data throughput per second but also higher service quality thanks to the agile signal reconstruction process.