  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.
