  The flourishing low-Earth orbit (LEO) constellation communication network
provides a promising solution for seamless coverage services to
Internet-of-Things (IoT) terminals. However, confronted with massive
connectivity and rapid variation of terrestrial-satellite link (TSL), the
traditional grant-free random-access schemes always fail to match this
scenario. In this paper, a new non-orthogonal multiple-access (NOMA)
transmission protocol that incorporates orthogonal time frequency space (OTFS)
modulation is proposed to solve these problems. Furthermore, we propose a
two-stages joint active user detection and channel estimation scheme based on
the training sequences aided OTFS data frame structure. Specifically, in the
first stage, with the aid of training sequences, we perform active user
detection and coarse channel estimation by recovering the sparse sampled
channel vectors. And then, we develop a parametric approach to facilitate more
accurate result of channel estimation with the previously recovered sampled
channel vectors according to the inherent characteristics of TSL channel.
Simulation results demonstrate the superiority of the proposed method in this
kind of high-mobility scenario in the end.
