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The flourishing low-Earth orbit (LEO) constellation communication networkprovides a promising solution for seamless coverage services toInternet-of-Things (IoT) terminals. However, confronted with massiveconnectivity and rapid variation of terrestrial-satellite link (TSL), thetraditional grant-free random-access schemes always fail to match thisscenario. 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 atwo-stages joint active user detection and channel estimation scheme based onthe training sequences aided OTFS data frame structure. Specifically, in thefirst stage, with the aid of training sequences, we perform active userdetection and coarse channel estimation by recovering the sparse sampledchannel vectors. And then, we develop a parametric approach to facilitate moreaccurate result of channel estimation with the previously recovered sampledchannel vectors according to the inherent characteristics of TSL channel.Simulation results demonstrate the superiority of the proposed method in thiskind of high-mobility scenario in the end.

Joint Active User Detection and Channel Estimation for Grant-Free NOMA-OTFS in LEO Constellation Internet-of-Things