Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Constellation Internet-of-Things

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite constellations
based Internet-of-Things (IoT) for seamless global coverage. Specifically, we propose to integrate the
grant-free non-orthogonal multiple access (NOMA) paradigm for random access with the emerging
orthogonal time frequency space (OTFS) modulation for accommodating the massive IoT access. So
that the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs) can be
mitigated. Furthermore, we put forward a two-stage joint active terminal identification (ATI) and channel
estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. At the
first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the
ATI and CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse
response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property
for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to
further refine the CE performance. Finally, a least squares based parallel time domain SD is developed
for demodulating the OTFS signals with relatively low complexity. Simulation results demonstrate the
superiority of the proposed methods over the state-of-the-art solutions in the case of the long round-trip
latency and severe Doppler effect.