Asynchronous Grant-Free Massive Access with Doppler Frequency Offset in NTN

Grant-free-based non-terrestrial networks (GF-NTN) enable wide-area coverage for low-cost and energy-limited devices in Internet-of-Things (IoT). However, the estimation of active devices and channels faces challenges due to the dual impact of propagation delay and Doppler frequency offset (DFO). To address this challenge, we propose an effective detection algorithm aiming to improve the estimation accuracy by exploiting the intrinsic properties in GF-NTN. A structured sparsity model is developed to capture the channel properties in delay-Doppler-time (DDT) domain. Simulation results confirm that the proposed algorithms outperform the state-of-the-art approaches confronted with large latency and severe Doppler effect.