LEO Satellite-Enabled Random Access with Large Differential Delay and Doppler Shift

TL;DR

This paper presents a novel OTFS-based transmission scheme and advanced receiver algorithms for LEO satellite IoT communications, effectively handling large delay and Doppler shifts to improve device identification, channel estimation, and symbol detection.

Contribution

It introduces a multi-module receiver structure with message passing, EM, and neural networks, specifically designed for LEO satellite IoT scenarios with large differential delay and Doppler shift.

Findings

Enhanced system performance with the proposed scheme

Significant improvement over conventional methods

Effective handling of large delay and Doppler shifts

Abstract

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these devices, the large differential delay and Doppler shift are handled at the satellite receiver. We firstly propose a spreading-based multi-frame transmission scheme with orthogonal time-frequency space (OTFS) modulation to mitigate the doubly dispersive effect in time and frequency, and then analyze the input-output relationship of the system. Next, we propose a receiver structure based on three modules: a linear module for identifying active devices that leverages the generalized approximate message passing algorithm to eliminate inter-user and inter-carrier interference; a non-linear module that employs the message passing algorithm to jointly estimate the channel and detect the transmitted symbols; and a third module that aims to exploit the three dimensional block channel sparsity in the delay-Doppler-angle domain. Soft information is exchanged among the three modules by careful message scheduling. Furthermore, the expectation-maximization algorithm is integrated to adjust phase rotation caused by the fractional Doppler and to learn the hyperparameters in the priors. Finally, the convolutional neural network is incorporated to enhance the symbol detection. Simulation results demonstrate that the proposed transmission scheme boosts the system performance, and the designed algorithms outperform the conventional methods significantly in terms of the device identification, channel estimation, and symbol detection.