OFDM-Based Massive Connectivity for LEO Satellite Internet of Things

TL;DR

This paper introduces a novel Bayesian message passing algorithm for joint device activity detection and channel estimation in OFDM-based LEO satellite IoT, addressing Doppler effects and channel sparsity to enhance access capacity and reduce latency.

Contribution

It proposes a new OFDM-symbol repetition technique, a grid-based channel model, and a robust MVSP algorithm with EM-based grid learning for improved satellite IoT connectivity.

Findings

Significantly better activity detection probability.

Higher channel estimation accuracy.

Enhanced access capacity and reduced latency.

Abstract

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bayesian message passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation-maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.