Massive MIMO-OTFS-Based Random Access for Cooperative LEO Satellite Constellations

TL;DR

This work tackles grant-free random access for IoT in cooperative LEO satellite constellations by modeling time-varying terrestrial-satellite links with a generalized complex exponential basis expansion model and leveraging OTFS to mitigate Doppler effects. It introduces a two-phase Bayesian framework (MRF-BGM-AMP with GAMP initialization, followed by EP-based refinement) for single-satellite channel estimation, and extends to centralized and distributed cooperative modes that fuse device identification and joint channel-symbol refinement using interference cancellation and structured probabilistic inference. Simulation results show substantial gains in activity error rate, NMSE, and symbol error rate over baselines, with the distributed mode achieving near-centralized performance after only two exchanges of soft symbol information. The proposed framework yields scalable, spectrally efficient random access for non-terrestrial networks by exploiting angular sparsity and cross-satellite cooperation, while FFT-based implementations keep complexity manageable.

Abstract

This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation.

Figures (8)

• Figure 1: Diagram for cooperative multi-satellite-enhanced random access.
• Figure 2: OTFS frame structure.
• Figure 3: Factor graph representation for refinement scheme.
• Figure 4: Performance comparison among different schemes given different SNR values, where $S_a=2$, $p_{\lambda}=0.1$, and $\rho=0.4$.
• Figure 5: The impact of BEM modeling accuracy, where SNR = 5 dB, $S_a=2$, $p_{\lambda}=0.1$, and $\rho=0.4$.