SMO-ISTA-Net: A Synergistic Multi-Stage Optimization Deep-Unfolding JADCE Framework for GFRA in LEO Satellite-Based IoT Systems

This paper investigates the uplink massive grant-free random access in low-earth-orbit (LEO) satellite-based Internet-of-Things systems, where accurate joint activity detection and channel estimation (JADCE) is essential for reliable data recovery. However, the resource constraint and high-dynamic characteristic of LEO scenarios pose significant challenges to traditional JADCE schemes in terms of both estimation accuracy and computational complexity. To overcome these limitations, we propose a novel deep-unfolding JADCE framework based on the synergistic multi-stage optimization iterative shrinkage thresholding algorithm network, referred to as SMO-ISTA-Net, to facilitate efficient massive device access. Specifically, we first develop a synergistic attention module, where an inertia-guided optimization strategy is introduced into the gradient descent process to improve convergence stability and adaptability to fast-varying satellite channels. In order to mitigate the temporal feature inconsistency caused by asynchronous access and multi-path propagation, we further design a lightweight cross-attention mechanism that enables efficient channel feature fusion and facilitates multi-stage information interaction. Moreover, we propose a memory-enhanced proximal-mapping module that incorporates a high-throughput short-term memory mechanism into the unfolded structure, so as to significantly reduce information loss and maximize memory retention of the network. Extensive simulations under diverse LEO scenarios demonstrated that our scheme can achieve superior convergence speed, estimation accuracy, and preamble efficiency, while maintaining low computational complexity and short runtime, compared to the state-of-the-art model-driven JADCE schemes.