Tensor-Based Unsourced Random Access for LEO Satellite Internet of Things

With the rapid expansion of Internet of Things (IoT) applications, the demand of wide coverage and massive connectivity is inevitable. In this context, this paper investigates massive unsourced random access (URA) paradigm for low earth orbit (LEO) satellite IoT applications, focusing on device separation and signal detection. By exploiting the structured Grassmannian constellation to generate the codebook, a tensor-based URA transmission scheme is provided, which models the separation and detection problem as a general canonical polyadic (CP) decomposition. Then, to evaluate the access capability of our considered URA scheme, a comprehensive uniqueness analysis considering both sufficient conditions and necessary conditions is presented. Accordingly, an efficient generalized line-search-accelerated alternating least squares (GLSA-ALS) method is proposed to conduct the device separation and signal detection, which can avoid a large number of inverse computations for large-scale matrices. To be specific, with the help of the relaxation factors during the iteration, our proposed method can converge at a fast speed with negligible performance loss, which facilitates a better trade-off between the detection accuracy and computational complexity. Furthermore, depending on the demand of a specific application scenario, the flexible selection of relaxation factors enables the proposed method to be compatible to the classical ALS method, which can enhance the performance at the cost of additional complexity. Finally, relying on the maximum likelihood (ML)-based detection approach, the message list transmitted by active devices from one common codebook can be recovered. Simulation results demonstrate that the proposed GLSA-ALS method outperforms the state-of-the-art methods for practical LEO satellite IoT applications.