SCMA Signal Reconstruction Based on Compressed Sensing in RIS-assisted Satellite Internet of Things

‍ ‍Satellite Internet of Things （IoT） is an important part of the future 6G network. The deployment of Reconfigurable Intelligence Surface （RIS） on the ground can further enhance the transmission capability of signals between heaven and earth. However， the access and detection problems of a large number of devices and the high complexity brought by the introduction of RIS pose challenges to the system design and implementation. To address the sparse nature of satellite IoT devices and services， this paper proposed a signal reconstruction algorithm based on compressed sensing， aiming to increase the number of users accessed by the system， while reducing the complexity of user detection and increasing the probability of successful detection. Firstly， the RIS-assisted satellite IoT system architecture was introduced， and the heaven and earth channel model and on-satellite received signal model were constructed. Then， considering the sparsity of satellite IoT ground terminals and the sparsity of services， the signal processing processes of Sparse Code Multiple Access （SCMA） and compressed sensing were combined， and the multi-user detection was transformed into the signal reconstruction in compressed sensing theory by reasonably designing the sparse code words in SCMA. Finally， an Evolved Approximate Message-Passing （EAMP） algorithm was proposed to solve the signal reconstruction problem in compressive sensing. Simulation results show that the RIS-assisted SCMA system can improve the throughput performance of the system compared to non-orthogonal multiple access technology in the power domain， while the EAMP algorithm has a higher probability of correct detection and lower algorithmic complexity than the conventional SIC algorithm.