A Novel Greedy Sparse Underdetermined Blind Separation Algorithm for LEO Satellite Communication System
Chengjie Li, Lidong Zhu, Xianfeng Guo, Zhen Zhang, Ying Li Yang · 2022 International Symposium on Networks, Computers and Communications (ISNCC) · 2022
An important characteristic of a low earth orbit (LEO) satellite communication system is a large number of satellites. Therefore, it is common that the number of receiving antennas is less than that of transmitting antennas. In this scenario, to save resources and improve spectral efficiency, an underdetermined blind separation algorithm can be considered. In this paper, under the underdetermined condition, the number of source signals is greater than that of the mathematical constraint equation, and the prior knowledge or characteristics of the source signal are usually used to add constraints. In order to obtain better signal processing performance, and combined with the information transmission characteristics of LEO satellite communication system, a greedy optimization algorithm can be executed. In order to improve the algorithm performance, during algorithm iteration, the sampling points with the highest cost performance are selected and added to the sample set at each time. The simulation results show that the algorithm has good performance.