Blind Separation of PCMA Signals Based on Iterative Quantum Genetic Optimization

摘要

The single-channel blind source separation (SCBSS) of paired carrier multiple access (PCMA) signal has been a great challenge in satellite communications, due to high complexity of existing separation algorithms and the uncertainty of channel parameters. In this paper, the blind separation was solved as a combinatorial optimization problem, and a novel blind separation algorithm based on the iterative quantum genetic optimization (IQGO) was proposed. By introducing genetic evolution, the search range of separation results was expanded, preventing falling into local extremum. Then the iteration of optimization accelerate the convergence of the optimal separation results. With IQGO, the influence of channel order on computational complexity of blind separation was greatly reduced. Meanwhile, the robustness to estimated error of channel parameters was enhanced by the joint optimization of parameters in IQGO. The simulation results verified that the IQGO-based separation of PCMA signals outperforms existing state-of-the-art separation algorithms, with higher accuracy and good parallel implementation characteristics.