Research On Techniques Of Blind Source Separation For The Paired Carrarie Multiple Access Based Signals

Because of the high spectrum efficiency, strong capacity of resistance to be intercepted, simple system structure, convenient application and so on, the technology of Paired Carrier Multiple Access(PCMA) is widely applied to the modern communication system. With a project for the background and based on the research of massive literature, this thesis analyzes the model for the PCMA based communication signal in single channel, and aims to deal with the PCMA based mixed signal detection and channel parameter estimation algorithms under a non-cooperative communication environment. Numerical simulation results show that the improved algorithm can implement accurate channel-parameter estimation and mixed signal detection. Specifically, its main research work is:(1)Based on the traditional Particle-filter algorithm, the method of particle trajectory updating is analyzed. By dynamically adjusting the particle number, we achieved an improved blind separation algorithm with low complexity. Considering that for the system parameters, such as phase deviation, transmission delay, etc., close to a constant in short time, the proposed algorithm processes the received PCMA signals in frame format and implements blind signal separation in two steps. Firstly, based on the Particle-filter method, recursive sampling and estimation of the system parameters and symbols are implemented in the proposed algorithm. During the process of estimation, some bas particles, whose contribution to the posterior probability density is small, are discarded to simplify the computation. Then after parameter estimation algorithm arriving at convergence, signal separation is implemented based on the maximum likelihood criterion using the system parameters estimated in the first step. Similar to the first step, during the computation of likelihood function, some inferior trajectories are discarded to further reduce computation complexity. MATLAB simulation results show that the improved Particle-filter algorithm for PCMA based signal is high robust and has good separation performance under low SNR.(2)Based on the improved Particle-filter algorithm, Turbo decoding is applied to the blind source separation, and a new blind separation method for PCMA signal is proposed, which further improves performance. Firstly, the thesis introduces the common structures for Turbo encoder and decoder, and verifies its performance. Secondly based on the iterative separation, we joint Turbo decoding to blind source separation. With the interaction of likelihood soft information between blind source separation and decoding, the priori symbols’ information for the blind separation is more and more accurate. MATLAB simulation results show that compared to the blind source separation algorithm without coding information, the proposed algorithm with Turbo iterative decoding can significantly improve the separation performance, where the BER reaches no more than410? under the condition of SNR being 10 dB. This achieves the performance requirements of the practical engineering application.Generally, the proposed algorithm improves the performance of symbol detection and channel parameter estimation of PCMA based mixed signals in this paper. Furthermore, system-level simulation is realized, and shows that the proposed algorithm can achieve the system requirements.