Research On High-efficient Algorithms For Blind Separation Of Communication Signals

With the development of communication technology, modern communication environment has become more and more complex, the received signals are often mixed with interfering signals. As the priori information of the source signals and the mixing system is unknown, the traditional signal processing methods gradually show shortcomings. Blind source separation (BSS) is put forward to solve this problem well, which has brought new ideas and methods to separate communication signals.Based on the introduction of relevant knowledge of BSS and the characteristic of communication signals, this paper emphatically investigated the joint diagonalization method, which was suitable for the sepatation of communication signals. Two adaptive blind source separation algorithms were studied firstly and the problem when aplied to communication signals separation was analyzed. Then, we realized the improved algorithm based on non-orthogonal joint diagonalization of second order statistics, which imporved the performance especially in noisy environment. Simulation results shown effectiveness of the improved algorithm.Due to the excessive iteration in traditional separation algorithms, it can not satisfy the real-time processing of communication signals. The optimum weight matrix was introduced into the joint diagonalization criterion and the Gauss-Newton iteration method was employed to improve the non-orthogonal joint diagonalization algorithm. Simulation results show effectiveness of the improved algorithm and significant improvement for performance with low signal to noise ratios.