基于子空间跟踪的盲自适应多用户检测技术研究

Research of Blind Adaptive Multiuser Detection Based on Subspace Tracking Algorithm

研究了一种基于子空间跟踪的盲自适应多用户检测的算法,提出了一种改进的PASTd算法和卡尔曼滤波算法相结合的盲自适应多用户检测算法。把改进的PASTd算法和卡尔曼滤波器结合,提出了一种新的盲自适应多用户检测器并且能够实现在高信噪比和远近效应情况下得到稳定的跟踪性能。仿真结果表明,该检测器可以迅速地跟踪维数变化了的信号子空间,从而提高检测器的性能,实现稳定的跟踪并使输出具有良好的信干比。提出的盲自适应多用户检测算法虽然在计算量上较LMS算法略有提高,但其收敛性能却优于后者,与RLS算法相比,提出的算法具有更低的计算复杂度和更优的收敛性能。

Research of blind mulitiuser detection based on a improved IPASTd, a blind adaptive multiuser detection algorithm based on a improved PASTd and Kalman filtering is proposed. The new adaptive detector based on a improved PASTd and the Kalman filtering can achieve a high signal to noise ratio and the near--far situation has been stable tracking performance. The simulation results show that the detector can quickly track the changes in the dimensions of the signal sub--space, so as to enhance the performance of the detector to achieve a stable output tracking and has a good signal interference to noise ratio （SINR）. The proposed blind muhiuser detection algorithm has a little higher computational quantity and excellent convergence performance than LMS algorithm, but it offers lower computational complexity and better convergence performance than RLS algorithm.