基于前馈神经网络的非合作PCMA信号盲分离算法

针对非合作接收PCMA混合信号盲分离中高复杂度束缚，提出一种基于前馈神经网络的分离算法，通过搭建神经网络分离平台，规避传统的发送符号遍历思想，实现PCMA混合信号低复杂度高性能盲分离.仿真实验表明，神经网络能够极大挖掘信号内在信息，针对QPSK调制PCMA混合信号，在信噪比7dB时误比特率达到10^-3数量级，并伴随着较PSP分离算法算术平方根级别的复杂度降低.

Blind Separation Algorithm for Non-cooperative PCMA Signal Based on Feedforward Neural Network

Aiming at the high complexity in blind separation of PCMA mixed signals with non-cooperative reception,the separation algorithm based on feedforward neural network is proposed.By setting up a neural network separation platform and avoiding the traditional idea of maximum a posteriori probability,the blind separation algorithm with low complexity and high performance can be realized.Simulation results show that the neural network can greatly exploit the intrinsic information of the signal,and 10^-3 orders of bit error rate performance is achieved with 7 dB of signal-to-noise ratio to QPSK modulated PCMA signals,accompanied by the declining complexity of the arithmetic square root level compared with the PSP algorithm.