门控递归单元神经网络坐标变换盲均衡算法

Coordinate transformation blind equalization algorithm based on gated recurrent unit neural network

针对数字信号传输过程中的码间干扰问题,提出了门控递归单元神经网络坐标变换盲均衡算法(GRUNN-CT-CMA).首先,在递归神经网络基础上加入门控结构,使门控递归单元神经网络(GRUNN)对长时间跨度信息的感知能力更强、记忆力更持久.其次,在GRUNN中引入坐标变换得到的盲均衡算法,进一步降低了稳态误差、加快了代价函数收敛速度.结果表明,与常模盲均衡算法(CMA)和延迟单元递归神经网络盲均衡算法(BRNN-CMA)相比,GRUNN-CT-CMA在均衡高阶多模信号时,稳态误差最小、收敛速度最快、输出信号星座图最清晰.

In order to solve the problem of inter symbol interference in the process of digital signal transmission, a coordinate transformation constant modulus blind equalization algorithm based on gated recurrent unit neural network(GRUNN-CT-CMA) is proposed. Firstly, based on the recurrent neural network, the gated recurrent unit neural network(GRUNN) with a gate structure was added, which has stronger perception and longer-lasting memory of long-span information. Secondly, the coordinate transformation blind equalization algorithm was introduced in GRUNN, which further reduced the residual error and corrected the phase offset. The simulation results show that comparing with the constant modulus blind equalization algorithm(CMA) and the bias-unit recurrent neural networkconstant modulus blind equalization algorithm(BRNN-CMA), when GRUNN-CT-CMA equalizing high order multi-mode signals, the steady-state error is minimal, the speed of convergence is the fastest and the constellation of the output signal is the clearest.