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

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.