Method for designing complex-valued channel equalizer

A method for designing a complex-valued channel equalizer, comprising:

• constructing a complex-valued channel equalizer by using a complex-valued neural network, wherein the complex-valued channel equalizer is applied to a digital communication system;
• collecting an output signal y(n)=[y(n), y(n−1), . . . , y(n−m+1)]T of a nonlinear channel of the digital communication system as an input of complex-valued neural network and s(n−τ) as a corresponding desired output, and taking the mean squared error as the loss function J(z,z*);
• and optimizing the loss function J(z,z*) to obtain an appropriate complex-valued neural network model, and finally using the complex-valued neural network to implement channel equalization,
• wherein the “optimizing the loss function J(z,z*)” specifically comprises:
• optimizing the loss function J(z,z*) by an adaptive complex-valued L-BFGS algorithm, in the adaptive complex-valued L-BFGS algorithm, calculating and comparing an approximation degree between the inverse of quasi-Newton matrix and the inverse of Hessian matrix along a particular vector, and selecting a memory size corresponding to the highest degree of approximation as the optimal memory size at the current iteration.