Blind channel balancing method based on improved PSO (Particle Swarm Optimization) BP (Back Propagation) neural network

Abstract

The invention designs a blind channel balancing method based on an improved PSO (Particle Swarm Optimization) BP (Back Propagation) neural network. In the process of solving the blind balancing problem on the basis of a BP neural network, determination of an initial weight and a threshold of the BP neural network is lack of the theoretical basis and has the defects of low convergence speed, easiness for falling into a local minimal value and the like so as to cause a poor channel blind balancing effect. In order to overcome the defects of the BP neural network and improving the channel blind balancing effect, the invention discloses a blink balancing method based on the improved PSO-BP neural network. According to the method, firstly, defects of a basic particle swarm algorithm are overcome, parameters of the basic particle swarm are improved, and an inertia weight and a learning factor are adaptively regulated; secondly, the initial weight and the threshold of the neural network are optimized by utilizing the advantage of high global searching capacity of the improved particle swarm, and then more accurate searching is carried out in such local space by utilizing a BP algorithm soas to obtain an optimal connection weight and threshold of the neural network; and finally, blind balancing based on the the improved PSO-BP neural network is implemented.

Description

Technical field: [0001] The invention relates to the field of wireless communication, in particular to a blind channel equalization method based on improved particle swarm optimization BP neural network. Background technique: [0002] Blind equalization technology only uses the statistical characteristics of the received signal itself to equalize the channel to eliminate the intersymbol interference caused by channel distortion and improve the communication quality. It has broad application prospects. With the development of artificial intelligence, it has become an important research field to use nonlinear dynamic system neural network with large-scale parallel processing ability to solve blind equalization. It can achieve the equalization effect that the previous blind equalization algorithm cannot achieve. The blind equalization algorithm using neural network can equalize both minimum phase channels and non-minimum phase channels, including nonlinear channels. However, t...

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Problems solved by technology

[0010] In order to solve the defects that the neural network is sensitive to network parameter initialization settings and easy to fall into local minimum points, the present invention proposes an improved simplified particle swarm optimization algorithm combined with BP algorithm to train the neural network, and determine the connection weight of the neural network through learning and training and threshold

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