Study On Blind Equalization Alogrithm Based On Neural Network Theory

In modern communication system, the inter-symbol interference (ISI) caused by non-ideal character of channel is the main factor which affect communication quality. The conventional equalizer resort to a training sequence in order to overcome inter-symbol interference. But this method would influence communication efficiency. Blind equalization techniques rely on solely the received channel output signal to adjust the equalizer weights without a known training sequence available.This paper formulize the foundational principle and development of blind equalization technique based on neural network. The traditional neural network blind equalization algorithms have many advantages and disadvantages. Through analyse these disadvantage, two kinds of new blind equalization algorithm based on neural network is proposed. The first algorithm based on feed-forward neural network, this algorithm is developed by the combination of conventional CMA and feed-forward neural network. According to the CMA blind equalization thought-way, a new cost function is proposed so that apply the feed-forward neural network to blind equalization. Because the difference of real-valued and complex-valued system, the different neuromime is adopt in different system. In addition, this paper design two kinds of transmission function to different input signal. The new algorithm make up the flaw such as small application domain and difficult chose of parameter. The second is blind equalization algorithm based on bilinear recurrent neural network. This algorithm adopt the bilinear recurrent neural network and design a new transmission function and cost function. Through computer simulation, all proposed algorithm have better convergence performance.