Channel Performance Improvement through FF and RBF Neural Network based Equalization
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 8
Abstract
In wireless technology, the communication systems require signal processing techniques to improve the channel performance. The wireless communication is not easily able to avail the error free signal transmission because channel introduces some of the distortions like cochannel interference, adjacent channel interference, Inter symbol interference during signal transmission. So in order to improve the channel performance basically three techniques named diversity, channel coding and equalization are used. In this paper, we are using neural network based equalization technique which is basically used to reduce ISI. The equalization process may be either liner or non –linear. The severely distorting channels limit the use of linear equalizers, so non-linear equalizers are more suitable and efficient instead linear equalizer. Neural network based equalizers are computationally more efficient alternative to currently used (without neural network) nonlinear equalizer e.g. the DFE. In this work, we are giving BER performance comparison of two different neural network based equalizer, first Feed forward neural network (Multi Layer Perceptron) and second RBF based equalizer. Finally in this work it is found that the performance of RBF based equalizer is better as compared to MLP equalizer. Because training process of RBF is faster than that of MLP network which may have more than three layers in its architecture. Second RBF have fast convergence rate as compared to that MLP network.
Authors and Affiliations
Manish Ravindra mahajan, Deepak Pancholi, A. C. Tiwari
Keywords
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