A novel equalization scheme for a wireless ATM communication channel using a recurrent neural network is proposed in this paper. The recurrent neural network used in this scheme is the Complex Bilinear Recurrent Neural Network (CBLRNN). A reduced version of the CBLRNN for faster and stable convergence is first proposed in this paper. The R-CBLRNN is then applied to equalization of a wireless ATM channel for 8PSK, which has severe nonlinearity and intersymbol interference due to multiple propagation paths. The experiments and results show that the proposed R-CBLRNN gives very favorable results in the Mean Square Error (MSE) criterion over conventional equalizers. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Park, D. C., Song, Y. S., & Woo, D. M. (2007). Equalization of 8PSK signals with a recurrent neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 105–110). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_14
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