Optimized BER for channel equalizer using cuckoo search and neural network

Swati Katwal, Vinay Bhatia


The digital data transfer faces issues regarding Inter-Symbol Interference (ISI); therefore, the error rate becomes dependent upon channel estimation and its equalization. This paper focuses on the development of a method for optimizing the channel data to improve ISI by utilizing a swarm intelligence series algorithm termed as Cuckoo Search (CS). The adjusted data through CS is cross-validated using Artificial Neural Network (ANN). The data acceptance rate is considered with 0-10% marginal error which varies in the given range with different bit streams. The performance evaluation of the proposed algorithm using the Average Bit Error Rate (A-BER) and Logarithmic Bit Error Rate (L-BER) had shown an overall improvement of 30-50% when compared with the Kalman filter based algorithm.


ANN; Channel Estimation; CS; Equalization

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DOI: http://doi.org/10.11591/ijece.v10i3.pp%25p
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