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- W3207678082 abstract "Decision feedback equalizer (DFE) is an instance of equalization technique which consists of a feedback filter for reconstructing inter-symbol interference (ISI) from past decisions and utilizes it for the removal of ISI from the received symbols. DFE can compensate for linear and nonlinear distortions, even in channels with deep spectral null, and can track the characteristics of time-varying channel. To further improve the performance of DFE in nonlinear and time-varying channel, a novel wavelet neural network-based decision feedback equalizer (WNN DFE) is proposed in this chapter. WNNs are especially suited for this application because of their time and frequency localization abilities. However, existing WNN learning techniques fall short of desired performance in case of equalizers. In this chapter, we also propose a WNN DFE training algorithm based on Cuckoo search optimization which avoids local minima unlike existing WNN training algorithms. Computer simulations are performed to evaluate the proposed equalizer architecture over fading, nonlinear, and time-varying channels and compared with existing techniques in the literature. The results show that the proposed scheme provides 12%–17% reduction in mean-square error (MSE) compared to the nearest competitive scheme in the case of benchmark linear channels. For nonlinear channels, the reduction in MSE is in the range of 35%–43% compared to the nearest competitive scheme. Similar improvements are observed in bit error rate (BER) for time-varying channel and for variation in eigenvalue ratio (EVR) of the channel." @default.
- W3207678082 created "2021-10-25" @default.
- W3207678082 creator A5090984270 @default.
- W3207678082 date "2021-10-13" @default.
- W3207678082 modified "2023-09-24" @default.
- W3207678082 title "Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network" @default.
- W3207678082 doi "https://doi.org/10.1201/9781003107026-8" @default.
- W3207678082 hasPublicationYear "2021" @default.
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