Abstract
Echo state network (ESN) is a class of neuromorphic computing approach called reservoir computing consisting of a large number of randomly interconnected neurons. Only the reservoir-to-output readout mappings are variable and are modified during the process of training. ESN functions as a densely interconnected recurrent neural network and is suitable for temporal prediction tasks, but with significantly reduced training complexity. In this article, we propose an equalizer based on ESN and evaluate its performance over nonlinear dispersive channels. We also perform experiments for selection various parameters of ESN and study their effect on performance of the equalizer. Based on extensive simulations performed, proposed scheme shows notable decrease in bit-error rate (BER) compared to existing equalizer in literature.
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Majumder, S. (2020). Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_9
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DOI: https://doi.org/10.1007/978-981-15-3357-0_9
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