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        Jointly optimized echo state network for short-term channel state information prediction of fading channel

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        Final Accepted Version (PDF, 332Kb)
        Author
        Luo, Qiwu
        Zhou, Jian
        Sun, Yichuang
        Simpson, Oluyomi
        Attention
        2299/23327
        Abstract
        Accurately obtaining channel state information (CSI) in wireless systems is significant but challenging. This paper focuses the technique of machine-learning-based channel estimation. In particular, a jointly optimized echo state network (JOESN) is proposed to form a concept of the CSI prediction which is made up of two interacting aspects of output weight regularization and initial parameter optimization. First, in order to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) are learned by a linear-weighted particle swarm optimization (LWPSO) for further improve the prediction accuracy and reliability. The experiments about computational complexity and three evaluating metrics are carried out on two chaotic benchmarks and one real-world dataset. The analyzed results indicate that the JOESN performs promisingly on multivariate chaotic time series prediction.
        Publication date
        2020-07-27
        Published in
        2020 International Wireless Communications and Mobile Computing, IWCMC 2020
        Published version
        https://doi.org/10.1109/IWCMC48107.2020.9148072
        License
        Other
        Other links
        http://hdl.handle.net/2299/23327
        Relations
        School of Physics, Engineering & Computer Science
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