Jointly optimized echo state network for short-term channel state information prediction of fading channel
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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
2020-07-27Published in
2020 International Wireless Communications and Mobile Computing, IWCMC 2020Published version
https://doi.org/10.1109/IWCMC48107.2020.9148072Other links
http://hdl.handle.net/2299/23327Metadata
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