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Wireless Communications and Mobile Computing
Volume 2018 (2018), Article ID 5048419, 7 pages
https://doi.org/10.1155/2018/5048419
Research Article

Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

1Beijing University of Chemical Technology, Beijing 100029, China
2School of Management, Hefei University of Technology, Hefei 230009, China

Correspondence should be addressed to Jianshan Sun; nc.ude.tufh@3149sjnus

Received 23 November 2017; Accepted 26 December 2017; Published 18 March 2018

Academic Editor: Yin Zhang

Copyright © 2018 Jingwen Huang and Jianshan Sun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Learning rate plays an important role in separating a set of mixed signals through the training of an unmixing matrix, to recover an approximation of the source signals in blind source separation (BSS). To improve the algorithm in speed and exactness, a sampling adaptive learning algorithm is proposed to calculate the adaptive learning rate in a sampling way. The connection for the sampled optimal points is described through a smoothing equation. The simulation result shows that the performance of the proposed algorithm has similar Mean Square Error (MSE) to that of adaptive learning algorithm but is less time consuming.