Abstract
A method based on a sparse auto-encoder (SAE) network for the estimation of spreading sequences in long-code direct-sequence code-division multiple access (LC-DS-CDMA) signals is proposed. First, a network classification model based on SAE and softmax classifier is established. Next, the effectiveness of the proposed method is verified by estimating Walsh sequences and m sequences. To estimate the spreading sequences, the LC-DS-CDMA signal is divided into fragments. Then, each user’s spreading sequence is separated by the fast independent component analysis (Fast-ICA) algorithm, and the amplitude fuzziness is eliminated by the delay-and-multiply method. Finally, the spreading sequences are estimated by the SAE model. Experimental results showed that the proposed algorithm could effectively estimate the spreading sequences of LC-DS-CDMA signals. Compared to the existing matching algorithm and Fast-ICA algorithm, the estimation time required by the proposed algorithm was shorter, and its estimation performance at low signal-to-noise ratios was superior.
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Acknowledgements
We acknowledged our sincere thanks to all anonymous reviewers for their insightful comments and constructive suggestions to polish this paper in high quality. This research was supported by the National Natural Science Foundations of China (No. 61571172), Zhejiang Provincial Key Lab of Data Storage and Transmission Technology and the open project of Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking. We acknowledged our sincere thanks to them for their encouragement and valuable support.
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Qiang, F., Zhao, Z., Shang, J. et al. Estimation of spreading sequences in LC-DS-CDMA signals based on sparse auto-encoder. Evol. Intel. 13, 235–246 (2020). https://doi.org/10.1007/s12065-019-00298-3
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DOI: https://doi.org/10.1007/s12065-019-00298-3