Abstract
Traditional semi-blind channel estimator is based on eigen value decomposition (EVD) or singular value decomposition (SVD), which effectively reduces the interference through dividing the observed signal into signal subspace and noise subspace. Due to the large computation, Massive MIMO systems could not afford the cost of traditional algorithms in spite of the high performance. In this paper, we propose a channel estimation algorithm based on subspace tracking, in which the signal subspace is obtained by approximating power iteration algorithm. Without sacrificing the estimation performance, the complexity is greatly reduced compared with the traditional semi-blind channel estimation algorithm, which improves the applicability of the estimator.
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Acknowledgment
This work is supported in part by National Natural Science Foundation of China (No. 61671184, No. 61401120, No. 61371100), National Science and Technology Major Project (No. 2015ZX03001041).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zheng, L., Zhao, D., Wang, G., Xu, Y., Wu, Y. (2018). Channel Estimation Based on Approximated Power Iteration Subspace Tracking for Massive MIMO Systems. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_8
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DOI: https://doi.org/10.1007/978-3-319-73564-1_8
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