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Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

Author(s): Xinqian, Xie; Mugen, Peng; Wenbo, Wang; Poor, H Vincent

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Abstract: To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio head prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed. Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the average effective signal-to-noise ratio (AESNR) in C-RANs.
Publication Date: Aug-2016
Citation: Xie, Xinqian, Mugen Peng, Wenbo Wang, and H. Vincent Poor. "Training design and channel estimation in uplink cloud radio access networks." IEEE Signal Processing Letters 22, no. 8 (2014): 1060-1064. doi:10.1109/LSP.2014.2380776
DOI: 10.1109/LSP.2014.2380776
ISSN: 1070-9908
EISSN: 1558-2361
Pages: 1060 - 1064
Type of Material: Journal Article
Journal/Proceeding Title: IEEE Signal Processing Letters
Version: Author's manuscript

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