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Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments

Authors
Lim, B.Yun, W.J.Kim, J.Ko, Y.
Issue Date
7월-2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Channel estimation; Contamination; Deep learning; Distortion; Hardware; Massive MIMO; Massive MIMO; Uplink; channel estimation; deep residual learning; hardware impairments; pilot contamination; transfer learning
Citation
IEEE Transactions on Vehicular Technology, v.71, no.7, pp.7599 - 7612
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Vehicular Technology
Volume
71
Number
7
Start Page
7599
End Page
7612
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143199
DOI
10.1109/TVT.2022.3170556
ISSN
0018-9545
Abstract
In multi-cell massive MIMO systems, channel esti- mation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach. For the channel estimator, we use a deep residual learning which extracts the features of interference caused by pilot contamination and eliminates them to estimate the channel information. Simulation results demonstrate that the proposed joint pilot design and channel estimator outperforms the conventional approach in multi-cell massive MIMO scenarios. Furthermore, the joint pilot design and channel estimator using transfer learning enhances the estimation performance by reducing the effects of pilot contamination when the prior knowledge of pilot contamination cannot be exploited. IEEE
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공과대학 (전기전자공학부)
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