Deep Learning-based Channel Estimation for Massive MIMO Systems with Pilot Contamination

Hiroki Hirose, Tomoaki Ohtsuki, Guan Gui
2020 IEEE Open Journal of Vehicular Technology  
In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) is required to obtain accurate estimation of channel state information (CSI) for a user terminal (UT). Because of the time-varying nature of the channel, the length of pilot signals is limited and the number of orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by
more » ... is deteriorated by pilot contamination from the neighboring cells. The minimum mean square error (MMSE) channel estimation can be used to reduce the influence of pilot contamination. However, it needs to know the covariance matrix of channels for all the UTs, which is unknown to the BS in practice. In this paper, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. The former method is better in terms of the training speed, however, the latter one can estimate the channel more accurately. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and covariance estimation methods via normalized mean square error (NMSE). In addition, we also investigate the impact of channel aging, and show that including some expected data into training datasets can avoid the great degradation of estimation quality. INDEX TERMS Channel estimation, deep learning, massive MIMO, pilot contamination. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 2, 2021 67 HIROKI HIROSE (Student Member) was born in Gifu, Japan, in 1995. He received the B.E. degree with Faculty of Science and Technology from Keio University in 2019. He is a master's student with Graduate School, Keio University. His research interest focuses on deep learning based physicallayer wireless communications. He is a a member of IEICE. TOMOAKI OHTSUKI (Senior Member, IEEE) received the B.E., M.E., and Ph. D. degrees in electrical engineering from Keio University,
doi:10.1109/ojvt.2020.3045470 fatcat:de7f3uqizvfxvozs6p5ixe3fya