Deep Learning Aided Channel Estimation for Massive MIMO with Pilot Contamination

Hiroki Hirose, Tomoaki Ohtsuki, Guan Gui

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) needs accurate estimation of channel state information (CSI) for a user terminal (UT). Due to the time-varying nature of the channel, the length of pilot signals is limited and the number of the 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 pilot contamination from the neighboring cells. With the minimum mean square error (MMSE) channel estimation, the influence of pilot contamination can be reduced by the fully known covariance matrix of channels for all the UTs using the same pilot signal. However, this matrix is unknown to the BS a priori, and has to be estimated. 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. In terms of the speed of training, the former method is better than the latter one. 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 the covariance estimation method via normalized mean square error (NMSE) of the channel.

本文言語English
ホスト出版物のタイトル2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728182988
DOI
出版ステータスPublished - 2020 12月
イベント2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
継続期間: 2020 12月 72020 12月 11

出版物シリーズ

名前2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
2020-January

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
国/地域Taiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

ASJC Scopus subject areas

  • メディア記述
  • モデリングとシミュレーション
  • 器械工学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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