Full Text:   <2186>

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CLC number: TN828.6

On-line Access: 2017-06-30

Received: 2017-01-09

Revision Accepted: 2017-04-20

Crosschecked: 2017-06-09

Cited: 1

Clicked: 6289

Citations:  Bibtex RefMan EndNote GB/T7714


Ze-song Fei


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.6 P.841-849


Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems

Author(s):  Jing-ming Kuang, Yuan Zhou, Ze-song Fei

Affiliation(s):  School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Corresponding email(s):   jmkuang@bit.edu.cn, zhouyuanbit@163.com, feizesong@bit.edu.cn

Key Words:  Two-dimensional (2D) direction-of-arrival (DOA) estimation, Channel impulse response estimation, Data detection, Uniform rectangular array (URA), Massive multiple-input multiple-output (MIMO)

Jing-ming Kuang, Yuan Zhou, Ze-song Fei. Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(6): 841-849.

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We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.




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