Self-Calibration for Massive MIMO with Channel Reciprocity and Channel Estimation Errors

Mi, D., Zhang, L., Dianati, M., Muhaidat, S., Xiao, P. and Tafazolli, R. (2019) Self-Calibration for Massive MIMO with Channel Reciprocity and Channel Estimation Errors. In: IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, 09-13 Dec 2018, ISBN 9781538647271 (doi:10.1109/GLOCOM.2018.8647759)

Mi, D., Zhang, L., Dianati, M., Muhaidat, S., Xiao, P. and Tafazolli, R. (2019) Self-Calibration for Massive MIMO with Channel Reciprocity and Channel Estimation Errors. In: IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, 09-13 Dec 2018, ISBN 9781538647271 (doi:10.1109/GLOCOM.2018.8647759)

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Abstract

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhang, Dr Lei
Authors: Mi, D., Zhang, L., Dianati, M., Muhaidat, S., Xiao, P., and Tafazolli, R.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2576-6813
ISBN:9781538647271
Published Online:21 February 2019
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