Reciprocity calibration methods for massive MIMO based on antenna coupling

Vieira, João; Rusek, Fredrik; Tufvesson, Fredrik

doi:10.1109/glocom.2014.7037384

Cited by 61 publications

(39 citation statements)

References 7 publications

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“…Thus, various automatic calibration methods for adaptive BS antennas that are well suited to TDD communication systems have been proposed in previous studies [20,21].We note that some of these methods allow the transmitter and receiver calibration values to be obtained automatically [20][21][22][23][24]. In recent research, the calibration method for massive antennas utilizing the channel reciprocity in a TDD system is proposed [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

et al. 2017

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This paper examines the effectiveness of implicit beamforming (IBF), which enables transmission without channel state information (CSI) feedback in multi-user multiple-input multiple-output (MU-MIMO) systems with a large number of antennas. First, we explain why CSI feedback from terminal stations to the base station produces a very large overhead. A calibration technique is then introduced, which compensates for the difference between the complex amplitudes of the transmitters and receivers to facilitate CSI-feedback-free beamforming; this technique is called IBF. The efficacy of this calibration technique is demonstrated by measuring the amplitude and phase errors obtained using a 16-element array testbed and by performing a channel capacity evaluation. Finally, the throughput under IEEE802.11ac-based massive MIMO transmission, both with and without CSI feedback, is obtained in terms of the medium access control efficiency.

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Section: Introductionmentioning

confidence: 99%

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

et al. 2017

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“…The estimation of channel non-reciprocity in TDD based MIMO systems has also been addressed in various works [3], [4], [6], [10], [11]. These studies can be divided into three main categories: The first two refer to such methods which are performed in BS using a reference antenna and are called "selfcalibration" methods [3], [4], [6], [10].…”

Section: Introductionmentioning

confidence: 99%

“…These studies can be divided into three main categories: The first two refer to such methods which are performed in BS using a reference antenna and are called "selfcalibration" methods [3], [4], [6], [10]. These two categories can be further differentiated based on the availability of additional circuitry.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and estimation of massive MIMO channel non-reciprocity: Sparsity-aided approach

Raeesi

Gokceoglu

Sofotasios

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we study the estimation of channel non-reciprocity in precoded time division duplexing based massive multiple-input multiple-output (MIMO) systems. The considered channel non-reciprocity model covers both the frequencyresponse and the mutual coupling mismatches between the transmitter and the receiver chains of a massive MIMO basestation (BS). Based on the assumed non-reciprocity model, it is shown that the effective downlink channel can be decomposed as a product of the uplink channel and another sparse matrix, referred to as the BS transceiver non-reciprocity matrix. Stemming from such modeling, we then propose an efficient estimator of the BS transceiver non-reciprocity matrix exploiting its sparse nature, combined with an appropriate formulation of the associated overthe-air pilot-signaling. The mean-squared error performance of the overall corresponding estimation method is finally evaluated using extensive computer simulations which indicate that the nonreciprocity characteristics can be estimated efficiently and accurately, thus potentially facilitating large system-level performance gains in multi-user massive MIMO systems.

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“…Not only are theoretical limits of channel state information and pilot contamination [3]- [6], which are linked to array reciprocity calibration [7], [8], subject of ongoing research. Hardware impairments such as I/Q-imbalance, phase noise and power-amplifier non-linearities are considered in literature as well [9].…”

Section: Introductionmentioning

confidence: 99%

A mutual coupling model for massive MIMO applied to the 3GPP 3D channel model

Pratschner

Caban

Schwarz

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Massive Multiple-Input Multiple-Output (MIMO) has become one of the key technologies for future mobile communication systems. Although there is a variety of potential benefits, unresolved implementation issues of massive MIMO are manifold. In this work, we consider the issue of mutual coupling in large antenna arrays. A known matrix model that describes array coupling effects is considered. This coupling model is augmented by a matching network in order to provide a universal coupling model that is applicable to any channel model. Impact of array coupling is then shown by applying the coupling matrix on the 3rd Generation Partnership Project 3D channel model. We show that the matching network leads to decorrelation and significantly reduces capacity losses due to mutual coupling in the context of massive MIMO.

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Reciprocity calibration methods for massive MIMO based on antenna coupling

Cited by 61 publications

References 7 publications

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

Modeling and estimation of massive MIMO channel non-reciprocity: Sparsity-aided approach

A mutual coupling model for massive MIMO applied to the 3GPP 3D channel model

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