Multi-User Massive MIMO Performance with Calibration Errors

Luo, Xiliang

doi:10.1109/twc.2016.2542135

Cited by 29 publications

(27 citation statements)

References 24 publications

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“…Note that the auxiliary channel H i→j inherits the reciprocity from the OTA channel C i→j : H i→j = H T j→i . Upon applying the vectorization operator for each bidirectional transmission between groups i and j, we have, similarly to (6) vec…”

Section: Optimal Estimation and Performance Limitsmentioning

confidence: 99%

“…Eliminating the common auxiliary channel H i→j , we get the elementary equation (4) for the LS method (15) or (16). Equivalently to (6), one obtains 6 The method used in [24] to derive the ML estimator, although called "Expectation Maximization" in the original paper, actually corresponds to the AML scheme, but using quadratic regularization terms for both f and h which can be interpreted as Gaussian priors and which may improve estimation in ill-conditioned cases.…”

Section: Maximum Likelihood Vs Least Squaresmentioning

confidence: 99%

“…Stacking these equations similarly to (6), but with respect to the i, j ∈ G(t), gives Y t (P t )f = n t for each time slot t.…”

Section: A Overviewmentioning

confidence: 99%

See 2 more Smart Citations

A Framework for Over-the-Air Reciprocity Calibration for TDD Massive MIMO Systems

Jiang

Decurninge

Gopala

et al. 2018

IEEE Trans. Wireless Commun.

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One of the biggest challenges in operating massive multiple-input multiple-output systems is the acquisition of accurate channel state information at the transmitter. To take up this challenge, time division duplex is more favorable thanks to its channel reciprocity between downlink and uplink. However, while the propagation channel over the air is reciprocal, the radio-frequency front-ends in the transceivers are not. Therefore, calibration is required to compensate the RF hardware asymmetry.Although various over-the-air calibration methods exist to address the above problem, this paper offers a unified representation of these algorithms, providing a higher level view on the calibration problem, and introduces innovations on calibration methods. We present a novel family of calibration methods, based on antenna grouping, which improve accuracy and speed up the calibration process compared to existing methods. We then provide the Cramér-Rao bound as the performance evaluation benchmark and compare maximum likelihood and least squares estimators. We also differentiate between coherent and noncoherent accumulation of calibration measurements, and point out that enabling non-coherent accumulation allows the training to be spread in time, minimizing impact to the data service. Overall, these results have special value in allowing to design reciprocity calibration techniques that are both accurate and resource-effective.

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Section: Optimal Estimation and Performance Limitsmentioning

confidence: 99%

Section: Maximum Likelihood Vs Least Squaresmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for Over-the-Air Reciprocity Calibration for TDD Massive MIMO Systems

Jiang

Decurninge

Gopala

et al. 2018

IEEE Trans. Wireless Commun.

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“…However, in practice, the transmit and receive branches are composed of totally different analog circuits. Accordingly, the radio-frequency (RF) gain of the transmit chain is different from that of the receive chain at the baseband [3]. These RF gain mismatches destroy the end-to-end TDD channel reciprocity and lead to severe performance degradation in massive MIMO systems [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the radio-frequency (RF) gain of the transmit chain is different from that of the receive chain at the baseband [3]. These RF gain mismatches destroy the end-to-end TDD channel reciprocity and lead to severe performance degradation in massive MIMO systems [3][4][5]. Careful calibration is thus necessitated to compensate those RF gain mismatches at the RF front ends (FEs) to restore the end-to-end UL/DL channel reciprocity.…”

Section: Introductionmentioning

confidence: 99%

How to Interconnect for Massive Mimo Self-Calibration?

Yang

Zhu

Shen

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits at different antennas at the BS break the end-to-end UL and DL channel reciprocity. To restore the channel reciprocity, it is necessary to calibrate all the antennas at the BS. This paper addresses the interconnection strategy for the internal self-calibration at the BS where different antennas are interconnected via hardware transmission lines. Specifically, the paper reveals the optimality of the star interconnection and the daisy chain interconnection respectively. From the results, we see the star interconnection is the optimal interconnection strategy when the BS are given the same number of measurements. On the other hand, the daisy chain interconnection outperforms the star interconnection when the same amount of time resources are consumed. Numerical results corroborate our theoretical analyses.

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Energy- and spectral-efficiency of zero-forcing beamforming in massive MIMO systems with imperfect reciprocity calibration: bound and optimization

Liu

Quan

et al. 2018

Sci. China Inf. Sci.

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In a time-division duplex (TDD) system with massive multiple input multiple output (MIMO), channel reciprocity calibration (RC) is generally required in order to cope with the reciprocity mismatch between the uplink and downlink channel state information. Currently, evaluating the achievable spectral efficiency (SE) and energy efficiency (EE) of TDD massive MIMO systems with imperfect RC (IRC) mainly relies on exhausting Monte Carlo simulations and it is infeasible to precisely and concisely quantify the achievable SE and EE with IRC. In this study, a novel method is presented for tightly bounding the achievable SE of massive MIMO systems with zero-forcing beamforming under IRC. On the basis of the analytical results, we demonstrate key insights for practical system design with IRC in three aspects: the scaling rule for interference power, saturation region of the SE, and the bound on the SE loss. Finally, the trade-off between spectral and energy efficiencies in the presence of IRC is determined with algorithms developed to optimize SE (EE) under a constrained EE (SE) value. The loss of optimal total SE and EE due to IRC is also quantified, which shows that the loss of optimal EE is more sensitive to IRC in a typical range of transmit power values.

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Multi-User Massive MIMO Performance with Calibration Errors

Cited by 29 publications

References 24 publications

A Framework for Over-the-Air Reciprocity Calibration for TDD Massive MIMO Systems

A Framework for Over-the-Air Reciprocity Calibration for TDD Massive MIMO Systems

How to Interconnect for Massive Mimo Self-Calibration?

Energy- and spectral-efficiency of zero-forcing beamforming in massive MIMO systems with imperfect reciprocity calibration: bound and optimization

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