Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Nie, Rongjiang; Chen, Li; Zhao, Nan; Chen, Yunfei; Wang, Weidong; Xian-bin, Wang

doi:10.1109/twc.2021.3073949

Cited by 10 publications

(4 citation statements)

References 36 publications

(67 reference statements)

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“…where P J denotes the power value of w J 2 2 , which can be obtained by using the signal strength and channel gains [32], [48]. However, owing to the CSI uncertainties of jammer's channels caused by the uplink-downlink channel reciprocity mismatch [55], [56], P J can not be accurately acquired. Following [57], we adopt P J − P J P J ≤ ε P J to denote the estimation error of w J 2 2 , where P J is the estimation value.…”

Section: Phase Shift Optimization With Probabilistic Constraintsmentioning

confidence: 99%

“…8 Since the impact of power estimation error on the system performance has been widely investigated in [13] and [58], this paper fixes ε P J = 0.1 for simplicity. As for the practical issuses that the uplink-downlink channel reciprocity mismatch, the impact analysis and calibration have been investigated in [55] and [56], and the full characterization of this case in our model remains unknown and requires a deep investigation to be conceived in our future research.…”

Section: Phase Shift Optimization With Probabilistic Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Sun

Luo

et al. 2022

IEEE Internet Things J.

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Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the quality of service (QoS) of users. This paper utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamicly adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer's channel state information (CSI). In addition, we further consider the fact that the jammer's transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer's statistical CSI, and signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users without the jammer's CSI, respectively. By applying the Decomposition-based large deviation inequality (DBLDI), Bernstein-type inequality (BTI), Cauchy-Schwarz inequality, and penalty non-smooth optimization method, we efficiently solve the initial intractable and non-convex problems. Numerical simulations demonstrate that the proposed antijamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.

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Section: Phase Shift Optimization With Probabilistic Constraintsmentioning

confidence: 99%

Section: Phase Shift Optimization With Probabilistic Constraintsmentioning

confidence: 99%

Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Sun

Luo

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…The transmit chain is composed of a digital-to-analog converter, a power amplifier, etc., while the receive RF chain consists of an analog-to-digital converter, a low noise amplifier, etc. Due to the different compositions, the RF responses of transmit and receive chains are generally asymmetric, which results in the reciprocity mismatch of the uplink and downlink channels [4].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical-Absolute Reciprocity Calibration for Millimeter-Wave Hybrid Beamforming Systems

Chen

Nie

Chen

et al. 2023

IEEE Trans. Wireless Commun.

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In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multipleoutput (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the overhead and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cramér-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.

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“…Successfully deploying novel advanced antenna systems (AAS) requires the correction of radio frequency (RF) hardware impairments originating from various sources. Among those are phase noise, active and passive intermodulation distortions, in-phase/quadrature imbalances and manufacturing imperfections of antenna arrays [1]- [3]. In the latter case, RF connections between analog-to-digital converter (ADC)/digitalto-analog converter (DAC) units and radiating elements can have unmatched lengths and other defects, resulting in different multiplicative complex gain and phase distortions across the signal bandwidth (e.g.…”

Section: Introductionmentioning

confidence: 99%

Antenna Array Calibration Via Gaussian Process Models

Tambovskiy¹,

Fodor²,

Tullberg³

2023

Preprint

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Antenna array calibration is necessary to maintain the high fidelity of beam patterns across a wide range of advanced antenna systems and to ensure channel reciprocity in time division duplexing schemes. Despite the continuous development in this area, most existing solutions are optimised for specific radio architectures, require standardised over-the-air data transmission, or serve as extensions of conventional methods. The diversity of communication protocols and hardware creates a problematic case, since this diversity requires to design or update the calibration procedures for each new advanced antenna system. In this study, we formulate antenna calibration in an alternative way, namely as a task of functional approximation, and address it via Bayesian machine learning. Our contributions are three-fold. Firstly, we define a parameter space, based on near-field measurements, that captures the underlying hardware impairments corresponding to each radiating element, their positional offsets, as well as the mutual coupling effects between antenna elements. Secondly, Gaussian process regression is used to form models from a sparse set of the aforementioned near-field data. Once deployed, the learned non-parametric models effectively serve to continuously transform the beamforming weights of the system, resulting in corrected beam patterns. Lastly, we demonstrate the viability of the described methodology for both digital and analog beamforming antenna arrays of different scales and discuss its further extension to support real-time operation with dynamic hardware impairments.

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Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Cited by 10 publications

References 36 publications

Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Hierarchical-Absolute Reciprocity Calibration for Millimeter-Wave Hybrid Beamforming Systems

Antenna Array Calibration Via Gaussian Process Models

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