Estimation of transmitter I/Q imbalance using convolutional neural networks

Wong, Lauren J.; Headley, William C.; Michaels, Alan J.

doi:10.1109/ccwc.2018.8301715

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…In [5], the authors have used CNN applied to the time-domain complex baseband error signal to a similar problem of classification of the radio signal modulation. In [6], the authors also applied CNN to the time-domain complex baseband error signal to estimate the transmitter I/Q imbalance, which can also be used to implement SEI.…”

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confidence: 99%

Comparison of techniques for radiometric identification based on deep convolutional neural networks

et al. 2019

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The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF-to-image techniques show that on their datasets the wavelet-based approach outperforms other approaches, also in the presence of white Gaussian noise.

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confidence: 99%

Comparison of techniques for radiometric identification based on deep convolutional neural networks

et al. 2019

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“…Mathematical model of I/Q imbalance has been established and widely used [1]- [14]. In this section, we consider the transmission of baseband signals over a flat-frequency noisy channel and I/Q imbalance in direct conversion receivers.…”

Section: I/q Imbalance System Modelmentioning

confidence: 99%

“…When α and θ have been estimated, original signal can be recovered by R = Γ −1 Y .We use C R , C Ω and C Y represent covariance matrix of R, Ω and Y respectively. With E[Ω k ] = [0, 0] T , C Ω can be given as(14) (see the bottom of this page).…”

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Blind Estimation Algorithms for I/Q Imbalance in Direct Down-Conversion Receivers

Song

Zhang

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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As known, receivers with in-phase and quadraturephase (I/Q) down conversion, especially direct-conversion architectures, always suffer from I/Q imbalance. I/Q imbalance is caused by amplitude and phase mismatch between I/Q paths. The performance degradation resulting from I/Q imbalance can not be mitigated with simply higher signal to noise ratio (SNR). Thus, I/Q imbalance compensation in digital domain is critical. There are two main contributions in this paper. Firstly, we proposed a blind estimation algorithm for I/Q imbalance parameters based on joint first and second order statistics (FSS) which has a lower complexity than conventional Gaussian maximum likelihood estimation (GMLE). This can be used for further precessing such as equalization in presence of receiver IQ imbalance. In addition, we find out the reason of error floor in conventional I/Q imbalance compensation method based on conjugate signal model (CSM). The proposed joint first order statistics and conjugate signal model (FSCSM) compensation algorithm can reach the ideal bit error rate (BER) performance.Index Terms-I/Q imbalance, blind estimation algorithm, first and second order statistics, conjugate signal model

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Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

2019

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Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This work presents an approach for identifying emitters using Convolutional Neural Networks to estimate the IQ imbalance parameters of each emitter, using only raw IQ data as input. Because an emitter's IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phaseshift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Further, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using less data.

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Estimation of transmitter I/Q imbalance using convolutional neural networks

Cited by 11 publications

References 18 publications

Comparison of techniques for radiometric identification based on deep convolutional neural networks

Comparison of techniques for radiometric identification based on deep convolutional neural networks

Blind Estimation Algorithms for I/Q Imbalance in Direct Down-Conversion Receivers

Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

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