On the performance of digital adaptive spur cancellation for multi-standard radio frequency transceivers

Gerzaguet, Robin; Ros, Laurent; Belvèze, Fabrice; Brossier, Jean-Marc

doi:10.1016/j.dsp.2014.07.006

Cited by 7 publications

(12 citation statements)

References 34 publications

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“…Furthermore, under the flat TxL channel assumption, the existence of a fractional delay in the reception stage due to digital blocks, analog blocks and propagation into the receiver, has not been considered. Also, a steady-state analysis of a LMSbased algorithm was described by [19] to reduce the impact of the spurs (which are narrowband pollutions due to leakage of clock harmonics), and a similar approach can be investigated for a wideband polluting signal. In this paper, we propose a digital compensation scheme for the TxL problem.…”

Section: Introductionmentioning

confidence: 99%

Performance of a digital transmitter leakage LMS-based cancellation algorithm for multi-standard radio-frequency transceivers

Gerzaguet

Ros

Belvèze

et al. 2016

Digital Signal Processing

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International audienceThis paper deals with a joint estimation algorithm that is dedicated to digital compensation of transmitter leakage pollution in frequency division duplexing transceivers. These transceivers are affected by transmitter to receiver signal leakage. Combined with the nonlinearity of amplifying components in the receiver path, the baseband signal received can be severely polluted by a baseband polluting term. This term is based on the square modulus of the transmitted signal, and it depends on the equivalent transmitter leakage channel which models leakages and the receiver path. Here, we consider a nonconvolutive, time-varying channel that is modeled by a time-varying complex gain and the presence of a fractional delay, modeling the propagation effects into the receiver. The complex gain is a sum of two components, a constant term that models static effects, and a first-order autoregressive model that approximates the time variation of the transmitter leakage channel. We focus here on a fully digital approach, using digital signal processing techniques and knowledge of the transmitted samples to mitigate the pollution. We first express the asymptotic performance of a transmitter leakage gain estimator piloted by a reference-based least-mean-square (LMS) approach in the synchronized case, and then we derive the influence of the fractional delay. We show that, in practice, the fractional delay cannot be neglected, and we propose a joint estimation of the fractional delay and the transmitter leakage gain to perform digital compensation. The proposed method is adaptive, recursive and online, and it has low complexity. This algorithm, that is developed for a flat transmitter leakage channel case, is seen to be robust in a typical selective channel simulation case, and more suitable than a classic multi-tap LMS scheme proposed in the literature

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Section: Introductionmentioning

confidence: 99%

Performance of a digital transmitter leakage LMS-based cancellation algorithm for multi-standard radio-frequency transceivers

Gerzaguet

Ros

Belvèze

et al. 2016

Digital Signal Processing

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“…• An extensive and fair comparison with the algorithms commonly used in the literature for tracking time-varying flat fading channels is provided, from theoretical formulas or numerical simulations. The considered algorithms are Kalman filters based on an AR(p) model with CM [11][12][13] and improved criterion [4,31,28], or based on an RW(p) model [33], but also least mean square (LMS) algorithms [35,38,39] or rather their integrated versions [40,41] to have the same model order p = 2 as the proposed algorithm.…”

Section: Contributions Of This Papermentioning

confidence: 99%

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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Second-order autoregressive modelbased Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation. Digital Signal Processing, Elsevier, 2019, 90, pp. AbstractThis paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer-Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p = 2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their ap-$ plicability. A general solution for any scenario is simply missing for p = 2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behaviour of the KF with respect to the channel state (Doppler frequency and signal to noise ratio).Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state.Analytic and numerical comparisons with first-and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p ≥ 15.

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“…If the optimal weight vector is WSS time varying, the asymptotic performance is composed of a static term (relative to the variance of x(n)) and a dynamic term (relative to the time variations of the optimal weight). The trade-off between these terms is characterized by an optimal step size of µ opt that provides minimal asymptotic error variance [5], [11]. In practice, the minimal step size µ min must be set to have µ min ≈ µ opt .…”

Section: And [•]mentioning

confidence: 99%

“…Thus, in various fields, noise-reduction algorithms that exploit knowledge of the polluters with a natural or synthesized reference [3], [4], [5] and equalization algorithms [6], [7], [8] are often based on LMS. Recently, LMS algorithms have been extensively used for cancellation of self-interference in radio frequency transceivers, where low complexity and exploitation of polluter knowledge are mandatory [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…In such a case, the performance can be expressed through the sum of a static term, relative to the influence of the additive noise, and a dynamic term, linked to the time variation of the channel. This leads to an optimal step size, which is related to the minimal mean squared error (MSE) that offers the best asymptotic performance [14], [15], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On multiplicative update with forgetting factor adaptive step size for least mean-square algorithms

Gerzaguet¹,

Ros²,

Belvèze³

et al. 2018

2018 25th International Conference on Telecommunications (ICT)

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Abstract-This paper deals with a new adaptive step-size overlay dedicated to Least Mean-Square (LMS) algorithms for noise cancellation with reference. LMS algorithms are often used in real-time estimations due to their robustness and simplicity, and are guided by their constant step size. Their transient and asymptotic performances are both linked to the step size, which leads to the well-known trade-off between speed and accuracy. In this paper, we propose a LMS algorithm with a new self-adaptive overlay based on a Multiplicative Update with Forgetting Factor (LMS-MUFF). The adaptive overlay improves the speed and the reactivity of the algorithm. In order to tune the algorithm parameters, we express the scalar case semi-analytical relationship between the LMS-MUFF parameters and a falsealarm probability, as defined from unwanted variations in the step-size during the asymptotic mode. We compare our method to reference methods in the literature and show that it offers better speed and reactivity with the same asymptotic performance.Index Terms-least mean-square algorithms; adaptive noise cancellation algorithms; self-adaptive step size; steepest descent algorithms.

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On the performance of digital adaptive spur cancellation for multi-standard radio frequency transceivers

Cited by 7 publications

References 34 publications

Performance of a digital transmitter leakage LMS-based cancellation algorithm for multi-standard radio-frequency transceivers

Performance of a digital transmitter leakage LMS-based cancellation algorithm for multi-standard radio-frequency transceivers

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

On multiplicative update with forgetting factor adaptive step size for least mean-square algorithms

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