Fractional-order algorithms for tracking Rayleigh fading channels

Shah, Syed Muslim; Samar, Raza; Raja, Muhammad Asif Zahoor

doi:10.1007/s11071-018-4122-4

Cited by 15 publications

(13 citation statements)

References 57 publications

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“…In conclusion, disappointing performance is obtained with low-order AR(p)-KF tuned with the CM criterion for the slow fading scenario, which is the most common one for practical applications, and solutions have to be found. It should be noted that in spite of this important warning highlighted from 2012, many authors still continue to use the KF with an AR (p = 1 or 2) model tuned with the CM criterion for Clarke channel estimation [19][20][21][22][23][24] with low to moderate Doppler frequencies. It appears thus important to continue to communicate on this point, and recommend alternative solutions.…”

Section: Challenge Of Slow Fading Channel Estimation With An Ar(p)-kamentioning

confidence: 99%

“…This criterion is known as the correlation matching (CM) criterion [9,[11][12][13] 1 and the solution is obtained by solving the Yule-Walker equations [14]. Over the past two decades, an extensive literature on Rayleigh Channel estimation was based on an AR(p)-KF tuned using the CM criterion [12,[15][16][17][18]11] and still continues nowadays [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…This solution allows determining the steady-state gains and covariance matrices directly, without iteration. In [37] it is shown that the solution of the filter equations (20) and (23) is obtained by finding the eigenvalues and eigenvectors of the following 4 × 4 matrix:…”

mentioning

confidence: 99%

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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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Section: Challenge Of Slow Fading Channel Estimation With An Ar(p)-kamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Rayleigh fading is a model used to analyze the effect of a medium of propagation on a transmission signal, like the ones that used by devices [20]. In this method, the magnitudes of transmitted signals are varied randomly as per a Rayleigh distribution, where that distribution is the combination of two uncorrelated Gaussian random variables.…”

Section: B Rayleigh Channelmentioning

confidence: 99%

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

Reddy*¹,

Goel²

2019

IJITEE

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With the advent of wireless communication, the problem of data security is of greatest interest. In this paper, spread spectrum techniques have been employed due to its advantages in providing data security. Further, the mechanical integrity of a transformer is investigated using an off-line diagnosis test called as Frequency Response Analysis (FRA). The FRA data of a transformer has been taken as the input signal which is being transmitted from the field where transformer is kept and received at the control room. Furthermore, Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) are employed on FRA data and analyzed the signals by transmitting over Additive white Gaussian noise (AWGN) and Rayleigh faded Channels.

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“…Nonstatic quantum light waves have been a focus of active research in optics and related subjects during several decades [5][6][7][8][9][10][11][12]. In particular, the manipulation and application of such waves are important in nano-optics according to their extensive role in nano science and technology [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Nonstatic Quantum Light Waves: The Principle for Wave Expansion and Collapse

Choi

2021

Photonics

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Nonstatic quantum light waves arise in time-varying media in general. However, from a recent report, it turned out that nonstatic waves can also appear in a static environment where the electromagnetic parameters of the medium do not vary in time. Such waves in Fock states exhibit a belly and a node in turn periodically in the graphic of their evolution. This is due to the wave expansion and collapse in quadrature space, which manifest a unique nonstaticity of the wave. The principle for wave expansion and collapse is elucidated from rigorous analyses for the basic nonstatic waves which are dissipative and amplifying ones. The outcome of wave nonstaticity can be interpreted in terms of the coefficient of the quadratic exponent in the exponential function appearing in the wave eigenfunction; if the imaginary part of the coefficient is positive, the wave expands, whereas the wave collapses when it is negative. Using this principle, we further analyze novel nonstatic properties of light waves which exhibit complicated time behaviors, i.e., for the case that the waves not only undergo the periodical change of nodes and bellies but their envelopes exhibit gradual dissipation/expansion as well.

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Fractional-order algorithms for tracking Rayleigh fading channels

Cited by 15 publications

References 57 publications

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

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

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

Characteristics of Nonstatic Quantum Light Waves: The Principle for Wave Expansion and Collapse

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