Nakagami-
            <i>m</i>
            phase-envelope joint distribution

Yacoub, M.D.; Fraidenraich, Gustavo; Filho, José Cândido Silveira Santos

doi:10.1049/el:20057014

Cited by 120 publications

(119 citation statements)

References 6 publications

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“…Based on the density functions in (9) and (10), the PDF of the in-phase and quadrature components of the signal Z are the same and given by [9]:…”

Section: Mimo System Modelmentioning

confidence: 99%

“…It is concluded that the fading effect turns the signal in (13) to the following signal [15]: Since the Nakagami fading model is supposed for our MIMO environment, the random processes R (t) and (t) in (14) have the obtained PDFs in (9) and (10), respectively. Thus, the received signals in the two receivers are given by:…”

Section: Channel Parameter Estimationmentioning

confidence: 99%

“…The envelope R k1 (t) and phase k1 (t) are independent processes [9]; therefore, Q 1 (t) and Q 2 (t) are also independent. Since the signal q k1 (t) is the product of two independent signals Q 1 (t) and Q 2 (t), we have:…”

Section: Channel Parameter Estimationmentioning

confidence: 99%

See 2 more Smart Citations

On the capacity of MIMO correlated Nakagami- m fading channels using copula

Gholizadeh

Amindavar

Ritcey

2015

J Wireless Com Network

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In this paper, a novel approach is proposed based on the probability density function (PDF) concept to achieve the capacity of a correlated ergodic multi-input multi-output (MIMO) channel with Nakagami-m fading. In our proposed method, channel parameters are unknown, and they are initially estimated by using the PDF of the received samples in the receiving antennas. The copula theory is employed to estimate the parameters of the channel in the proposed PDF-based approach. By appealing to copula, the notion of PDF estimation is simplified in the computation technique when we are faced with correlated signals. Since we are working on a correlated channel, the copula concept results in a powerful estimation approach for the PDF of the signals in the receivers. Accurate PDF estimation leads to having a precise calculation for channel parameters. Hence, the new approach guarantees that the capacity of a correlated ergodic channel is predicted reliably. In the previous works, either the capacity of simple uncorrelated Nakagami-m channels is presented or an asymptotic formulation is suggested for a correlated Nakagami-m channel. However, our proposed method introduces an analytic expression for the capacity of the MIMO correlated Nakagami-m fading channel relying on copula. All the results in both channel parameter estimation and channel capacity prediction are validated with some simulations.

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“…Based on the density functions in (9) and (10), the PDF of the in-phase and quadrature components of the signal Z are the same and given by [9]:…”

Section: Mimo System Modelmentioning

confidence: 99%

Section: Channel Parameter Estimationmentioning

confidence: 99%

See 1 more Smart Citation

On the capacity of MIMO correlated Nakagami- m fading channels using copula

Gholizadeh

Amindavar

Ritcey

2015

J Wireless Com Network

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“…However, this is a rather simplistic assumption, since it is hard to imagine a fading signal with uniform distribution whose envelope approximately ranges from Hoyt to Rice, Hoyt and Rice themselves having nonuniform phases. In an attempt to fill this gap, in [4], a complex fading model leading to Nakagami-envelope and nonuniform phase distribution was proposed. Such a model was then improved in [5] to account for power, or, equivalently, clustering, imbalance between in-phase and quadrature components.…”

Section: Introductionmentioning

confidence: 99%

The Complexα-μFading Channel with OFDM Application

Freitas¹,

Bomfin²,

Souza³

et al. 2017

International Journal of Antennas and Propagation

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The aims of this paper are threefold: (i) to present a model for the complex -fading channel; (ii) to propose an efficient, simple, and general method to generate complex -samples; (iii) to make use of this channel in order to assess the bit error rate performance of an OFDM system. An analytical framework is then used, whose output is validated through Monte Carlo simulation. Several important conclusions concerning the system performance as a function of the channel parameters, namely, nonlinearity, clustering, and power imbalance of in-phase and quadrature components, are drawn.

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“…It is widely believed that wireless *Correspondence: mansour@ieee.org 1 LAb STIC -ENSTA Bretagne, 2 Rue François Verny, Brest 29200, France Full list of author information is available at the end of the article transmission channels can be modeled using stationary random variables [1,8,9]. We should highlight the fact that various PDFs have been used in the context of wireless communications, such as Gausssian, Rayleigh, log-normal, exponential, one-sided Gaussian distribution, Hoyt, Weibull, Rice, Nakagami-m, α − μ, and η − κ (see [10][11][12][13][14][15][16][17][18][19][20][21] and the references therein). It is worth pointing out that parametrical estimation methods can be used if the channel model (or a PDF for the real random variable (RV)) is selected or identified.…”

Section: Introductionmentioning

confidence: 99%

Blind estimation of statistical properties of non-stationary random variables

Mansour

Mesleh

Aggoune

2014

EURASIP J. Adv. Signal Process.

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To identify or equalize wireless transmission channels, or alternatively to evaluate the performance of many wireless communication algorithms, coefficients or statistical properties of the used transmission channels are often assumed to be known or can be estimated at the receiver end. For most of the proposed algorithms, the knowledge of transmission channel statistical properties is essential to detect signals and retrieve data. To the best of our knowledge, most proposed approaches assume that transmission channels are static and can be modeled by stationary random variables (uniform, Gaussian, exponential, Weilbul, Rayleigh, etc.). In the majority of sensor networks or cellular systems applications, transmitters and/or receivers are in motion. Therefore, the validity of static transmission channels and the underlying assumptions may not be valid. In this case, coefficients and statistical properties change and therefore the stationary model falls short of making an accurate representation. In order to estimate the statistical properties (represented by the high-order statistics and probability density function, PDF) of dynamic channels, we firstly assume that the dynamic channels can be modeled by short-term stationary but long-term non-stationary random variable (RV), i.e., the RVs are stationary within unknown successive periods but they may suddenly change their statistical properties between two successive periods. Therefore, this manuscript proposes an algorithm to detect the transition phases of non-stationary random variables and introduces an indicator based on high-order statistics for non-stationary transmission which can be used to alter channel properties and initiate the estimation process. Additionally, PDF estimators based on kernel functions are also developed. The first part of the manuscript provides a brief introduction for unbiased estimators of the second and fourth-order cumulants. Then, the non-stationary indicators are formulated. Finally, simulation results are presented and conclusions are derived.

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Nakagami- m phase-envelope joint distribution

Cited by 120 publications

References 6 publications

On the capacity of MIMO correlated Nakagami- m fading channels using copula

On the capacity of MIMO correlated Nakagami- m fading channels using copula

The Complexα-μFading Channel with OFDM Application

Blind estimation of statistical properties of non-stationary random variables

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