Impulsive noise modeling and robust receiver design

Clavier, Laurent; Peters, Gareth W.; Septier, François; Nevat, Ido

doi:10.1186/s13638-020-01868-1

Cited by 14 publications

(11 citation statements)

References 116 publications

(196 reference statements)

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“…Green Wireless Networks: Peripheral capacity analysis is a vital attribute of energy-efficient wireless networks that helps to reduce the excess of energy during communication challenges [15]. 3. IoT and Sensor Networks: Due to this the IoT technologies are more effective when these communication links are more efficient.…”

Section: Introductionmentioning

confidence: 99%

Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems

Al-Rubaye,

ALRikabi,

Hazim

2023

Heritage and Sustainable Development

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The highest rate at which information may be reliably sent via a communication link is known as its capacity. In the case of non-Gaussian noise, the capacity of the channel depends on the specific characteristics of the noise, which can cause severe errors and reduce the reliability of communication systems over a fading channel. The Gaussian mixture impulsive noise model (GMINM), which is a more general and flexible non-Gaussian model for impulsive noise, has been compared in this paper with the Middleton Class-A impulsive noise model (MCAINM) in terms of derived channel capacity normalized by channel bandwidth (C/BW) with and without Rayleigh fading (Rf) channels. It also investigated the trade-off between complexity and accuracy in modeling the impulsive noise using two simplified Middleton Class-A impulsive noise models based on derived C/BW. The derived C/BW of these models under various conditions, such as different signal-to-noise ratios and impulsive noise parameters and models, have been performed and evaluated using two different scenarios: the exact method and the semi-analytical method. When the impulsive noise parameters and A are both near 0 in GMINM and MCAINM, respectively, the capacity of the impulsive noise channel is found to be equivalent to that of the Gaussian channel sustainable, as shown by the findings based on Monte-Carlo simulations. We have shown that when the impulsive noise decreases, the capacity increases in all models; however, the capacity of Gaussian noise is higher than the capacity of non-Gaussian noise, which in turn is higher than the capacity of non-Gaussian noise over the Rf channel overall values of SNR in dB. Moreover, multi-channel configuration introduces spatial diversity and multiplexing gains that have been proposed to sustainably optimize the ergodic capacity for the challenge case when the channel state information (CSI) is unknown at the transmitter in non-Gaussian noise over Rf channel. In today's rapidly evolving world, sustainable communication systems play a crucial role in ensuring efficient and responsible utilization of resources. As the demand for wireless communication continues to rise, it becomes imperative to optimize the capacity of communication channels, especially in scenarios involving non-Gaussian noise models and fading channels.

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Section: Introductionmentioning

confidence: 99%

Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems

Al-Rubaye,

ALRikabi,

Hazim

2023

Heritage and Sustainable Development

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“…Most of existing algorithms consider noise as being Gaussian or negligible. However, in many applications the measurements are affected by impulsive noise or outliers, e.g., [21][22][23], in which context standard methods fail to achieve the BSS. To deal with impulsive noise, some authors have proposed robust batch BSS algorithms, e.g., [7,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A New Robust Adaptive Algorithm for Second Order Blind Source Separation

Bekhtaoui

Abed-Meraim

Meche

et al. 2022

ENPESJ

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This paper deals with the adaptive blind source separation problem in presence of impulsive noise. New algorithms extending the well known SOBI method from batch to adaptive scheme are introduced. At first, the standard Gaussian noise case is considered, leading to our first algorithm referred to as Adaptive SOBI (A-SOBI). Later on, a robust version of A-SOBI, referred to as RA-SOBI is derived to handle the impulsive noise case. RA-SOBI relies on robust subspace tracking for the whitening stage together with robust correlation estimation for the separation stage. All proposed algorithms are of relatively low complexity and allow to achieve good separation quality as illustrated by our simulation results.

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“…It has been shown in theory and experimentally [1] that the generated interference will, in such situations, exhibit an impulsive nature. It is essential to take into account the interference statistical properties, for instance to design robust receivers [2].…”

Section: Introductionmentioning

confidence: 99%

Alpha-Stable model for Interference in IoT networks

Clavier

Pedersen

Rodríguez

et al. 2021

2021 IEEE Conference on Antenna Measurements &Amp; Applications (CAMA)

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The increase in connected device, linked with the evolution of IoT and cellular networks with 5G and towards 6G makes crucial the limitation of the interference impact. Devices in networks (like LoRa, SigFox and even WiFi) or in different networks are not necessarily coordinated to optimize their communication scheme and reduce interference. As a consequence, it is essential to characterize the interference statistics in order to assess its impact on the network performance and define the appropriate access policies. With a theoretical approach, a large number of studies have tackled this characterization question. However, much less is available from an experimental point of view. In this paper, we address this key gap. Measurements were performed in Aalborg, Denmark, in the unlicensed 863 − 870 MHz. We show that the heavy tailed behaviour of the interference, predicted by theory, is indeed appropriate. We also show that the α-stable distribution can be a good model for interference in IoT networks.

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Impulsive noise modeling and robust receiver design

Cited by 14 publications

References 116 publications

Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems

Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems

A New Robust Adaptive Algorithm for Second Order Blind Source Separation

Alpha-Stable model for Interference in IoT networks

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