Method to Cancel Impulsive Noise From Power-Line Communication Systems by Processing the Information in the Idle Carriers

Torío, Pablo; Sánchez, Manuel García

doi:10.1109/tpwrd.2012.2209776

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Finally, D k is transformed into the time domain signal d^n through the inverse discrete Fourier transform, and then, IN m n is reconstructed based on d^n. The frequency domain representation of IN M k is derived from m n through forwarding discrete fourier transform (DFT) [29]. The denoised signal R k is derived as follows:…”

Section: Iterative Denoising and Adjusting The Number Of Iterationsmentioning

confidence: 99%

“…The total noise term D , which contains the terms

W_{k}

and

I_{k}

, is generated from the following expression:

{\hat{D}}_{k} = false(R_{k}^{eq} - {\hat{S}}_{k} false) {\hat{H}}_{k}, k = 0, 1, 2, \dots, N - 1.

Finally,

{\hat{D}}_{k}

is transformed into the time domain signal

{\hat{d}}_{n}

through the inverse discrete Fourier transform, and then, IN

{\hat{m}}_{n}

is reconstructed based on

{\hat{d}}_{n}

. The frequency domain representation of IN

{\hat{M}}_{k}

is derived from

{\hat{m}}_{n}

through forwarding discrete fourier transform (DFT) [29]. The denoised signal

{\tilde{R}}_{k}

is derived as follows:

{\tilde{R}}_{k} = R_{k}^{eq} - {\hat{M}}_{k} {\hat{H}}_{k}^{- 1}, k = 0, 1, 2, \dots, N - 1.

If INC <

ψ false(μ, p false)

, the post‐multiple selector switches to the feedback branch and feeds back a cleaner received signal

\overset{false~}{R}

to the pre‐multiple selector to repeat the iterative operation, i.e.…”

Section: Proposed Decision Directed In Mitigation Schemementioning

confidence: 99%

See 1 more Smart Citation

Adaptive decision directed impulse noise mitigate in power line communication

Zhang

Liu

2018

IET signal process.

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Impulse noise (IN) is the main cause of performance degradation in high-speed power line communication systems. Traditional methods mainly focus on manually setting a fixed blanking threshold to mitigate the corresponding IN. However, the fixed threshold cannot adapt to the time-varying IN efficiently. To solve this problem, an adaptive IN-mitigation system is proposed based on orthogonal frequency division multiplexing in a time-varying IN channel. The characteristics of IN are preevaluated by the method of moment estimation. Moreover, the adaptive threshold is efficiently solved in closed form according to the IN characteristics. In addition, an adaptive iterative IN-mitigation block is designed to leverage the performance of the receiver. For the number of iterations, a look-up table is constructed according to the IN characteristics. The experimental results show that the proposed method achieves performance balance in weak, moderate, and heavy IN environments simultaneously. It is noted that the bit error rate significantly decreases with an increment in E b /N 0 .

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Section: Iterative Denoising and Adjusting The Number Of Iterationsmentioning

confidence: 99%

“…The total noise term D , which contains the terms

W_{k}

and

I_{k}

, is generated from the following expression:

{\hat{D}}_{k} = false(R_{k}^{eq} - {\hat{S}}_{k} false) {\hat{H}}_{k}, k = 0, 1, 2, \dots, N - 1.

Finally,

{\hat{D}}_{k}

is transformed into the time domain signal

{\hat{d}}_{n}

through the inverse discrete Fourier transform, and then, IN

{\hat{m}}_{n}

is reconstructed based on

{\hat{d}}_{n}

. The frequency domain representation of IN

{\hat{M}}_{k}

is derived from

{\hat{m}}_{n}

through forwarding discrete fourier transform (DFT) [29]. The denoised signal

{\tilde{R}}_{k}

is derived as follows:

{\tilde{R}}_{k} = R_{k}^{eq} - {\hat{M}}_{k} {\hat{H}}_{k}^{- 1}, k = 0, 1, 2, \dots, N - 1.

If INC <

ψ false(μ, p false)

, the post‐multiple selector switches to the feedback branch and feeds back a cleaner received signal

\overset{false~}{R}

to the pre‐multiple selector to repeat the iterative operation, i.e.…”

Section: Proposed Decision Directed In Mitigation Schemementioning

confidence: 99%

Adaptive decision directed impulse noise mitigate in power line communication

Zhang

Liu

2018

IET signal process.

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“…Only a few researchers (i.e., [14,15]) have analyzed this point of view, but they determine other factors that influenced the requirements of network performance. For example, a novel technique of using single frequency network with flooding based routing that converts the channel congestion into channel cooperation for a narrowband PLC application is described in [16].…”

Section: Related Workmentioning

confidence: 99%

A Flexible Approach for Smart Management of Transmissions in Power Line Communications

Collotta

Tirrito

2014

Journal of Computer Networks and Communications

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Power line communications (PLCs) refer to a technology based on the existing electrical wiring to transmit data among the devices connected to the network. The PLC technology is an excellent solution widely studied and analysed by researchers, even in those areas characterized by strict requirements, such as industries. In this paper, a technique based on fuzzy logic, for the dynamic management of the amplitude of the signal emitted by the devices of a power line network, is proposed. The main aim is to manage the amplitude of the transmission signal in order to reduce the noise introduced into the network, and, as a consequence, the power consumption, increasing data transmission quality of network in terms of Quality of Service (QoS). This solution has been implemented into embedded systems based on the ADD1010, a power line System on Chip (SoC), and tested through a real scenario realized in laboratory.

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“…As a result, when such noise occurs during data transmission, the energy of IN spreads among all the subcarriers of an OFDM based PLC system. The spread of energy takes place while demodulating the received signal by performing fast Fourier transformation (FFT) on the time domain samples that are affected by the IN [10] . In such a hostile environment, the pilot symbols used to estimate the channel are also severely affected, making precise channel estimation a challenging task [11] .…”

Section: Introductionmentioning

confidence: 99%

On channel estimation for power line communication systems in the presence of impulsive noise

Shrestha

Mestre

Payaró

2018

Computers & Electrical Engineering

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In this paper, two maximum-likelihood (ML) channel estimators that are robust and perform consistent channel estimation in the presence of impulsive noise (IN) for power line communication (PLC) systems are proposed and analyzed. The two estimators differ on the way they exploit the estimated IN to determine the coefficients of the channel impulse response. In the first approach, the channel estimator treats the estimated IN as a deterministic quantity; in the second as a random quantity. The performances of both estimators are analyzed and numerically evaluated. As it will be shown, between the two proposed estimators, the one that is based on the random approach outperforms the deterministic one in all typical PLC scenarios. However, the deterministic approach-based estimator can perform consistent channel estimation regardless of the IN behavior with less computational effort and becomes an efficient channel estimation strategy in situations where high computational complexity cannot be afforded.

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Method to Cancel Impulsive Noise From Power-Line Communication Systems by Processing the Information in the Idle Carriers

Cited by 11 publications

References 6 publications

Adaptive decision directed impulse noise mitigate in power line communication

Adaptive decision directed impulse noise mitigate in power line communication

A Flexible Approach for Smart Management of Transmissions in Power Line Communications

On channel estimation for power line communication systems in the presence of impulsive noise

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