Impulsive Noise Measurements

Agba, Basile L.; Sacuto, Fabien; Au, Minh; Labeau, Fabrice; Gagnon, François

doi:10.1007/978-3-319-91328-5_3

Cited by 9 publications

(1 citation statement)

References 25 publications

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“…Additionally, power line network connections and power supply fluctuations within the household can cause transient impulses that affect the quality and reliability of the PLC signal. Furthermore, external sources of noise, such as nearby lightning strikes [ 18 ] or RFI from electronic devices, can couple into the power line network and manifest as impulsive noise, further degrading the performance of in-house PLC systems. Undoubtedly, one of the most important natural sources of impulse noise is lightning (atmospheric discharge).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the Fritchman–Markov Model

Iyiola

Familua

Swart

et al. 2023

Sensors

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Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman–Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system’s statistical error process. The PLC system’s fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman–Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman–Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time.

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Section: Introductionmentioning

confidence: 99%