Narrowband PLC channel modeling using USRP and PSK modulations

Familua, Ayokunle Damilola; Ogunyanda, K.; Swart, Theo G.; Ferreira, H.C.; Olst, Rex van; Cheng, Ling

doi:10.1109/isplc.2014.6812355

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…A single error-free state would not show this adequately, so the use of two error-free states is justified. Indoor PLC channels in [24], visible light communication channels in [39], and underwater acoustic channels in [40] were successfully modeled using an FMM with a single-error state. In these works, two or three error-free states with reasonable precision represented the channel burst behavior.…”

Section: The Fritchman-markov Modelmentioning

confidence: 99%

“…This is because PMM can take into account how impulses occur in bursts. Subsequently, the Fritchman–Markov Model (FMM) [ 22 ] has been utilized to study the statistical properties of noise and disturbances and also to model noise processes in a wide range of digital communication problems [ 14 , 23 , 24 ]. In [ 23 ], Familua and Cheng used the Fritchman model and the Baum–Welch algorithm to model the noise and disturbances on the in-house CENELEC A-band channel based on experimental measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [ 25 ] modeled impulsive noise over an automotive power line communication network with an improved Markov model. Familua et al [ 24 ] also developed different phase-shift keying (PSK) modulation schemes using software-defined radio and the universal software radio peripheral (USRP) as an experimental platform for the modeling of noise and disturbances on the low-voltage in-house CENELEC A-band environment with a first-order Markov model. Myint et al [ 26 ] recently investigated the error behaviors in LDPC-coded and Turbo-coded OFDM data transmission systems in a 5G environment.…”

Section: Introductionmentioning

confidence: 99%

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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: The Fritchman-markov Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Furthermore, the transmission over such channel can be attenuated by the signal reflection due to the mismatching of the impedance of the electrical network because of its topology and physical properties. Therefore, one of the major issues is studying the transfer function of the PLCs and to find appropriate channel model which can perfectly represent it in computer simulations 85 . However, there are number of the channel models that can accurately model the transmission channel are proposed in the literature such as multipath and two‐port network models, the latter also know as ABCD matrix channel mode 86,87 …”

Section: Inside Train Communicationmentioning

confidence: 99%

Communication systems of high‐speed railway: A survey

Gheth

Rabie

Adebisi

et al. 2021

Trans Emerging Tel Tech

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Modern railway services are required to deliver good quality services to the passenger throughout the whole journey. These include improved performances, safety, and reduce delays. There is also the requirement for in‐train customer experience such as infotainment and access to reliable communication systems. The railway industry has employed different types and generations of communication systems in recent times. Signaling systems were used at the early stage of the railway services. Today, communication systems such as the second‐generation communication system, also known as the global system for mobile communications (GSM), the Third‐generation (3G) and the Fourth‐generation (4G) are utilized in the railway industry. In this paper, we present a brief history of railway communication systems, their features and applications. In addition, we discuss the technical challenges and potential solutions of in‐train communication systems and how data can be distributed on‐board and between the train coaches using state of the art and hybrid communication technologies.

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NB-PLC channel: Estimation of periodic impulsive noise parameters and mitigation techniques

Andreadou

Fulli

2018

International Journal of Electrical Power & Energy Systems

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Narrowband PLC channel modeling using USRP and PSK modulations

Cited by 11 publications

References 10 publications

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

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

Communication systems of high‐speed railway: A survey

NB-PLC channel: Estimation of periodic impulsive noise parameters and mitigation techniques

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