Index modulation aided multi carrier power line communication employing rank codes from cyclic codes

M.A.N.S., Raghavendra; Acharya, U. Shripathi

doi:10.1016/j.phycom.2019.100975

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…A power-line channel contaminated by impulsive noise is allowed to carry the n-bit code word. Data transmission will be compromised by random noise and a misspelled code phrase (error data) [26]. In this case, mistakes are fixed by calculating the Hamming distance between each pair of code words.…”

Section: Proposed Combined Rs Coding and Adaptive Lms Techniquementioning

confidence: 99%

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

Mohammed

Mahmood

2023

Bulletin EEI

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In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.

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Section: Proposed Combined Rs Coding and Adaptive Lms Techniquementioning

confidence: 99%

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

Mohammed

Mahmood

2023

Bulletin EEI

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“…Hamming Codes: Hamming codes provide singleerror correction and double-error detection capabilities. These codes are used to correct errors at the bit level during data transmission [47]. b. Reed-Solomon Codes: Reed-Solomon codes are block-based error correction codes that can correct multiple-bit errors.…”

Section: B Error Correction Codes and Communicationmentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field. INDEX TERMSPower line communication, error correction codes, machine learning, transmission control protocols, communication protocols, power networks.

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Performance of Orthogonal and Non-Orthogonal Space Time Block Code Through the Underwater Wireless Optical Channels

Naik,

Mans,

Acharya

et al. 2023

2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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Index modulation aided multi carrier power line communication employing rank codes from cyclic codes

Cited by 3 publications

References 27 publications

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Performance of Orthogonal and Non-Orthogonal Space Time Block Code Through the Underwater Wireless Optical Channels

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