Hybrid cooperative spectrum sensing for improving cognitive power line communication systems

Menezes, Alam; Coutinho, Yan F.; Ribeiro, Moisés V.

doi:10.1016/j.compeleceng.2022.108286

Cited by 3 publications

(2 citation statements)

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“…PLC is widely used in many fields due to its communication quality, speed, cost, and ease of installation advantages. However, noise and signal distortions in the power grid can impact the communication quality of PLC [18,19]. Different techniques are employed for the development and improvement of PLC.…”

Section: A Power Line Communicationmentioning

confidence: 99%

“…Instead of focusing on suppressing the IN-affected samples in the received signal, a more effective approach involves estimating the IN and integrating it into the channel estimation process [18]. This can be accomplished by utilizing signal processing schemes proposed in [19][20][21] to estimate the sparse IN components present in the received signal. A likelihood function can be formulated to leverage the estimated IN and observations of the received signal.…”

Section: Related Work On ML Based Error Correction Codes and Communic...mentioning

confidence: 99%

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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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Section: A Power Line Communicationmentioning

confidence: 99%