Deep Learning Based Decoding for Polar Codes in Markov Gaussian Memory Impulse Noise Channels

Tseng, Sheng‐Tsaing; Hsu, Wen-Jing; Tseng, Der-Feng

doi:10.1007/s11277-021-08923-0

Cited by 9 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study combines coding methods used in PLC systems and deep learning methods to achieve higher accuracy rates [139].…”

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

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

View full text Add to dashboard Cite

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.

show abstract

“…The study combines coding methods used in PLC systems and deep learning methods to achieve higher accuracy rates [139].…”

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

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

View full text Add to dashboard Cite

show abstract

“…A DNN approach to evaluating model parameters in power line communication systems is reported in [20], where the impulse noise model was inadvertently assumed. Other than the aforementioned FC neural network model, the Long Short-Term Memory (LSTM) neural network decoder for Polar codes facing memory impulse noise is proposed in [24], where both system performance and latency results are demonstrated to be superior than those of existing decoding strategies such as Successive Cancellation, Belief Propagation and Successive Cancellation List approaches.…”

Section: Introductionmentioning

confidence: 99%

Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM System

Tseng

Lin

Tseng

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Due to inevitable corruption on signals across all subcarriers, strong and frequently occurring impulses pose a grim threat to Orthogonal Frequency Division Multiplexing (OFDM) systems, where system performance is likely to be further degraded by multipath fading phenomenon. Recently, a deep neural network (DNN) receiver is shown to be capable of not only implicitly estimating channel state information but also explicitly recovering the transmitted symbols without assuming the signal-to-noise ratio (SNR) level. In principle, the DNN model is trained using data generated from computer simulations–according to WINNER II channel model as fading channels, and the additive white Gaussian noise (AWGN) as background noise. As with its conventional peers which assume the inter- fering source from AWGN only, the aforementioned DNN receiver is prone to substantial performance loss when subject to impulse noise. To address this obstacle, this paper incor- porates the fine-tuning notion into the DNN model–by substituting impulse noise-laced data samples for succeeding training data–with an aim at enhancing representation learning. To attest the efficacy of the proposed DNN receiver, the bit error rate (BER) result derived from a compressive sensing-based receiver, enabled by consensus alternating direction method of multipliers (ADMM), is used as a benchmark for performance comparison. Noteworthily, the induced BER performance is comparable to that of the clipping-featured receiver, the threshold of which however solicits the knowledge of the SNR value, an assumption that is otherwise relaxed by the proposed DNN receiver. Furthermore, through extensive simulations, the deep learning based approach demonstrates promising robustness against impulse noise model mismatch between training and testing counterparts.

show abstract

“…Recently, the researchers studied polar codes for stochastic processes with memory 20 . Tseng et al 21 applied the long short‐term memory (LSTM) neural network for polar codes under the Markov–Gaussian memory impulsive noise channel.…”

Section: Introductionmentioning

confidence: 99%

Performance of polar codes with low complexity cyclic redundancy check aided bit‐flipping for successive cancellation list decoding over power line channels

Zhou,

Li,

Zhao

et al. 2023

Int J Communication

View full text Add to dashboard Cite

SummaryPower lines have complex transmission characteristics and noise interference when used as information transmission channel, thus limiting their data‐carrying rate and reliability. Impulsive noise is the most dominant factor degrading the performance of communication systems over power lines. To combat the impulsive noise, polar codes are introduced into the orthogonal frequency division multiplexing (OFDM)‐based power line communication (PLC) system. Because the successive cancelation list bit‐flip (SCLF) decoding algorithm of polar codes has high decoding complexity, a low complexity segmented cyclic redundancy check (CRC)‐aided SCLF decoding is proposed to improve the decoding performance. The decoding process can be terminated early with segmented CRC, which can reduce the bit‐flip decoding complexity significantly with short and medium block lengths. Experimental results demonstrate that for various configurations, the proposed decoder outperforms the traditional CRC‐aided SCL decoder in terms of decoding performance and complexity, and the suggested polar codes‐based OFDM‐based PLC system can reduce the bit error rate of the system in the presence of impulsive noise.

show abstract

Deep Learning Based Decoding for Polar Codes in Markov Gaussian Memory Impulse Noise Channels

Cited by 9 publications

References 34 publications

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

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

Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM System

Performance of polar codes with low complexity cyclic redundancy check aided bit‐flipping for successive cancellation list decoding over power line channels

Contact Info

Product

Resources

About