Improved Channel Equalization using Deep Reinforcement Learning and Optimization

Katwal, Swati; Bhatia, Vinay

doi:10.4108/eai.28-10-2021.171685

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…The proposed system has a limitation in that it only operates effectively when the quantity of subcarriers is fixed. If there is a need to adjust the number of subcarriers Accordingly, another study conducted by Katwal, Swati, and Vinay Bhatia [ 15] This dealt with the problem of signal interference that was presented in the communication system that was investigated for this study. After the creation of WOA in the SI block, which was followed by a DL block that used Q-learning to determine the ideal bit streams that would give the least amount of interference while data was being transmitted, the authors proposed a multi-block design.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

S Hanoon,

F Khazal,

M Jamel

2024

ejuow

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Channel equalization is crucial to the efficiency of wireless network systems. To improve communication reliability and decrease computing complexity, 5G networks have made great progress with the help of deep learning (DL). When applied to 5G and future networks, deep learning has been proven to increase system performance while decreasing computational complexity. ZF is often used to acquire a channel equalizer because of its inexpensive cost and lack of statistical expertise; nonetheless, ZF has a large equalizer error. Because of the limitations of the Minimum Mean Square Error (MMSE) method, deep learning models provide a more convenient solution for solving the channel equalizer issue. Since deep learning may give a better performance-complexity trade-off, it can be used to enhance MMSE and ZF channel equalizers. Its generalization and resilience also make it an attractive tool for use in this context. To address the shortcomings of the ZF and MMSE equalizers, this thesis focuses on developing a DL-based channel equalizer. An Orthogonal Frequency-Division Multiplexing (OFDM)-based single-input-single-output (SISO) system is used to measure the DL-based equalizer's efficacy. Simulation results show that the DL-based channel equalizer can achieve at least a 3 dB gain in SNR over the MMSE equalizer for a SER = 10-3 in low and high selective channel models, respectively, validating the performance of the benchmarked equalizer using different frequency selectivity levels. Furthermore, the DL-based equalizer results in a drastic decrease in computing complexity in contrast to the ZF and MMSE equalization techniques.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

S Hanoon,

F Khazal,

M Jamel

2024

ejuow

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“…The process of restoring the original signal uses a filter such as a finite impulse response filter as a channel equalizer [ 28 , 29 ]. The role of the equalizer filter is to use the observations/estimations of the channel to restore the transmitted signal and reduce the distortions caused by the channel as much as possible [ 7 , 10 , 15 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

“…The role of the equalizer filter is to use the observations of the channel to restore the transmitted signal and reduce the distortions caused by the channel as much as possible [ 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Turbo Codes, LDPC Codes, and Polar Codes over an AWGN Channel in the Presence of Inter Symbol Interference

Cuc

Morgoș

Grava

2023

Sensors

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This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications.

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