Capacity of 60 GHz Wireless Communications Based on QAM

Wang, Jingjing; Li, Na; Shi, Wei; Ma, Yangyang; Liang, Xiulong; Dong, Xinli

doi:10.1155/2014/815617

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Note that to generate the 8-QAM constellation we used the standard Matlab function qammod; the same constellation shape was used in Refs. [74]-[77].…”

mentioning

confidence: 99%

Neural Networks-Based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Freire

Napoli

Spinnler

et al. 2022

IEEE J. Select. Topics Quantum Electron.

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This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) based nonlinear channel equalizers in coherent optical communication systems. The goal of this study is to guide researchers and engineers working in this field. We start by clarifying the metrics used to evaluate the equalizers' performance, relating them to the loss functions employed in the training of the NN equalizers. The relationships between the channel propagation model's accuracy and the performance of the equalizers are addressed and quantified. Next, we assess the impact of the order of the pseudo-random bit sequence used to generate the -numerical and experimental -data as well as of the DAC memory limitations on the operation of the NN equalizers both during the training and validation phases. Finally, we examine the critical issues of overfitting limitations, the difference between using classification instead of regression, and batch-size-related peculiarities. We conclude by providing analytical expressions for the equalizers' complexity evaluation in the digital signal processing (DSP) terms and relate the metrics to the processing latency.

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“…Note that to generate the 8-QAM constellation we used the standard Matlab function qammod; the same constellation shape was used in Refs. [74]-[77].…”

mentioning

confidence: 99%

Neural Networks-Based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Freire

Napoli

Spinnler

et al. 2022

IEEE J. Select. Topics Quantum Electron.

View full text Add to dashboard Cite

show abstract

“…Note that to generate the 8-QAM constellation we used the standard Matlab function qammod; the same constellation was used in Refs [65]-[68]…”

mentioning

confidence: 99%

Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Freire¹,

Napoli²,

Spinnler³

et al. 2021

Preprint

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This paper performs a detailed multi-faceted analysis of the key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel equalizers in coherent optical communication systems. Our study aims to guide researchers and engineers working in this field. We start by clarifying the metrics used to evaluate the equalizers' performance, relating them to the loss functions employed in the training of the NN equalizers. The relationships between the channel propagation model's accuracy and the performance of the equalizers are addressed and quantified. Next, we assess the impact of the order of the pseudo-random bit sequence used to generate the -numerical and experimental -data as well as of the DAC memory limitations on the operation of the NN equalizers both during training and validation phases. Finally, we examine the critical issues of overfitting limitations, a difference between using classification instead of regression, and the batch size-related peculiarities. We conclude by providing analytical expressions for the equalizers' complexity evaluation in the digital signal processing (DSP) terms.

show abstract

Optimization of 16-QAM and 32-QAM Constellations for Mitigating Impairments of Phase Noise in Millimeter-Wave Receivers

Mvone¹,

Hannachi

Hammou³

et al. 2022

IEEE Trans. Wireless Commun.

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Capacity of 60 GHz Wireless Communications Based on QAM

Cited by 4 publications

References 11 publications

Neural Networks-Based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Neural Networks-Based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Optimization of 16-QAM and 32-QAM Constellations for Mitigating Impairments of Phase Noise in Millimeter-Wave Receivers

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