Deep-learning-based OFDM channel compensation hardware implementation algorithm design

Zhongqian Liu; Dan Ding

doi:10.1117/12.2645649

23 August 2022 Deep-learning-based OFDM channel compensation hardware implementation algorithm design

Zhongqian Liu, Dan Ding

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123051L (2022) https://doi.org/10.1117/12.2645649
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

In order to solve the problem that some high-performance deep learning neural networks are not ideal for application in embedded devices due to the defects of high complexity and large computation. From the perspective of easy hardware implementation, the hardware implementation algorithm of OFDM channel compensation based on deep learning is researched and designed, and the channel compensation scheme of conventional pre-equalization + DNN fine compensation is designed based on the idea of joint efficient implementation of FPGA + GPU.

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Zhongqian Liu and Dan Ding "Deep-learning-based OFDM channel compensation hardware implementation algorithm design", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123051L (23 August 2022); https://doi.org/10.1117/12.2645649

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KEYWORDS

Orthogonal frequency division multiplexing

Data modeling

Computer simulations

Performance modeling

Statistical modeling

Algorithm development

Neural networks

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