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Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection

End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OF...

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Main Authors: Xiaoyu, Zhang, Thien, Van Luong, Periklis, Petropoulos, Lajos, Hanzo
Format: Bài trích
Language:English
Published: IEEE 2022
Subjects:
Optical transmitters
Optical receivers
Online Access:https://ieeexplore.ieee.org/document/9674226
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5741
https://doi.org/10.1109/JLT.2022.3141222
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spelling oai:localhost:PNK-57412022-08-17T05:54:52Z Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection Xiaoyu, Zhang Thien, Van Luong Periklis, Petropoulos Lajos, Hanzo Optical transmitters Optical receivers End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OFDM) scheme, termed as LACONet, for exploiting the increased bandwidth efficiency of LACO-OFDM. LACONet employs a Neural Network (NN) at the transmitter for bit-to-symbol mapping, and another NN at the receiver for recovering the data bits, which together form an AE and can be trained in an end-to-end manner for simultaneously minimizing both the BER and PAPR. Moreover, the detection architecture of LACONet is drastically simplified compared to classical LACO-OFDM, since the Fast Fourier Transform (FFT) operation is applied only once at the receiver. We further propose a generalized AE-aided optical OFDM scheme for IM/DD communications, termed as IMDD-OFDMNet, where the unipolarity of the Time Domain (TD) signal is no longer guaranteed by the Hermitian Symmetry, but rather by taking the absolute square value of the complex TD signal. As such, all available subcarriers of IMDD-OFDMNet are used for carrying useful information, hence it has a higher throughput than the LACO-based schemes. As a further benefit, its transceiver requires only a single Inverse FFT or FFT. Finally, simulation results are provided to show that our learning schemes achieve better BER and PAPR performance than their conventional counterparts. 2022-05-05T07:26:15Z 2022-05-05T07:26:15Z 2022 Bài trích https://ieeexplore.ieee.org/document/9674226 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5741 https://doi.org/10.1109/JLT.2022.3141222 en IEEE
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Optical transmitters
Optical receivers
spellingShingle Optical transmitters
Optical receivers
Xiaoyu, Zhang
Thien, Van Luong
Periklis, Petropoulos
Lajos, Hanzo
Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
description End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OFDM) scheme, termed as LACONet, for exploiting the increased bandwidth efficiency of LACO-OFDM. LACONet employs a Neural Network (NN) at the transmitter for bit-to-symbol mapping, and another NN at the receiver for recovering the data bits, which together form an AE and can be trained in an end-to-end manner for simultaneously minimizing both the BER and PAPR. Moreover, the detection architecture of LACONet is drastically simplified compared to classical LACO-OFDM, since the Fast Fourier Transform (FFT) operation is applied only once at the receiver. We further propose a generalized AE-aided optical OFDM scheme for IM/DD communications, termed as IMDD-OFDMNet, where the unipolarity of the Time Domain (TD) signal is no longer guaranteed by the Hermitian Symmetry, but rather by taking the absolute square value of the complex TD signal. As such, all available subcarriers of IMDD-OFDMNet are used for carrying useful information, hence it has a higher throughput than the LACO-based schemes. As a further benefit, its transceiver requires only a single Inverse FFT or FFT. Finally, simulation results are provided to show that our learning schemes achieve better BER and PAPR performance than their conventional counterparts.
format Bài trích
author Xiaoyu, Zhang
Thien, Van Luong
Periklis, Petropoulos
Lajos, Hanzo
author_facet Xiaoyu, Zhang
Thien, Van Luong
Periklis, Petropoulos
Lajos, Hanzo
author_sort Xiaoyu, Zhang
title Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
title_short Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
title_full Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
title_fullStr Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
title_full_unstemmed Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection
title_sort machine-learning-aided optical ofdm for intensity modulated direct detection
publisher IEEE
publishDate 2022
url https://ieeexplore.ieee.org/document/9674226
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5741
https://doi.org/10.1109/JLT.2022.3141222
_version_ 1751856280626003968
score 8.891145

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