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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
Format: Bài trích
Language:English
Published: IEEE Transactions on Magnetics 2022
Subjects:
Optical communications
LACO-OFDM
Online Access:https://ieeexplore.ieee.org/document/9674226/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5883
https://doi.org/10.1109/jlt.2022.3141222
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spelling oai:localhost:PNK-58832022-08-17T05:54:54Z Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection Xiaoyu, Zhang Thien Van, Luong Periklis, Petropoulos Optical communications LACO-OFDM 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. 2022-07-13T01:59:48Z 2022-07-13T01:59:48Z 2022 Bài trích https://ieeexplore.ieee.org/document/9674226/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5883 https://doi.org/10.1109/jlt.2022.3141222 en IEEE Transactions on Magnetics
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Optical communications
LACO-OFDM
spellingShingle Optical communications
LACO-OFDM
Xiaoyu, Zhang
Thien Van, Luong
Periklis, Petropoulos
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
author_facet Xiaoyu, Zhang
Thien Van, Luong
Periklis, Petropoulos
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 Transactions on Magnetics
publishDate 2022
url https://ieeexplore.ieee.org/document/9674226/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5883
https://doi.org/10.1109/jlt.2022.3141222
_version_ 1751856316836478976
score 8.891053

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