Hiển thị ở chế độ nhân viên: Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection

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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Tác giả chính:	Xiaoyu, Zhang, Thien, Van Luong, Periklis, Petropoulos, Lajos, Hanzo
Định dạng:	Bài trích
Ngôn ngữ:	English
Nhà xuất bản:	IEEE 2022
Chủ đề:	Optical transmitters Optical receivers
Truy cập trực tuyến:	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
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score	8.891145

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

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