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Machine-learning-aided optical OFDM for intensity modulated direct detection

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-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.
LACO-OFDM, OFDM, Optical communications, autoencoder, bit error ratio, deep learning, neural network, optical OFDM, peak-to-average-power ratio, visible light communications
0733-8724
2357-2369
Zhang, Xiaoyu
ea1ec5dd-5b9f-4ba9-b420-c05e771a5ae3
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhang, Xiaoyu
ea1ec5dd-5b9f-4ba9-b420-c05e771a5ae3
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Zhang, Xiaoyu, Luong, Thien V, Petropoulos, Periklis and Hanzo, Lajos (2022) Machine-learning-aided optical OFDM for intensity modulated direct detection. Journal of Lightwave Technology, 40 (8), 2357-2369. (doi:10.1109/JLT.2022.3141222).

Record type: Article

Abstract

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.

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LACONet_paper (final) - Accepted Manuscript
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More information

Accepted/In Press date: 2 January 2022
e-pub ahead of print date: 7 January 2022
Keywords: LACO-OFDM, OFDM, Optical communications, autoencoder, bit error ratio, deep learning, neural network, optical OFDM, peak-to-average-power ratio, visible light communications

Identifiers

Local EPrints ID: 454161
URI: http://eprints.soton.ac.uk/id/eprint/454161
ISSN: 0733-8724
PURE UUID: fcfbd20b-3c79-456d-b366-e5424517749d
ORCID for Xiaoyu Zhang: ORCID iD orcid.org/0000-0002-0793-889X
ORCID for Periklis Petropoulos: ORCID iD orcid.org/0000-0002-1576-8034
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 01 Feb 2022 17:45
Last modified: 13 Aug 2022 02:05

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Contributors

Author: Xiaoyu Zhang ORCID iD
Author: Thien V Luong
Author: Lajos Hanzo ORCID iD

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