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.
Autoencoder, Bit error ratio, LACO-OFDM, OFDM, Optical OFDM, Optical communications, deep learning, neural network, peak-to-average-power ratio, visible light communications
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
15 April 2022
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), .
(doi:10.1109/JLT.2022.3141222).
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.
Text
LACONet_paper (final)
- Accepted Manuscript
More information
Accepted/In Press date: 2 January 2022
e-pub ahead of print date: 7 January 2022
Published date: 15 April 2022
Additional Information:
Funding Information:
This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Projects EP/P034284/1 and EP/P003990/1 (COALESCE). The work of Lajos Hanzo was supported in part by the European Research Council's Advanced Fellow Grant QuantCom under Grant 789028.
Publisher Copyright:
© 1983-2012 IEEE.
Keywords:
Autoencoder, Bit error ratio, LACO-OFDM, OFDM, Optical OFDM, Optical communications, deep learning, neural network, 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
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Date deposited: 01 Feb 2022 17:45
Last modified: 14 Dec 2024 03:02
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