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Deep learning-aided optical IM/DD OFDM approaches the throughput of RF-OFDM

Deep learning-aided optical IM/DD OFDM approaches the throughput of RF-OFDM
Deep learning-aided optical IM/DD OFDM approaches the throughput of RF-OFDM
Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as OOFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.
Autoencoder (AE), Bit error ratio (BER), Deep learning, Deep neural network (DNN), O-OFDMNet, Optical orthogonal frequency division multiplexing (O-OFDM), Peak-to-average power ratio (PAPR), Visible light communications (VLC)
0733-8716
212-226
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Zhang, Xiaoyu
ea1ec5dd-5b9f-4ba9-b420-c05e771a5ae3
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Hoang, Minh Tiep
79ed4c0b-02ee-420a-a4cf-eeb0c2715d76
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Luong, Thien V
a15fc6c2-8387-4e11-aae6-64553eb9770c
Zhang, Xiaoyu
ea1ec5dd-5b9f-4ba9-b420-c05e771a5ae3
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Hoang, Minh Tiep
79ed4c0b-02ee-420a-a4cf-eeb0c2715d76
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Luong, Thien V, Zhang, Xiaoyu, Xiang, Luping, Hoang, Minh Tiep, Xu, Chao, Petropoulos, Periklis and Hanzo, Lajos (2021) Deep learning-aided optical IM/DD OFDM approaches the throughput of RF-OFDM. IEEE Journal on Selected Areas in Communications, 40 (1), 212-226. (doi:10.1109/JSAC.2021.3126080).

Record type: Article

Abstract

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as OOFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

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O_OFDMNet_FINAL - Accepted Manuscript
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Accepted/In Press date: 7 June 2021
e-pub ahead of print date: 13 November 2021
Keywords: Autoencoder (AE), Bit error ratio (BER), Deep learning, Deep neural network (DNN), O-OFDMNet, Optical orthogonal frequency division multiplexing (O-OFDM), Peak-to-average power ratio (PAPR), Visible light communications (VLC)

Identifiers

Local EPrints ID: 449644
URI: http://eprints.soton.ac.uk/id/eprint/449644
ISSN: 0733-8716
PURE UUID: 3b310c01-e8ad-4957-baba-49ef62686452
ORCID for Xiaoyu Zhang: ORCID iD orcid.org/0000-0002-0793-889X
ORCID for Luping Xiang: ORCID iD orcid.org/0000-0003-1465-6708
ORCID for Chao Xu: ORCID iD orcid.org/0000-0002-8423-0342
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: 10 Jun 2021 16:30
Last modified: 28 Apr 2022 06:38

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Contributors

Author: Thien V Luong
Author: Xiaoyu Zhang ORCID iD
Author: Luping Xiang ORCID iD
Author: Minh Tiep Hoang
Author: Chao Xu ORCID iD
Author: Lajos Hanzo ORCID iD

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