Machine learning assisted two-dimensional beam-steering for integrated optical phased arrays

Sved, Joel; Song, Shijie; Zhou, Lihua; Minasian, R.A.; Yi, Xiaoke

doi:10.1016/j.optcom.2023.129517

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Given a noisy input, the neural network can analyze repetitive features from large datasets to classify these inputs. Previous work has shown the efficacy of neural networks and deep learning in improving FSO communication schemes [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In recent years, CNNs have been used in particular to increase the information capacity of optical communication schemes using Laguerre-Gaussian modes and various demodulation techniques [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for enhanced free-space optical communications

Bart,

Savino,

Regmi

et al. 2023

Mach. Learn.: Sci. Technol.

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Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of light used in ON–OFF keying (OOK) free-space optical (FSO) communication. Here we present and experimentally validate a convolutional neural network (CNN) to reduce the bit error rate of FSO communication in post-processing that is significantly simpler and cheaper than existing solutions based on advanced optics. Our approach consists of two neural networks, the first determining the presence of bit sequences in thermal noise and turbulence and the second demodulating the bit sequences. All data used for training and testing our network is obtained experimentally by generating OOK bit streams, combining these with thermal light, and passing the resultant light through a turbulent water tank which we have verified mimics turbulence in the air to a high degree of accuracy. Our CNN improves detection accuracy over threshold classification schemes and has the capability to be integrated with current demodulation and error correction schemes.

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Section: Introductionmentioning

confidence: 99%

Deep learning for enhanced free-space optical communications

Bart,

Savino,

Regmi

et al. 2023

Mach. Learn.: Sci. Technol.

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Deep neural network-based phase calibration in integrated optical phased arrays

Kim,

Yoon

et al. 2023

Sci Rep

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Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams.

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Integrated Microwave Photonic Signal Processing and Sensing

Minasian

2023

2023 23rd International Conference on Transparent Optical Networks (ICTON)

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Machine learning assisted two-dimensional beam-steering for integrated optical phased arrays

Cited by 4 publications

References 31 publications

Deep learning for enhanced free-space optical communications

Deep learning for enhanced free-space optical communications

Deep neural network-based phase calibration in integrated optical phased arrays

Integrated Microwave Photonic Signal Processing and Sensing

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