Advanced multi-feedback stochastic parallel gradient descent wavefront correction in free-space optical communication

Li, Zhaokun; Shang, Tao; Liu, Xiongchao; Qian, Peiheng; Zhang, Yinling

doi:10.1016/j.optcom.2023.129268

Cited by 3 publications

(2 citation statements)

References 24 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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“…Similarly, Pengfei Wang and Nenggan Zenhg [39] propose two methods to achieve a linear convergence to the minimal solution by using an asynchronous stochastic recursive gradient. Li et al [40] propose a modification of the stochastic parallel gradient descendant algorithm for the free-space optical communication. Their adaptation follows the work originally made by Hu et al [41] for fiber coupling.…”

Section: Introductionmentioning

confidence: 99%

Modification of Learning Ratio and Drop-Out for Stochastic Gradient Descendant Algorithm

et al. 2023

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The stochastic gradient descendant algorithm is one of the most popular neural network training algorithms. Many authors have contributed to modifying or adapting its shape and parametrizations in order to improve its performance. In this paper, the authors propose two modifications on this algorithm that can result in a better performance without increasing significantly the computational and time resources needed. The first one is a dynamic learning ratio depending on the network layer where it is applied, and the second one is a dynamic drop-out that decreases through the epochs of training. These techniques have been tested against different benchmark function to see their effect on the learning process. The obtained results show that the application of these techniques improves the performance of the learning of the neural network, especially when they are used together.

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