Communications in the Near Maritime Environment using Light Carrying Orbital Angular Momentum Midn 1/C Marco McGavick, Prof. Avramov-Zamurovic, Prof. Nelson Motivation Related Work • Free space optical (FSO) communications have a low Machine Learning probability of intercept or detection. However, optical • [2] attempted to establish a relationship between • Machine learning is necessary to classify the received turbulence significantly deteriorates FSO transmissions. the topological charge of the OAM carried by a image after degradation due to turbulence. beam and deterioration due to optical turbulence. • Light carrying orbital angular momentum (OAM) is more • Our classification program is a re-trained version of resilient to turbulence and exhibits orthogonality. This • [3] used light carrying OAM to encode, transmit, AlexNet [4], a convolutional neural network (CNN). means that beams can be superimposed with no and recover a grayscale image of Mozart. Their Using a beam set of 4 seed beams (15 total beams), interference. system was not optimized for high data rates. test images were taken using the in-lab experimental setup. These images were used to modify AlexNet to • These properties can be leveraged to increase data classify new images of beams in our beam set. transmission rates in the maritime environment despite Methods high turbulence. Machine learning allows us to increase • Encoding: Laguerre-Gaussian beams carry OAM. the bit rate by increasing the number of bits per symbol. The beam equation is used to create phase screens imparted on an SLM. There are infinitely many combinations of unique topological charges. We generate base beams and use the superpositions of these beams to create an alphabet. The design challenge is to generate the most distinct set of Figure 5: the results of using our classification intensity distributions to comprise our alphabet, program to classify four random images selected Figure 4: the architecture of the AlexNet CNN. thus aiding machine learning algorithm from 4,500 test images of 15 beams. The four images Modifications for our program include changing the were classified with 100% accuracy. Figure 1: the deteriorating effect atmospheric • Propagating: the beam propagates through the output layer from 1000 classifications to match the turbulence has on a beam during propagation [1]. atmosphere, encountering optical turbulence like number of beams in the beam set [5] . heat differentials. In testing, propagation distance Conclusion Problem Statement will be roughly one kilometer. • Our machine learning program can accurately classify %% retraining AlexNet • Receiving/decoding: the beam strikes a screen. A images of beams carrying OAM, despite them • The goal of this project is to implement an image- for beams in beam set high-speed camera captures an image of the light propagating underwater through significant based communications system that reliably utilizes load beam .tif file intensity pattern. The image is submitted to our turbulence. light carrying OAM to encode information and resist split into 1000s of .png image classification program. distortion due to turbulence as it propagates. resize and crop beam images • This indicates that we can expect similar performance from outside experiments when the time comes. • We define reliable as receiving and decoding the save .png to beam’s folder End messages with >99% accuracy. References • We will test the process in the near maritime load AlexNet environment to better characterize the effects of change 23rd and 25th layers [1] LumOptica. “Atmospheric Laser Propagation Modelling.” LumOptica. https://lumoptica.com/atmospheric-laser-propagation-modelling optical turbulence on beam integrity. MyNet = retrain(AlexNet, images) [2] J. Wiedemann, C. Nelson, and S. Avramov-Zamurovic, “Scintillation of laser beams carrying orbital angular momentum propagating in a near maritime %% using MyNet environment,” Optics Communications, vol. 458, Mar, 2020. Test = classify(MyNet, new_images) [3] M. Krenn, R. Fickler, M. Fink, J. Handsteiner, M. Malik, T. Scheidl, R. Ursin, and A. Zeilinger, “Communication with spatially modulated light through turbulent air across Vienna,” New Journal of Physics, vol. 16, Nov. 2014. [4] “Transfer learning using AlexNet,” Mathworks. Available: https://www.mathworks.com/help/deeplearning/ug/transfer-learning-using- alexnet.html [Accessed Nov. 15, 2020]. Figure 3: experimental set up for transmission across [5] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "ImageNet the Severn river Results Classification with Deep Convolutional Neural Networks." Advances in neural information processing systems. 2012. • Due to COVID-19 limitations, current experimentation • Using a beam set of 16 beams (a “blank” is limited to the lab. The Severn river is replaced by a transmission being the 16th beam), our tub of water and mirrors to retain propagation classification program classified degraded images Acknowledgement Figure 2: beam propagation path across the Severn distance. Propagation occurs through the water. with 100% percent accuracy. • Professor Joel Esposito for introducing me to machine learning. river (~900 m) [2]. Optical turbulence is created with heat guns..