Tuning of Optical Beamforming Networks

A Deep Learning Approach

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Abstract

Optical beamforming networks (OBFNs), which consist of many small and flat antennas, called phased array antennas (PAAs), can be tuned such that the signal beam from the airplanes can be steered towards a satellite. This was proposed as a alternative to the mechanically steered antenna, which has many disadvantages. The problem of tuning a large-scale OBFN is in many aspects similar to training a deep neural network. The tuning methodology developed in this thesis is based on the feedback that can be measured in real system. The deep learning approach is data driven, which tunes OBFNs from a given set of training examples. This is essential for online tuning in the future research. The deep learning approach is proven to work well for tuning large-scale OBFNs, e.g., 8x1, 16x1, and 32x1 binary tree structured OBFNs for any desired delays.