Mohammad Albaqer Hammid Jwaid Al Ghezi


Presentation: 9:40 a.m.-10:00 a.m., Kennedy Union 311



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Free space optical communication plays a role in daily communications and has the advantage that it does not need a huge infrastructure of cables. Due to that advantage it can be used to deliver the internet to urban as well as remote locations, in the communication with drones, etc. However, the optical signal propagating through the atmosphere gets distorted due to fluctuations in weather parameters such as temperature and wind speed, resulting in optical turbulence, which impacts the strength of the optical signal that is received. In our work, we will use a deep learning algorithm to predict when these distortions could happen based on optical turbulence and weather data. Deep learning algorithms will be trained on the weather data as an input and the intensity of the signal as an output. Knowing the potential fading in the signal can help us to prevent losing the connection with the receiver. For instance, if we control a drone with an optical communication channel then it is important to know the potential fading in the signal, since this can be helpful for the controller to take action to prevent losing the connection with the drone.

Publication Date


Project Designation

Graduate Research

Primary Advisor

Miranda van Iersel

Primary Advisor's Department

Electro-Optics and Photonics


Stander Symposium project, School of Engineering

United Nations Sustainable Development Goals

Industry, Innovation, and Infrastructure

Predicting Fading in Free Space Communication Channel Using Deep Learning