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  • Space Systems Operations (SSO) Thesis Research Guide This guide has been prepared for SSO students to assist them in the research and writing of their master’s thesis. It outlines the specific steps necessary to successfully complete a thesis of high quality. It has been written specifically for you, the student. See the SSO Thesis Guide link under the Thesis Documents and Topics section.

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Satellite communications

  • Masters Thesis
  • Mazahri, Mohammad R.
  • Mishra, Nirmal K.
  • Bavarian, Behzad
  • Engineering
  • California State University, Northridge
  • Dissertations, Academic -- CSUN -- Engineering.
  • 2017-02-17T18:33:14Z
  • http://hdl.handle.net/10211.3/186832
  • by Mohammad R. Mazahri
  • Includes bibliographical references (leaves 203-204)
  • California State University, Northridge. Department of Engineering.

California State University, Northridge

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

LSU Scholarly Repository

Home > GRADSCHOOL > GRADSCHOOL_THESES > 5615

LSU Master's Theses

Neural networks for interference mitigation in satellite communication systems.

Martha E. Cash , Louisiana State University and Agricultural and Mechanical College Follow

Semester of Graduation

Master of Electrical Engineering (MEE)

Electrical and Computer Engineering

Document Type

The objective of this thesis is to utilize the power of machine learning to develop a neural network-aided receiver in a DVBS-2X satellite communication system to improve the downlink transmission quality in the presence of interference. An emphasis is placed on mitigating the effects caused by non-linear distortions, carrier frequency offset, and additive white Gaussian noise. This thesis proposes a feed-forward neural network with two hidden layers to compensate for the distortions in the received signal. The proposed system model uses 16-APSK modulation scheme. The neural network is tested under varying degrees of non-linear distortion, frequency offsets, and varying levels of noise power.

Digital pre-distortion and frequency offset correction are used to compare neural network performance. The experiments presented in this work are generated using Simulink, MATLAB, and TensorFlow. The results show that the neural network can outperform digital pre-distortion when only non-linear distortions are considered. The neural network can also compensate for carrier frequency offset when the offset is less severe. However, the proposed neural network struggles when severe frequency offsets are considered. Future work will investigate using neural network architectures with memory to better compensate for severe carrier frequency offset and other channel effects like fading.

Recommended Citation

Cash, Martha E., "Neural Networks for Interference Mitigation in Satellite Communication Systems" (2022). LSU Master's Theses . 5615. https://repository.lsu.edu/gradschool_theses/5615

Committee Chair

Shuangqing Wei

10.31390/gradschool_theses.5615

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