A Deep Learning MIMO Communication System Based on Auto-encoder Design

Mirzaee, Ali | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53583 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hossein Khalaj, Babak
  7. Abstract:
  8. Today, the use of deep learning algorithms in the design of communication systems has received much attention. One of these areas is the partial or total design of these systems using deep networks. The overall design of a communication system using deep networks allows for global optimization and can provide better performance in cases where classical methods have suboptimal performance without significantly increasing the computational load. In this research, a comprehensive architecture for designing communication systems based on Auto-encoder neural networks is presented. This architecture has the same functionality as classical systems, considering all parts of these systems including transmitter, receiver, modulation, beamforming and channel as a matter of classification and optimization, and offers better performance than existing methods in multi-user MIMO mode
  9. Keywords:
  10. Deep Learning ; Neural Network ; Beamforming ; Communication System ; Cooperative Multiple Input Multiple Output (MIMO)System ; Autoencoder

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