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Improving Physical Layer Security for Cellular Users in Threat of Eavesdroppers

Rahimi, Mahdi | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53267 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Aref, Mohammad Reza
  7. Abstract:
  8. Security for wireless communication networks has become a crucial issue because of the broadcast nature of wireless channels. On every cellular network Since it is possible for passive nodes to easily overhear the confidential information transmitted between transmitter and receiver, it is necessary to provide secure communication.When a cellular user wants to communicate with a node or base station on the cellular network, it expects that information will be transmitted and received without interference, with enough quality and without eavesdropping. On the other hand In practical scenarios, we know that cellular network nodes are not equipped with ideal equipment and some percent of transmitted power can’t be used to detect information by the receiver and is wasted or sometimes it’s considered as disturbance signal in detection. Another issue is unwanted interference on the cellular network which can interrupt communication between the receivers and transmitters. The main goal is to establish a secure and reliable communication with consideration of cellular network challenges. In this thesis we will consider D2D pairs as protection of communication on cellular network, first D2D pairs will be introduced and their advantages and disadvantages will be clarified, then secure and perfect communication can be achieved with aid of stochastic geometry and consideration of D2D’s advantages and non-ideality of receivers. We expand this result in multi users which can move around the communication network randomly and finally we introduce machine learning and artificial neural networks. Then we apply them to achieve physical layer secrecy for cellular user by selection of the best groups of jammers on the cellular network
  9. Keywords:
  10. Stochastic Geometry ; Artificial Neural Network ; Machine Learning ; Physical Layer Security ; Device to Device Communications ; Cellular Network ; Improving Security

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