Wireless Network Coding with Intelligent Reflecting Surfaces

  • Amanat Kafizov

Student thesis: Master's Thesis

Abstract

Conventional wireless techniques are becoming inadequate for beyond fifth-generation (5G) networks due to latency and bandwidth considerations. To increase the wireless network throughput and improve wireless communication systems’ error performance, we propose physical layer network coding (PNC) in an Intelligent Reflecting Surface (IRS)-assisted environment. We consider an IRS-aided butterfly network, where we propose an algorithm for obtaining the optimal IRS phases. Also, analytic expressions for the bit error rate (BER) are derived. The numerical results demonstrate that the scheme proposed in this thesis significantly enhances the BER performance. The proposed scheme is compared to traditional network coding without IRS. For instance, at a target BER of 10−3, 28 dB and 0.75 dB signal to noise ratio (SNR) gains are achieved at the relay and destination node of the 32-element IRS-assisted butterfly network model.
Date of AwardApr 2021
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Science and Engineering
SupervisorMohamed-Slim Alouini (Supervisor)

Keywords

  • network coding
  • butterfly network
  • performance analysis
  • Intelligent reflecting surfaces

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