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Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels.

Ricardo Coelho Ferreira1, Michelle S P Facina1, Felipe A P de Figueiredo2

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Summary

Large intelligent surfaces (LIS) enhance wireless communication by improving signal quality and efficiency. This study models LIS channels using Gamma distribution, deriving accurate bit error probability expressions for better system performance.

Keywords:
Rayleigh fadingVon Mises distributionlarge intelligent surfacesmassive MIMO systemsmaximum ratio transmission

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Area of Science:

  • Wireless Communication Engineering
  • Information Theory
  • Signal Processing

Background:

  • Large intelligent surfaces (LIS) offer potential improvements in signal-to-noise ratio, spectral efficiency, and energy consumption.
  • Existing models often assume perfect phase correction in LIS, which is not always practical.

Purpose of the Study:

  • To model the complex fading channel of a system employing LIS with non-perfect phase correction.
  • To derive accurate performance metrics, specifically bit error probability, for such systems.
  • To analyze the impact of phase errors on system performance.

Main Methods:

  • Utilizing the Central Limit Theorem (CLT) to establish an equivalent Gamma fading channel model.
  • Deriving closed-form expressions for bit error probability and upper bounds.
  • Employing Monte Carlo simulations to validate analytical approximations under phase errors.

Main Results:

  • The overall channel, considering fading and non-perfect phase correction, can be accurately modeled as a Gamma distribution.
  • Accurate closed-form expressions for bit error probability and tight upper bounds were derived.
  • The study provides a method to analyze system performance even with imperfect phase correction at the LIS.

Conclusions:

  • The Gamma distribution provides a robust model for LIS channels with phase errors.
  • The derived analytical expressions offer valuable tools for performance evaluation and system design.
  • The research addresses the practical challenge of non-ideal phase correction in LIS systems.