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Artificial Noise Injection and Its Power Loading Methods for Secure Space-Time Line Coded Systems.

Jingon Joung1, Jihoon Choi2, Bang Chul Jung3

  • 1School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial noise (AN) injection method to enhance the security of space-time line coded (STLC) systems. The novel AN design and power control significantly boost secrecy rates, especially when eavesdropper channels are weaker.

Keywords:
artificial noisephysical layer securitypower controlsecrecy ratespace-time line code

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Area of Science:

  • Wireless communication security
  • Information theory
  • Signal processing

Background:

  • Space-time line coded (STLC) systems are susceptible to eavesdropping by illegitimate receivers.
  • Improving the secrecy rate in STLC systems is crucial for secure data transmission.
  • Existing methods may not fully exploit the STLC structure for enhanced security.

Purpose of the Study:

  • To propose a novel artificial noise (AN) injection method for 2x2 STLC systems.
  • To enhance the secrecy rate of STLC systems against eavesdroppers.
  • To derive the optimal power loading factor for the designed AN.

Main Methods:

  • Design of a novel AN tailored to the STLC structure.
  • Derivation of the optimal power loading factor for AN.
  • Performance evaluation through numerical simulations.

Main Results:

  • The proposed AN injection method significantly improves the secrecy rate of STLC systems.
  • The optimal power loading factor enhances the effectiveness of the AN.
  • The method shows greater efficacy when there is a substantial difference between main and eavesdropper channel gains.

Conclusions:

  • The developed AN injection technique effectively secures STLC systems.
  • The proposed method offers a significant improvement in secrecy rate compared to conventional STLC systems.
  • Exploiting channel gain differences is key to maximizing security benefits.