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Neural network applications for jamming state information generator.

H M Kwon1, L T Schaefer

  • 1Lockheed Eng. and Sci. Co., Houston, TX.

IEEE Transactions on Neural Networks
|January 1, 1994
PubMed
Summary
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A new neural network jamming state information (JSI) generator improves performance for coded frequency-hopped M-ary frequency-shift-keying (FH/MFSK) systems. This approach outperforms traditional methods, even without knowing jamming details.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Information Theory

Background:

  • Coded frequency-hopped M-ary frequency-shift-keying (FH/MFSK) systems face performance degradation due to partial-band noise jamming.
  • Existing jamming state information (JSI) schemes, like the maximum a posteriori (MAP) rule, rely on total received energy and knowledge of the jamming fraction, potentially limiting their effectiveness.

Purpose of the Study:

  • To introduce a novel neural network-based approach for generating JSI in FH/MFSK systems.
  • To compare the performance of the neural network JSI generator against the conventional MAP-based JSI generator.
  • To enhance the robustness of the neural network JSI generator by enabling it to function effectively even when the jamming fraction is unknown.

Main Methods:

  • A neural network model was developed for JSI generation.

Related Experiment Videos

  • The performance of the neural network JSI generator was evaluated against the MAP-based JSI generator under known jamming fraction conditions.
  • The neural network model was generalized to handle unknown jamming fraction scenarios.

    • Main Results:

    • The neural network JSI generator demonstrated significantly better performance compared to the MAP JSI generator for coded FH/MFSK systems operating at high code rates.
    • The generalized neural network approach maintained superior performance even when the jamming fraction was unknown.

    Conclusions:

    • Neural network-based JSI generation offers a substantial performance improvement for coded FH/MFSK communication systems, particularly under high code rate conditions.
    • The proposed neural network method provides a more robust and effective solution for JSI generation compared to traditional energy-based methods, especially in scenarios with unknown jamming parameters.