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Online machine learning algorithms to optimize performances of complex wireless communication systems.

Koji Oshima1,2, Daisuke Yamamoto2, Atsuhiro Yumoto2

  • 1Innovation Design Initiative, National Institute of Information and Communications Technology, Koganei, Tokyo, Japan.

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This study introduces machine learning for optimizing complex wireless communication systems. Two novel schemes, supervised learning and reinforcement learning, demonstrate effectiveness in real-world experiments.

Keywords:
cognitive radiocomplex systemscross layer optimizationmachine learningmulti-armed bandit problemoptimization algorithmreinforcement learningwireless communication systems

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Modern wireless communication systems require advanced optimization techniques.
  • Data-driven and feedback-based approaches are crucial for enhancing system performance.
  • Machine learning offers potential solutions for complex system optimization.

Purpose of the Study:

  • To develop a comprehensive framework for optimizing wireless communication systems.
  • To propose and investigate two novel optimal decision-making schemes.
  • To address limitations in existing research on wireless system optimization.

Main Methods:

  • Implementation of a supervised learning model for optimal decision-making.
  • Development of a simple and implementable reinforcement learning algorithm.
  • Verification of proposed schemes through real-world experiments and computer simulations.

Main Results:

  • The proposed supervised learning scheme provides effective optimization.
  • The reinforcement learning algorithm offers a practical approach to system optimization.
  • Experimental and simulation results validate the necessity and effectiveness of the research.

Conclusions:

  • Machine learning-driven approaches are vital for optimizing complex wireless networks.
  • The presented supervised and reinforcement learning schemes offer significant advancements.
  • This research validates the practical applicability and benefits of advanced ML techniques in wireless communications.