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

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.