Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning.

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Large-scale wireless networks face inevitable congestion due to increased nodes and applications.
• Congestion leads to inefficient network capacity utilization and performance degradation.
• Existing management frameworks struggle with the complexity of modern wireless systems.

Purpose of the Study:

• To propose an autonomous and adaptive wireless network management framework using multi-agent deep reinforcement learning (MADRL).
• To optimize network efficiency and fairness by incorporating application-specific requirements and node-level objectives.
• To demonstrate the performance benefits of a MADRL approach over traditional single-agent methods.

Main Methods:

• Utilizing a multi-agent deep reinforcement learning architecture for network management.
• Developing a novel reward function that integrates application awareness and fairness metrics.
• Conducting experimental evaluations to compare the proposed framework against a single-agent approach.

Main Results:

• The proposed MADRL framework achieves optimized, application-specific performance and enhances network fairness.
• Significant improvements were observed in adaptive data rate and network responsiveness compared to single-agent systems.
• Qualitative benefits include network size independence, node-led priorities, and a reduced search space.

Conclusions:

• MADRL offers a powerful solution for managing complex, large-scale wireless networks autonomously.
• The application-aware and fairness-focused reward function is crucial for achieving balanced network objectives.
• The multi-agent approach provides superior adaptability and efficiency in dynamic wireless environments.