Fairness guarantees in a neural network adaptive congestion control framework

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Existing neural network rate control (NNRC) frameworks manage queueing delay and length.
• Fair allocation of network resources among competing sources remains a challenge.

Purpose of the Study:

• To expand the NNRC framework for guaranteed fair resource allocation.
• To introduce a novel algorithm for stable and adaptive control of communication channels per source.

Main Methods:

• Extension of the neural network rate control (NNRC) framework.
• Development of a novel adaptive algorithm for managing communication channels.
• Simulation studies on heterogeneous delay, long-distance, high-speed networks.

Main Results:

• The enhanced NNRC framework successfully guarantees fair resource allocation.
• The novel algorithm provides stable and adaptive control over communication channels.
• Simulations validated the methodology in complex network environments.

Conclusions:

• The expanded NNRC framework effectively addresses fair resource allocation.
• The adaptive channel control algorithm is a significant advancement in network resource management.
• The developed methodology is robust and applicable to high-speed networks.