Multiagent Q-Learning-Based Mobility Management for Multi-Connectivity in mmWAVE Cellular Systems.

Area of Science:

• Wireless communication systems
• Mobile network management
• Machine learning applications in telecommunications

Background:

• Millimeter wave (mmWave) cellular systems face challenges with path loss and blockages, necessitating advanced mobility management.
• Massive multiple-input-multiple-output (MIMO) systems are crucial for mmWave but increase susceptibility to link failures.
• Multi-connectivity is essential for meeting high capacity and reliability demands in next-generation cellular networks.

Purpose of the Study:

• To propose a novel multiagent distributed Q learning-based mobility management scheme.
• To enhance multi-connectivity in mmWave cellular systems.
• To address model complexity and accelerate learning through a hierarchical structure.

Main Methods:

• Development of a hierarchical multiagent distributed Q learning algorithm.
• Simulation using a realistic urban measurement dataset from Wireless Insite.
• Performance comparison against independent Q learning and a heuristic scheme.

Main Results:

• The proposed scheme demonstrates improved performance in terms of handover probability.
• Enhanced spectral efficiency is observed compared to baseline methods.
• The hierarchical structure effectively manages complexity and speeds up the learning process.

Conclusions:

• The multiagent distributed Q learning approach offers an effective solution for mobility management in mmWave multi-connectivity.
• Hierarchical structuring is beneficial for complex learning tasks in cellular networks.
• The proposed scheme provides a viable strategy for reliable and efficient mmWave communication.