Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios.

Area of Science:

• Telecommunications Engineering
• Wireless Network Optimization
• 5G Network Performance

Background:

• User Equipment (UE) location data enhances service customization and network resource management.
• 5G technology introduces new features that, combined with location-awareness, can improve network performance.
• Current 3GPP dual connectivity (DC) node selection relies solely on received power, potentially limiting network optimization.

Purpose of the Study:

• To propose an alternative methodology for selecting nodes in dual connectivity (DC) systems.
• To enhance network performance and user Quality of Experience (QoE) by optimizing node selection.
• To leverage User Equipment (UE) location and network metrics for a more effective selection strategy.

Main Methods:

• Developed a system that utilizes User Equipment (UE) location and multiple network metrics for node selection.
• Implemented a Quality of Experience (QoE)-based node selection strategy as an alternative to power-based methods.
• Conducted system-level simulations in a dense urban scenario to evaluate the proposed solution.

Main Results:

• The optimal node selection strategy is dependent on the User Equipment (UE) location.
• The proposed QoE-based selection method demonstrates an increase in perceived user Quality of Experience (QoE).
• Results highlight the variability of optimal selection based on UE position and service type.

Conclusions:

• Location-aware, QoE-based node selection offers significant advantages over traditional power-based methods in 5G DC.
• The proposed approach effectively enhances user Quality of Experience (QoE) by considering diverse network conditions.
• This methodology provides a foundation for more intelligent and user-centric network resource management in future wireless systems.