Dynamic Spectrum Allocation Using Multi-Source Context Information in OpenRAN Networks.

Area of Science:

• Telecommunications Engineering
• Computer Science
• Network Optimization

Background:

• Limited radio resources and inefficient spectrum utilization pose significant challenges in modern wireless networks.
• Existing methods often rely solely on radio-related information, limiting dynamic resource allocation.
• The need for intelligent systems that can predict and adapt to varying network traffic demands is critical.

Purpose of the Study:

• To propose a novel multi-source mechanism for dynamic adjustment of occupied frequency bands.
• To enhance spectral efficiency by leveraging diverse contextual information sources beyond traditional radio data.
• To develop an algorithm for intelligent spectrum resource management in open radio access networks.

Main Methods:

• Development of an algorithm integrating contextual information from various sources (e.g., city monitoring) using fuzzy and soft connections.
• Simulation of a system that dynamically adjusts spectrum allocation based on predicted user traffic.
• Evaluation of the trade-off between spectrum usage reduction and user bitrate under the proposed mechanism.

Main Results:

• Demonstrated ability to reduce the spectrum used by leveraging multi-source contextual data.
• Achieved increased spectral efficiency with only a slight, simultaneous reduction in user bitrate.
• Validated the effectiveness of the proposed mechanism in optimizing radio resource allocation.

Conclusions:

• The proposed multi-source mechanism effectively enhances spectral efficiency by intelligently managing radio resources.
• Contextual information integration offers a viable solution for dynamic spectrum adjustment in wireless networks.
• The approach aligns with the open radio access network concept, allowing for flexible information merging policies.