Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Area of Science:

• Wireless Communication
• Cognitive Radio Networks
• Algorithm Design

Background:

• Geographic mobility (GM) is critical for communication performance but often overlooked in cognitive radio (CR) algorithms.
• Existing CR decision-making algorithms lack in-depth analysis of geographic mobility.
• Optimizing channel selection in CR networks is essential for efficient spectrum utilization.

Purpose of the Study:

• To introduce a novel algorithm, DMAGM, for CR decision-making that incorporates geographic mobility analysis.
• To evaluate the performance of DMAGM in reducing latency and computational complexity.
• To propose and analyze an improved version, FDMAGM, with feedback for enhanced accuracy and stability.

Main Methods:

• Development of the decision-making algorithm with geographic mobility (DMAGM).
• Simulation of a CR network including base stations, primary users, and CRs with dynamic mobility.
• Comparative analysis of DMAGM against existing algorithms (ATDDiM, FAHP, AHP, Dijkstra) based on latency reduction and computational complexity.

Main Results:

• DMAGM significantly reduces decision-making latency by 12.77% to 94.27% compared to other algorithms.
• The algorithm demonstrates superior performance in reducing latency and computational complexity.
• The feedback-enhanced version (FDMAGM) shows improved accuracy and stability over time, despite potentially longer initial calculation.

Conclusions:

• DMAGM effectively enhances CR channel selection by integrating geographic mobility analysis, leading to reduced latency.
• FDMAGM offers continuous adaptation and improved reliability through feedback, crucial for critical communication scenarios.
• Both algorithms present practical performance improvements for CR networks, addressing limitations in prior research.