Primary User Localization and Its Error Analysis in 5G Cognitive Radio Networks

Area of Science:

• Wireless communication
• Signal processing
• Network engineering

Background:

• Accurate primary user (PU) localization is essential for cognitive radio networks (CRNs).
• Existing methods often assume omnidirectional communication, limiting accuracy in non-cooperative CRN environments.
• Received Signal Strength (RSS)-based techniques like Centroid Localization (CL) and Multidimensional Scaling (MDS) are suitable but have limitations.

Purpose of the Study:

• To propose a novel PU localization method for CRNs utilizing directional antennas and RSS measurements.
• To enhance localization accuracy by employing a sector-based scoring strategy for Received Signal Strength (RSS).
• To evaluate the proposed method's robustness and performance against conventional techniques.

Main Methods:

• Developed a PU localization technique using RSS values from different sectors of a secondary user's (SU) antenna.
• Implemented a scoring strategy across antenna sectors to estimate PU location.
• Proposed and evaluated two distinct scoring functions for localization.

Main Results:

• The proposed method demonstrates robustness to varying PU locations and channel conditions.
• Localization accuracy improves with an increased number of SUs due to more measurements.
• Accuracy degrades with wider SU sector beamwidths, as distant grid points contribute more.
• The method significantly outperforms conventional Centroid Localization (CL).

Conclusions:

• The proposed sector-based RSS localization method offers a significant performance improvement for PU localization in CRNs.
• The approach effectively addresses the challenge of non-cooperative PUs by leveraging directional antenna information.
• Further research can explore optimal scoring functions and network parameter tuning for enhanced accuracy.