Dynamic channel allocation for secondary users in cognitive radio network.

Area of Science:

• Wireless Communication
• Network Engineering
• Artificial Intelligence

Background:

• Cognitive Radio Networks (CRNs) address spectral resource shortages by enabling Secondary Users (SUs) to opportunistically access underutilized frequency bands.
• Ensuring the integrity of Primary Users (PUs) is crucial for effective spectrum sharing in CRNs.
• Traditional CRN paradigms face challenges in dynamic spectrum management.

Purpose of the Study:

• To develop an independent channel assignment model for CRNs using Mamdani fuzzy logic.
• To optimize spectrum utilization and network performance by coordinating channel selection based on network conditions.
• To enhance the adaptive responsiveness of CRNs to dynamic network environments.

Main Methods:

• Implementation of a 27-rule Mamdani fuzzy logic decision model for channel assignment.
• Utilizing an energy-detection approach for spectrum sensing to identify idle channels.
• Employing a distributed decision-making framework at individual SU units for adaptive channel selection.

Main Results:

• The fuzzy-based allocation system demonstrated significant improvements in network throughput.
• A notable reduction in service delay and packet drop rate was observed.
• Increased spectrum utilization was achieved compared to traditional CRN approaches.

Conclusions:

• Fuzzy logic-based channel assignment effectively enhances CRN performance.
• The proposed distributed system offers adaptive spectrum management capabilities.
• Cognitive intelligence combined with fuzzy decision-making is vital for future IoT communications.