A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor...

Area of Science:

• Wireless communication
• Cognitive radio networks
• Sensor networks

Background:

• Cooperative spectrum sensing in cognitive sensor networks (CSNs) faces challenges with large numbers of cognitive nodes (CNs), leading to wasted sensing time and energy.
• Efficient energy management is crucial for sustained operation and effective spectrum utilization in CSNs.

Purpose of the Study:

• To propose a novel wireless power transfer (WPT)-based weighted clustering cooperative spectrum sensing model for CSNs.
• To maximize the spectrum access probability of CSNs by optimizing sensing time and clustering number.

Main Methods:

• A WPT-based weighted clustering model is proposed, dividing CNs into clusters with selected cluster heads.
• Common CNs transfer radio frequency (RF) energy from the primary node (PN) to cluster heads for power.
• Joint resource optimization is formulated to allocate sensing time and determine the clustering number.

Main Results:

• The proposed model enables cluster heads to achieve higher transmission power compared to traditional models.
• Simulation results demonstrate the existence of optimal sensing time and clustering number for maximizing spectrum access probability.
• The model effectively addresses energy wastage challenges in large-scale cooperative spectrum sensing.

Conclusions:

• The WPT-based weighted clustering model offers a viable solution for improving cooperative spectrum sensing efficiency in CSNs.
• Optimized resource allocation (sensing time and clustering) is key to enhancing spectrum access probability.
• This approach contributes to more sustainable and effective utilization of the cognitive radio spectrum.