Opportunistic Capacity-Based Resource Allocation for Chunk-Based Multi-Carrier Cognitive Radio Sensor Networks

Area of Science:

• Electrical Engineering
• Computer Science
• Wireless Communications

Background:

• Spectrum allocation in cognitive radio sensor networks (CRSNs) traditionally assumes static environments.
• Real-world spectrum availability is dynamic due to primary user (PU) and secondary user (SU) activity and mobility.
• This dynamism necessitates adaptive resource allocation strategies.

Purpose of the Study:

• To investigate resource allocation for chunk-based multi-carrier CRSNs in time-varying spectrum environments.
• To develop a novel opportunistic capacity model for accurately describing dynamic spectrum resources.
• To propose a joint power and chunk allocation model that leverages available spectrum capacity.

Main Methods:

• A continuous time semi-Markov chain (CTSMC) was employed to model time-varied spectrum resources.
• A joint power and chunk allocation model was formulated considering opportunistic capacity.
• The complex allocation problem was decomposed into two sub-problems and solved using the Lagrangian dual method.

Main Results:

• The proposed opportunistic capacity model effectively captures the dynamics of spectrum resources.
• The joint power and chunk allocation strategy optimizes resource utilization in fluctuating spectrum conditions.
• Simulation results demonstrate superior performance compared to conventional algorithms.

Conclusions:

• The developed opportunistic capacity-based resource allocation algorithm is effective for CRSNs with time-varying spectrum.
• This approach provides a significant performance improvement over traditional methods in dynamic environments.
• The findings are crucial for efficient spectrum management in future wireless networks.