Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement

Area of Science:

• Wireless Communications
• Signal Processing
• Optimization Theory

Background:

• Linear cooperative spectrum sensing requires optimal weights and thresholds for secondary users.
• Balancing missed detection probability and network throughput presents a multi-objective optimization challenge.

Purpose of the Study:

• To develop a method for obtaining evenly distributed Pareto solutions in linear cooperative spectrum sensing.
• To address limitations of the normal constraint (NC) method, including lack of solution methods and guidance on the number of Pareto solutions.

Main Methods:

• The normal constraint (NC) method is adapted to transform the multi-objective problem into single-objective optimization (SOO) problems.
• A stochastic global optimization algorithm is employed to solve the SOO problems.
• A method is proposed to determine the optimal number of Pareto solutions under computational constraints.

Main Results:

• The proposed methods effectively solve the SOO problems generated by the NC method.
• A technique is introduced to determine the optimal number of Pareto solutions within complexity limits.
• Extended NC refines Pareto solutions and allows selection of preferred solutions.

Conclusions:

• The developed techniques enhance cooperative spectrum sensing by providing a set of optimal trade-off solutions.
• The approach offers a practical framework for spectrum management in dynamic wireless environments.
• Computer simulations validate the effectiveness and efficiency of the proposed optimization methods.