Robust and Fair Multi-Objective Power Allocation Problem Based on Efficient and Healthy Cognitive Radio

Abstract

Cognitive radio networks (CRNs) is a technology that can alleviate the scarcity of radio resources, improve communication efficiency, and reduce electromagnetic radiation pollution. However, traditional research mostly concentrates on a single optimization function, which is too constrained to achieve global consideration. We suggest a multi-objective optimization problem (MOP) with the objectives of transmission rate and power efficiency. Then, we introduce a fairness factor with the minimum protection rate to ensure the quality of data transfer for each secondary user(SU). We use the ellipsoid set to characterize the uncertain parameters under the actual channel state information (CSI). In the worst case, the semi-infinite programming (SIP) problem is transformed into a second-order cone programming (SOCP) problem. The original problem is linearly combined using the weighted-sum method to construct a single objective problem (SOP), which is then turned into a solvable convex optimization problem and resolved using the Lagrange dual algorithm and sub-gradient method. The simulation results demonstrate the ability of our proposed algorithm to balance power and transmission rate optimization by adjusting the weighting values, while maintaining good robustness.

Keywords

• cognitive radio networks (crns)
• multi-objective optimization
• robust power allocation
• fairness factor
• rate constraint
• weighting coefficient