Optimal energy efficiency resource allocation strategy for cognitive clustering network under PUEA attack

Abstract

5G has pushed the use of radio spectrum to a new level, and cognitive clustering network can effectively improve the utilization of radio spectrum, which is a feasible way to solve the growing demand for wireless communications. However, cognitive clustering network is vulnerable to PUEA attack, which will lead to the degradation of system detection performance, thereby reducing the energy efficiency. Aiming at these problems, this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack. A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance. We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem, which is solved by Lagrangian function and Karush-Kuhn-Tucker condition. Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed. Simulation results indicate that proposed method is effective when subjected to PUEA attacks, and the impact of different parameters on energy efficiency is analyzed.