Research On Multi-objective Optimization Mechanism In Cognitive Radio Networks

Nowadays,with the rapid development of wireless communications business,the spectrum resources become increasingly scarce,but many of the available spectrum resources are not being fully utilized.Cognitive radio technology is used as an effective solution in practical applications and the study.Cognitive radio technology allows unauthorized users to share spectrum with authorized users by access channel opportunistically and concurrently,thereby increasing the utilization of frequency,at the same time,making the network architecture extremely complex.In the cognitive radio network,the secondary user can concurrently transmission data,and the interference constraint is used to ensure that the interference of the receiver of the primary user is lower than the interference temperature threshold.In order to satisfy the requirements of the secondary user,a number of quality of service targets are required to achieve the optimal state at the same time.Therefore,joint optimization problem of the multi-objective is the key problem of cognitive wireless network.The main contents are as follows:1.According to the problem of all users can access the same channel to communicate,two multi-objective optimization schemes of jointing power control are proposed.When the system is infeasible,the access control and power control are jointly optimized.As admission control is the NP problem,the particle swarm optimization algorithm based on linear programming is proposed and designed.At the same time,the convergence of the proposed algorithm is analyzed and proved.When the system is feasible,the rate and power control are jointly optimized,and the improved Lagrange algorithm is used to solve this joint optimization problem effectively.The numerical results show that the time complexity of proposed algorithm is slightly increased,but can be quickly converged due to the addition of the feasibility analysis.The number of admitted uses can be effectively improves while reducing power consumption;In addition,the proposed algorithm can allocate transmission rate more fairly.2.Due to the fact that it is difficult for cognitive users to estimate the channel accurately,the imperfect channel model is considered,and theL₀-norm minimization is used to solve the problem.Furthermore,the L₀-minimization problem is a non-convex problem,and a multi-objective optimization scheme based on the smooth approximation of entropy function is proposed.Finally,the problem is transformed into the optimal value of the Lagrange function,using the Armijo gradient descent method to solve the multi-objective optimization problem.Numerical results showed that the number of admitted uses of the proposed algorithm does not obviously improve when SINR is relatively low,but the proposed algorithm can reduce the transmission power consumption and improve the number of admitted uses when SINR is relatively high.