Research On Power Control Method Of Cognitive Radio Network Based On Deep Learning

With the continuous development of informationize process,wireless communication technology is more and more widely used in modern society.Emerging services,growing user scale and rapidly increasing number of devices put forward higher requirements for network capacity and resource utilization efficiency of wireless communication system.Cognitive radio(CR)technology can realize the sharing of wireless spectrum resources and the coordination of multi-user interference through the intelligent perception of wireless communication environment.It has great potential to improve the utilization of communication resources and enhance the network capacity.Power control is the carrier of spectrum utilization in the physical layer of cognitive radio networks(CRNs).It is of great value to suppress interference,improve spectrum efficiency and improve network capacity.The power control problem in CRNs is usually modeled as a non-convex optimization problem,which is difficult to solve.The traditional solution methods generally rely on the mathematical methods such as fractional programming,branch and bound(BB),semi-definite programming relaxation to develop the solution algorithm,which has high dependence on the expert knowledge in the field of wireless communication and mathematics.The implementation process is complex and the computational complexity is high.In recent years,deep learning(DL)technology has achieved a breakthrough in computer vision,natural language processing,speech recognition and other fields.The data-driven nature of DL makes it unnecessary for artificial feature engineering and domain expert knowledge.These advantages are also valued in the field of wireless communication,and are considered as the technical basis of intelligent radio in the future 6G mobile communication system.In this context,aiming at the problem of power control in CRNs,this paper studies an intelligent solution method based on DL.The main research and technical contributions of this paper mainly include the following three aspects:(1)Aiming at the optimization of sum rate maximization power control problem in cognitive radio interference channel network,a BB based algorithm is researched(SR-BB),which can effectively obtain the global optimal solution of the problem.It provides a performance reference for verifying the SR-DL.(2)Aiming at the optimization of sum rate maximization power control problem in cognitive radio interference channel network,a DL based scheme is researched(SR-DL),which can realize sum rate maximization under the rate and power constraints of all users.Due to the interference between users,the problem is non-convex,so it is difficult to solve.The DL method is used to solve the problem adaptively.Firstly,the barrier function method is used to add the rate constraint to the objective function,and then the unsupervised learning strategy is used to solve the problem of lack of training set(i.e.optimal power allocation).The simulation results show the effectiveness of the scheme.(3)Aiming at the optimization of global energy efficiency(GEE)maximization power control problem in cognitive radio interference channel network,a DL based scheme(GEE-DL)is researched.Although the SR-DL scheme can achieve relatively high sum rate,but the energy efficiency(EE)will be very low,so we research the GEE maximization problem under the rate and power constraints of all users.The scheme is the same as SR-DL,i.e.,we choose barrier function method and unsupervised learning strategy.The simulation results show the effectiveness of the scheme.