Capacity Performance Analysis And Optimization Of Cognitive WPCN Networks

With the rapid development of wireless communication technology and the increasing number of wireless applications,the spectrum and energy resources as the two most basic physical resources for wireless communication network become more and more valuable.Therefore,this paper combines spectrum sensing technology with energy harvesting technology in the cognitive wireless powered communication network(WPCN)and proposes a joint energy and spectrum harvesting scheme to maximize the utilization of these two resources and enhance spectrum efficiency as well as energy supply simultaneously.In order to study their interactions,the half-duplex cognitive hybrid access point(HAP)is first considered,which performs spectrum sensing,energy broadcasting,and information reception in sequence,and a joint optimization framework is proposed to obtain the optimized cognitive detection threshold and resource allocation.Noting that the joint optimization is a NP-hard problem,this paper decomposes the original problem and designs an efficient alternative algorithm to solve it.Next,to further enhance the spectrum efficiency,this paper consider the full-duplex cognitive HAP.It is found that the order in which users transmit data in this scenario shows a strong impact on their energy harvesting performance.Therefore,this paper designs the joint optimization with a novel dynamic user scheduling.In conventional cognitive WPCN,the energy transmission and data reception are performed by a fixed base station.However,the existing energy near-far effect may cause severe imbalance in performance between energy collectors at different locations.Considering the flexible mobility of drones,it can increase coverage,deploy quickly and efficiently to support cellular networks and improve their quality of service.Hence,in the new generation of cognitive WPCN,this paper considers replacing traditional fixed base stations with drone-based base stations,fully utilizes the moving characteristics of the drone and extends the original problem to the drone-based base station scenario,in which a new drone trajectory is designed,and an alternative algorithm integrating all the stop points is proposed.Through simulations,it is found that for half-duplex and full-duplex cognitive HAPs,as well as drone-based base station scenarios,the proposed joint energy and spectrum harvesting optimization schemes work very well with guaranteed convergence,and the proposed algorithms achieve significantly increased capacity contrast to comparison schemes.