In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the p...In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.展开更多
文摘In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.
