Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is studied in this paper. The computation rate maximization problems in a UAVenabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV’s speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are respectively proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A 1-D search algorithm is proposed to solve these problems based on S-Procedure under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

Mobile Edge Computing (MEC) is a promising technology in the next generation network, which provides computing services for user equipments (UEs) to reduce the task delay and prolong the usage time of UEs. To address the deficiency of poor channel quality caused by multipath and blockages in traditional MEC networks, a multiple input single output (MISO) UAV-assisted MEC network is studied. A system energy consumption minimization problem is formulated by jointly optimizing the the UAV’s beamforming vectors, the UAV’s central processing unit (CPU) frequency, the UAV’s trajectory, the UEs’ transmission power and the UEs’ CPU frequency subject to the constraints on the task, the UAV’s trajectory, and the UEs’ computation tasks. A three-stage iterative algorithm is proposed to solve the challenging non-convex problem. The closed-form expressions for the optimal UAV CPU frequency and the transmission power of UEs are derived. Simulation results show that the proposed algorithm is superior to the benchmark schemes in terms of energy consumption, and the convergence performance is guaranteed.