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 wide variety of works have been conducted in vehicle-to-everything (V2X) communications to enable a variety of applications for road safety, traffic efficiency and passenger infotainment. Although dedicated short-range communications (DSRC) based V2X is already in the deployment phase, cellular based V2X is gaining more interest in academia and industry most recently. This paper surveys the existing work and challenges on LTE and 5G to support efficient V2X communications. First, we present the motivations for cellular based V2X communications. Second, we summarize the LTE V2X architecture and operating scenarios being considered. Third, we discuss the challenges in existing LTE for supporting V2X communications such as physical layer structure, synchronization, multimedia broadcast multicast services (MBMS), resource allocation, security and survey the recent solutions to these challenges. We further discuss the challenges and possible solutions for 5G based vehicular communications. Finally, we discuss the open research issues and possible research directions in cellular based vehicular communications.

This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicle networks. More- over, the resource allocation policy is designed by considering the vehicle mobility and the hard service deadline constraint. These critical challenges have often been either neglected completely or addressed inadequately in the existing work on the vehicular networks because of their high complexity. We develop the deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper. Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained.

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.