Abstract
Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.
Highlights
The fast proliferation in the Internet-of-Things (IoT) applications has fuelled an exponential growth of IoT devices, including smartphones, wearable devices and wireless sensors, which are widely deployed to support diverse smart applications [1]
We provide numerical results to validate the performance of all presented algorithms
We assume that the unmanned aerial vehicle (UAV)-carried wireless-powered Mobile edge computing (MEC) network has K = 4 IoT devices, whose positions are (10, 10), (0, 10), (10, 0) and (0, 0), respectively
Summary
The fast proliferation in the Internet-of-Things (IoT) applications has fuelled an exponential growth of IoT devices, including smartphones, wearable devices and wireless sensors, which are widely deployed to support diverse smart applications (e.g., smart cities, automatic manufacturing and smart homes) [1]. Many of these intelligent applications, such as augmented reality and autonomous navigation, are computationally-intensive and latency-sensitive, which are extremely difficult for IoT devices to handle due to their limited computing capacity. In the binary offloading pattern, the entire computation missions are accomplished either at the IoT devices or at the nearby MEC servers [2], [3]
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