UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design (1802.03906v1)

Abstract: With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.

• The paper presents a novel UAV-enabled MEC framework that jointly optimizes offloading and trajectory design to reduce energy use.
• The paper employs sequential convex optimization to efficiently solve the non-convex power minimization problem under energy constraints.
• The paper’s simulation results show significant improvements over benchmark trajectories, offering insights for low-latency mobile applications.

UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

The paper "UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design" explores the domain of mobile edge computing (MEC) facilitated by unmanned aerial vehicles (UAVs), emphasizing the optimization of computation offloading and UAV trajectory design to minimize energy consumption. Given the rapid development of the Internet of Things (IoT), mobile applications such as augmented reality and mobile gaming demand high computational power coupled with low latency, which challenges energy-limited devices. The authors propose a novel system where UAV-enabled wireless power transfer (WPT) addresses these challenges by providing short-distance line-of-sight communication that enhances both energy transfer efficiency and computation task execution.

Technical Contributions

The paper presents a comprehensive paper of the interaction between UAV-enabled WPT and MEC systems. It formulates a power minimization problem constrained by computation tasks and energy harvesting causality, describing it as non-convex and computationally intensive. To solve this, the authors leverage sequential convex optimization techniques to develop an alternative optimization algorithm, which iteratively refines resource allocation and UAV trajectory to achieve convergence efficiently.

Simulation results presented in the paper demonstrate that the proposed design surpasses other schemes under various metrics and conditions. Specifically, the UAV's trajectory optimization accounts for energy transfer efficiency by adjusting its flight path to reduce distance from ground users without compromising mission goals. This adjustment leads to significant improvements in power consumption when compared to straight or semi-circle trajectories used as benchmarks.

Implications and Future Directions

The implications of utilizing UAVs in MEC systems are multidimensional:

1. Operational Efficiency: Enhancement in data processing capabilities at the network edge improves end-user experience for latency-sensitive applications.
2. Energy Efficiency: UAV-enabled WPT systems introduce renewable and sustainable energy solutions for mobile devices, promising prolonged operational time without frequent charging requirements.
3. Trajectory Optimization: Strategic UAV deployments tailored to computation task requirements and user distribution pave the way for more dynamic and reactive edge computing infrastructures.

In terms of future developments, the paper opens avenues for research geared towards more complex UAV propulsion energy models that account for additional variables such as UAV acceleration. Furthermore, integration of machine learning techniques to predict user behavior and optimize computational offloading more efficiently is anticipated to refine these UAV-enabled MEC systems further. The advancement in algorithmic efficiency and adaptability paves the path toward scalable solutions capable of serving increasingly demanding applications and users in dynamic environments.

In summary, this paper contributes to the niche yet vital research area of UAV-enabled MEC systems, offering practical solutions and laying the groundwork for future technological expansions that could reshape the landscape of mobile computing and wireless communications.