Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization

Abstract

This paper studies a new mobile edge computing (MEC) setup where an unmanned aerial vehicle (UAV) is served by cellular ground base stations (GBSs) for computation offloading. The UAV flies between a give pair of initial and final locations, during which it needs to accomplish certain computation tasks by offloading them to some selected GBSs along its trajectory for parallel execution. Under this setup, we aim to minimize the UAV's mission completion time by optimizing its trajectory jointly with the computation offloading scheduling, subject to the maximum speed constraint of the UAV, and the computation capacity constraints at GBSs. The joint UAV trajectory and computation offloading optimization problem is, however, non-convex and thus difficult to be solved optimally.To tackle this problem, we propose an efficient algorithm to obtain a high-quality suboptimal solution. Numerical results show that the proposed design significantly reduces the UAV's mission completion time, as compared to benchmark schemes. I. INTRODUCTIONWith recent technology advancement and manufacturing cost reduction, unmanned aerial vehicles (UAVs) have received growing interests in various applications such as cargo delivery, filming, rescue and search, etc [1]. To maintain the UAVs' safe operation with real-time command/control and enable their new applications with artificial intelligence (AI), it becomes increasingly important to enhance the communication and computation capabilities of UAVs. In order to provide reliable communication for UAVs, cellular-connected UAV communication has recently emerged as a viable new solution, in which UAVs are integrated into cellular networks as new aerial mobile users [2], [3]. As compared to the conventional direct UAV-to-ground communication with limited range [1], the cellular-connected UAV communication is able to provide seamless wireless communication for UAVs. By contrast, there has been very limited work addressing how to improve the computation performance of UAVs. Notice that in the forthcoming AI era, UAVs need to handle computation-intensive and yet latency-critical tasks, while in practice they usually have limited computation resources on-broad due to their size, weight, and power (SWAP) limitations. Therefore, it is imminent as well as challenging to solve the open problem of how to significantly enhance the computation power for future UAVs.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

“…On the other hand, in practice, small UAVs may also have the need to offload the computation tasks to ground BSs in cellular-connected UAVs. By exploiting its LoS dominant links with many ground BSs, a UAV user can simultaneously connect with multiple GBSs to exploit their distributed computing resources to improve the computation offloading performance [150]. In [150], it has been shown that when the number of task-input bits is sufficiently large, the UAV should hover above its associated GBSs in order to achieve the most efficient computation offloading.…”

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

“…A. Obtaining g (λ, µ, ν, ω, η) by Solving Problem (25) For any given value of (λ, µ, ν, ω, η) in the feasible set of problem (D2), the dual function can be obtained by solving problem (25). Note the problem (25) can be decomposed into KN independent subproblems, and each one is further decomposed into several subproblems as follows.…”

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

“…The role of the UAV Operation modes Contributions [3] User with computation task Partial offloading Cost minimization [4] User with computation task Partial offloading Cost minimization [5] User with computation task Partial offloading Completion time minimization [6] MEC server executing computation Partial offloading Architecture establishment [7] MEC server executing computation Partial offloading Energy consumption minimization [8] MEC server executing computation Partial offloading Energy consumption minimization [9] MEC server executing computation Partial offloading and binary computation Computation bits maximization [10] Relay offloading computation task Partial offloading Offloading bits maximization are assumed to hover in the sky and their trajectories are not optimized. Different from the works in [3] and [4], the authors in [5] proposed a resource allocation strategy that jointly optimizes the trajectory of the UAV and the offloading time in order to minimize the mission completion time of the UAV.…”