This paper proposes a novel user cooperation approach in both computation and communication for mobile edge computing (MEC) systems to improve the energy efficiency for latency-constrained computation. We consider a basic three-node MEC system consisting of a user node, a helper node, and an access point (AP) node attached with an MEC server, in which the user has latency-constrained and computation-intensive tasks to be executed. We consider two different computation offloading models, namely the partial and binary offloading, respectively. For partial offloading, the tasks at the user are divided into three parts that are executed at the user, helper, and AP, respectively; while for binary offloading, the tasks are executed as a whole only at one of three nodes. Under this setup, we focus on a particular time block and develop an efficient fourslot transmission protocol to enable the joint computation and communication cooperation. Besides the local task computing over the whole block, the user can offload some computation tasks to the helper in the first slot, and the helper cooperatively computes these tasks in the remaining time; while in the second and third slots, the helper works as a cooperative relay to help the user offload some other tasks to the AP for remote execution in the fourth slot. For both cases with partial and binary offloading, we jointly optimize the computation and communication resources allocation at both the user and the helper (i.e., the time and transmit power allocations for offloading, and the central process unit (CPU) frequencies for computing), so as to minimize their total energy consumption while satisfying the user's computation latency constraint. Although the two problems are non-convex in general, we develop efficient algorithms to solve them optimally. Numerical results show that the proposed joint computation and communication cooperation approach significantly improves the computation capacity and energy efficiency at the user and helper, as compared to other benchmark schemes without such a joint design.

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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.

This paper proposes a novel joint computation and communication cooperation approach in mobile edge computing (MEC) systems, which enables user cooperation in both computation and communication for improving the MEC performance. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite block, and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the computation and communication resource allocation at both the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. We provide the optimal solution to this problem. Numerical results show that the proposed joint cooperation approach significantly improves the computation capacity and the energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design.