Mobile Edge Computing Server Research Articles

Rapid prototyping and performance evaluation of ETSI MEC-based applications

The Multi-access Edge Computing (MEC) standard of the European Telecommunications Standards Institute (ETSI) will enable context-aware services for users of mobile 4G/5G networks. ETSI MEC application developers need tools to aid the design and the performance evaluation of their apps. During the early stages of deployment, they should be able to evaluate the performance impact of design choices - e.g., what round-trip delay can be expected due to the interplay of computation, communication and service consumption. When a prototype of the app exists, it needs to be tested live, under controllable conditions, to measure key performance indicators. In this paper, we present an open-source framework that allows developers to do all the above. Our framework is based on Simu5G, the OMNeT++ based simulator of 5G (NewRadio) and 4G (LTE) mobile networks. It includes models of ETSI MEC entities (i.e., MEC orchestrator, MEC host, etc.) and provides a standard-compliant RESTful interface towards application endpoints. Moreover, it can interface with external applications, and can also run in real time. Therefore, one can use it as a cradle to run a MEC app live, having underneath both 4G/5G data packet transport and MEC services based on information generated by the underlying emulated radio access network. We describe our framework and present a use-case of an emulated MEC-enabled 5G scenario.

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

EECDN: Energy-efficient Cooperative DNN Edge Inference in Wireless Sensor Networks

Multi-access edge computing (MEC) is emerging to improve the quality of experience of mobile devices including internet of things sensors by offloading computing intensive tasks to MEC servers. Existing MEC-enabled cooperative computation offloading works focus on the optimization of total energy consumption but fail to exploit multi-relay diversity and min-max fairness of energy consumption on participated sensors. We explore a typical wireless sensor network with multi-source, multi-relay, and one edge server, where relay nodes can provide both cooperative communication and computation services. We divide the energy efficiency optimization problem into two sub-problems: One is to minimize the weighted average total energy consumption per time slot, and the other is to minimize the maximum weighted energy consumption. For the first sub-problem, we propose an optimal algorithm named as optimal weighted average total energy consumption algorithm (OTCA) based on bipartite matching. For the second sub-problem, greedy algorithm for fairness guarantee (GAF) is proposed with an approximation ratio of (1 + ε), where ε is a small positive constant. Extensive numerical results show that OTCA outperforms the baseline algorithms by 26.7–77.4% on the average total weighted energy consumption while GAF outperforms benchmark algorithms by 30.7–84.4%. NS-3 simulation experiments comply with numerical results.

A MEC architecture for a better quality of service in an Autonomous Vehicular Network

Designing 5G networks and related applications such as the Internet of Things (IoT), Cellular and autonomous vehicular networks (AVNET) is a challenge. Indeed, these networks are subjected to a number of constraints that play a crucial role in the network’s quality of service (QoS). Among these constraints, we have the management of computing, storage, bandwidth resources and low latency requirements. If in the past cloud computing has been used to avoid networks being affected by some of the previous constraints, that technology negatively affects the low latency requirements required by AVNETs for example. Multi-access Edge Computing (MEC) has recently emerged as a palliative solution to cloud computing. MEC aims to bring computing and storage resources from Cloud Data Center to Edge Data Center nearer to the User Equipment (UEs) in order to reduce the UEs’ requests latency for an improvement of the network’s QoS. Many MEC architectures have been proposed for AVNET. These solutions make use of Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC) or Network Slicing (NS) technologies. Some of these techniques combine partially these technologies while others do not. But to the best of our knowledge, none of them combines all these technologies to obtain a better QoS. In this paper, we combine the SDN, NFV, SFC and NS technologies to efficiently manage the MEC server resources for guaranteeing the QoS requirements in AVNETs. The QoS parameters we consider are latency, computing, storage and bandwidth resources. In that way, we first present a MEC server mathematical resource management model, then, we propose a new MEC architecture adapted to AVNETs that uses the aforementioned technologies also as the mathematical model we first present to manage the bandwidth, computing, and storage resources. The simulation results show that these resources are well managed, resulting to a low end to end latency for autonomous vehicles (AVs) requests executing on edge/cloud servers when the direct communication of AVs with cloud server takes long end to end delays, ensuring a better QoS for the AVNET.

Collaborative coalitions-based joint service caching and task offloading for edge networks

Mobile edge computing (MEC) is seen as a promising method of computation offloading. Moving services from cloud servers to mobile edge nodes (MENs) can decrease service response time. Due to the restricted processing and bandwidth capabilities of MEC networks, service caching and replacement incurs inescapable latency and energy costs. Thus motivated, we propose the joint service caching and task offloading problem in edge networks (SCTOE) to minimize the cost of edge services. We present a collaborative service caching mechanism (CSCM) that strengthens service sharing between MENs by forming caching coalitions. We prove that SCTOE is NP-hard and that CSCM can enhance the service efficiency of the edge network by theoretical analyses. Then we utilize a random rounding algorithm (RR) to figure out the feasible solution to the SCTOE problem. Finally, we test the performance of the CSCM+RR algorithm by simulating experiments using a real dataset. Simulation results demonstrate that our approach can effectively utilize the available caching resources to maximize the performance of the algorithm in the average response delay and the profit of edge networks.

Joint Task Offloading and Service Caching for Multi-Access Edge Computing in WiFi-Cellular Heterogeneous Networks

Enabled by Multi-access Edge Computing (MEC) in a WiFi-cellular heterogeneous network, the tasks of mobile terminals (MTs) can be offloaded via the cellular network to the MEC servers or cloud server, or via the WiFi network to alleviate transmission congestion of the cellular network. The MEC also enables service caching to cache the programs/libraries/databases of the tasks to avoid repeated input data uploading. Existing research works lack joint optimization on the task offloading and service caching for MEC in the WiFi-cellular heterogeneous network. In this paper, a novel resource management scheme for joint task offloading and service caching is proposed to maximize the energy consumption benefits of all the MTs covered by a WiFi-cellular heterogeneous network while guaranteeing the task processing delay tolerance of each MT. We consider the constraints on limited computing and storage resources of the MEC servers equipped on the cellular base station and the WiFi access point, and we also consider cellular channel allocation for the task offloading. We design an iterative algorithm based on the alternating optimization technique to solve the proposed mixed integer nonlinear programming problem efficiently. Extensive simulations are conducted in multiple scenarios by varying different crucial parameters. The numerical results demonstrate that our scheme can largely improve the system performance in all the scenarios, and energy consumption reduction optimized by our scheme is 16.24%-43.09% higher than those by the comparative works.

Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.

Wireless Multiplayer Interactive Virtual Reality Game Systems With Edge Computing: Modeling and Optimization

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation.

Group Delay-Aware Scalable Mobile Edge Computing Using Service Replication

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches.

Computation Offloading and Resource Allocation in MEC-Enabled Integrated Aerial-Terrestrial Vehicular Networks: A Reinforcement Learning Approach

As important services of the future sixth-generation (6G) wireless networks, vehicular communication and mobile edge computing (MEC) have received considerable interest in recent years for their significant potential applications in intelligent transportation systems. However, MEC-enabled vehicular networks depend heavily on network access and communication infrastructure, often unavailable in remote areas, making computation offloading susceptible to breaking down. To address this issue, we propose an MEC-enabled vehicular network assisted through aerial-terrestrial connectivity to provide network access and high data-rate entertainment services to a vehicular network. We present a time-varying, dynamic system model where high altitude platforms (HAPs) equipped with MEC servers, connected to a backhaul system of low-earth orbit (LEO) satellites, are used to provide computation offloading capability to the vehicles, as well as to provide network access for vehicle-to-vehicle (V2V) communications. Our main objective is to minimize the total computation and communication overhead of the joint computation offloading and resource allocation strategies for the system of vehicles. Since our formulated optimization problem is a mixed-integer non-linear programming (MINLP) problem, which is NP-hard, we propose a decentralized value-iteration-based reinforcement learning (RL) approach as a solution. In our Q-learning-assisted analysis, each vehicle acts as an intelligent agent to form optimal strategies for offloading and resource allocation. We further extend our solution to deep Q-learning (DQL) and double deep Q-learning to overcome the issues of dimensionality and the over-estimation of the value functions, as in Q-learning. Simulation results prove the effectiveness of our solution in successfully reducing system costs compared to baseline schemes.

Lyapunov Optimization Based Mobile Edge Computing for Internet of Vehicles Systems

Mobile-Edge Computing (MEC) is an emerging paradigm in the Internet of Vehicles (IoV) to meet the ever-increasing computation demands of smart applications. To provide satisfactory computation performance, it is of significant importance to conduct computation offloading in IoV. In this paper, we investigate a multi-vehicle IoV system assisted by MECs with limited computation resources, where vehicles with complex applications can offload their subtasks to MEC servers. Applications are modeled as interdependent subtasks with general random task graphs, different from existing works with independent ones. To maximize the average logarithmic data processing rate (LDPR), the computation offloading problem is formulated as a time-average optimization with long-term constraints, which results from variable vehicle number, various applications and time-varying communication channels. To reduce the cooperation overhead, we propose a multi-agent Proximal Policy Optimization algorithm (Ly-MAPPO) which requires local observation only to solve the subproblems achieved by Lyapunov optimization technique in real time. In addition, to improve the performance of the Ly-MAPPO algorithm, Graph Convolutional Neural Network (GCN) is introduced to extract inter-dependencies between subtasks. Extensive simulations show that the GCN embedded Ly-MAPPO outperforms other baseline algorithms, e.g., greedy algorithm and gene algorithm, etc., for different traffic loads and computation resources in MEC servers.

The Meta Distribution of Task Offloading in Stochastic Mobile Edge Computing Networks

The stochastic geometry modeling and analysis of mobile edge computing (MEC) networks contribute to the effective network configuration. Most prior works investigate the MEC network performance by spatial averaging the topological randomness, which can hide the differences among users and result in inaccurate analytical results. To address this limitation, an analytical framework is developed to study the meta distribution of the task offloading performance. We consider the Poisson MEC network where the users with dynamic task requests offload the tasks to the MEC server. Given the model, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b$</tex-math></inline-formula> -th moment and the meta distribution of the success task offloading probability are first analyzed. Further, the exact yet convenient approximation of the MEC computing load is derived, based on which the edge computing delay is obtained. Our results demonstrate that the proposed meta-analytical framework allows tractable yet accurate analytical results, and provides additional fine-grained information about MEC-network planning.

Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System.

Unmanned aerial vehicles (UAVs) are widely used in wireless communication systems due to their flexible mobility and high maneuverability. The combination of UAVs and mobile edge computing (MEC) is regarded as a promising technology to provide high-quality computing services for latency-sensitive applications. In this paper, a novel UAV-assisted MEC uplink maritime communication system is proposed, where an MEC server is equipped on UAV to provide flexible assistance to maritime user. In particular, the task of user can be divided into two parts: one portion is offloaded to UAV and the remaining portion is offloaded to onshore base station for computing. We formulate an optimization problem to minimize the total system latency by designing the optimal flying altitude of UAV and the optimal task allocation ratio. We derive a semi closed-form expression of the optimal flying altitude of UAV and a closed-form expression of the optimal task allocation ratio. Simulation results demonstrate the precision of the theoretical analyses and show some interesting insights.

Non-cooperative game algorithms for computation offloading in mobile edge computing environments

Mobile Edge Computing (MEC) has become a promising technology for 5G networks. Computation offloading is an essential issue of MEC, which enables mobile User Equipment (UE) to enjoy rich wireless resources and huge computing power anywhere. This paper considers the Quality-of-Experience (QoE) of UEs in 5G MEC systems and presents a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm for computation offloading of MEC applications. We establish an MEC computation offloading model by considering the QoE requirements of UEs, and discuss the communication overheads, computation cost, and energy consumption models to minimize the energy consumption and time delay of each UE. Considering that there are multiple UEs who want to offload their computation tasks to a resource-constrained MEC server, and each UE is selfish and competitive, we formulate the problem of computation offloading decision as a non-cooperative game model. We prove the existence of a Nash Equilibrium (NE) solution for the proposed game model. In addition, we propose an algorithm that jointly optimizes energy consumption and time delay under QoE preferences to achieve optimal offloading benefits for each UE. Moreover, we respectively propose a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm to efficiently find the NE solution. Extensive simulation experiments are conducted to verify the effectiveness of the proposed MEC computation offloading model and the QCOG-DG and QCOG-SG algorithms. Simulation results show that the proposed QCOG-DG algorithm can efficiently find the NE solutions in the MEC scenarios with UEs of different sizes.

DOT: Decentralized Offloading of Tasks in OFDMA-Based Heterogeneous Computing Networks

A fundamental issue in multiaccess edge computing (MEC) is efficiently offloading multiple tasks to multiple helper nodes (MTMH), i.e., MEC servers. However, most of the existing decentralized schemes do not consider interuser interference or merely adopt time division multiple access (TDMA) as the multiple access scheme for MTMH in the heterogeneous scenario, leading to a large latency. To address these issues, we propose DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, to minimize the sum cost in terms of energy consumption and delay. Specifically, we first formulate DOT as an optimization problem considering the interuser interference and dynamics in communication and computation resource allocation. Then, considering the huge dimension of potential offloading decisions and conflicting objectives of different users, the total cost of each user is minimized in a distributed manner by modeling the offloading problem as a potential game. The formulated potential game is proved to be an ordinal potential game and thus admits a Nash equilibrium (NE). Further, we develop an offloading algorithm to achieve the NE by exploiting the finite improvement property. Finally, simulation results demonstrate that DOT can achieve a lower cost compared with other baselines.

5G Multi-RAT URLLC and eMBB Dynamic Task Offloading With MEC Resource Allocation Using Distributed Deep Reinforcement Learning

In this article, a deep reinforcement learning (DRL) control scheme is proposed to satisfy the strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication (URLLC) and enhanced mobile broadband (eMBB) using 5G multiple radio access technology (RAT)-based partial offloading and multiaccess edge-computing (MEC) resource allocation. In the proposed scheme, the user equipment (UE) makes optimal offloading decisions while the MEC server dynamically adjusts the server resources based on offloading requests from multiple UEs using DRL technology. The aim of the proposed scheme is to minimize the energy consumption of the UEs while maximizing the system utility (SU) performance, which is composed of the spectral efficiency (SE) and offloading success rate (OSR) of the MEC server. In addition, multiagent distributed learning technology and best experience push (BEP) techniques are used to enhance the learning efficiency of the DRL framework. The simulation result shows that the proposed scheme provides an improved SU and energy consumption performance compared to the benchmark offloading schemes.

Dynamic Energy-Saving Offloading Strategy Guided by Lyapunov Optimization for IoT Devices

In the Internet of Everything era, various Internet of Things (IoT) devices have become popular, and the number of computing-intensive applications has increased substantially. As an emerging technology, mobile-edge computing (MEC) gives network edge nodes stronger computing and storage capabilities, bringing users a good Quality of Experience (QoE). By offloading some computing tasks to the edge for processing, the burden on IoT devices can be effectively reduced. However, this approach exacerbates the computing and storage resource depletion of the MEC server and the bandwidth and transmission cost of the wireless link used to offload computing tasks. Additionally, making an offloading decision online without future system status information is a considerable challenge. Therefore, we should study and design a reasonable offloading strategy to reduce the additional overhead, which is of significance. We establish a virtual queue model to describe the workload offloading problem of IoT devices in a two-layer MEC network. This is a stochastic optimization problem. Based on Lyapunov optimization, we transform the research problem into a deterministic optimization problem. A Lyapunov online energy consumption optimization algorithm (LOECOA) is proposed to effectively balance the system’s queue backlog and energy consumption. Based on theoretical analysis and a large number of experimental and numerical results, our algorithm performs better on energy consumption while satisfying the system constraints under a dynamic task arrival rate.

Communication-, Computation-, and Control-Enabled UAV Mobile Communication Networks

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

Deep reinforcement learning based offloading decision algorithm for vehicular edge computing.

Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can’t maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG.

Joint resource allocation, user clustering and 3-D location optimization in multi-UAV-enabled mobile edge computing

This paper studies a multi-unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAVs are equipped with a powerful MEC server to provide computation services for ground users. Under NOMA protocol and partial offloading mode, we aim to jointly optimize the NOMA user clustering, sub-carrier assignment, transmit power of users, CPU frequency, and 3-D location of UAVs to maximize the sum computation bits of all users. Since the formulated problem is a mixed-integer non-linear optimization, we propose a three-stage algorithm to address the four sub-problems iteratively. First, two efficient NOMA user clustering algorithms are proposed to divide the users into several NOMA clusters. Second, a heuristic sub-carrier assignment algorithm is presented based on the channel gain and interfering users. Third, by adopting the successive convex approximation (SCA) technique and Lagrange dual method, the closed-form expressions of the transmit power and CPU frequency of users are derived. Forth, a convex optimization problem is obtained to optimize the 3-D location of UAVs. The numerical evaluations illustrate that the proposed system outperforms the benchmark schemes in terms of sum computation bits performance.