关键词： unmanned aerial vehicle   mobile edge computing   computation offloading   deep deterministic policy gradient   K-nearest neighbor  

摘要： Unmanned aerial vehicles (UAVs) are widely used to improve the coverage and communication quality of wireless networks and assist mobile edge computing (MEC) due to their flexible deployments. However, the UAV-assisted MEC systems also face challenges in terms of computation offloading and trajectory planning in the dynamic environment. This paper employs deep reinforcement learning to jointly optimize the computation offloading and trajectory planning for UAV-assisted MEC system. Specifically, this paper investigates a general scenario where multiple pieces of user equipment (UE) offload tasks to a UAV equipped with a MEC server to collaborate on a complex job. By fully considering UAV and UE movement, computation offloading ratio, and blocked relations, a joint computation offloading and trajectory optimization problem is formulated to minimize the maximum computational delay. Due to the non-convex nature of the problem, it is converted into a Markov decision process, and solved by the deep deterministic policy gradient (DDPG) algorithm. To enhance the exploration capability and stability of DDPG, the K-nearest neighbor (KNN) algorithm is employed, namely KNN-DDPG. Moreover, the prioritized experience replay algorithm, where the constant learning rate is replaced by the decaying learning rate, is utilized to enhance the converge. To validate the effectiveness and superiority of the proposed algorithm, KNN-DDPG is compared with the benchmark DDPG algorithm. Simulation results demonstrate that KNN-DDPG can converge and achieve 3.23% delay reduction compared to DDPG.

关键词： Resource management   Task analysis   Servers   Delays   Reliability engineering   Error probability   Reliability theory   Time division multiple access   mobile edge computing   finite blocklength   extreme value theory   6G  

摘要： In this paper, we study a multi-access mobile edge computing (MEC) network in the industrial Internet-of-Things (IoT) scenario, which aims at providing a joint computation service for a group of sub-tasks offloaded from multiple user equipments (UEs). The whole MEC service, including a communication phase and a computation phase, is required to satisfy both a low latency and a high reliability requirement. We derive the end-to-end reliability (error probability) of the whole MEC service and provide corresponding reliability-optimal design frameworks, where both the perfect channel state information (CSI) and outdated CSI scenarios are considered. In particular, we characterize the low-latency communication behavior with the consideration of the finite blocklength (FBL) impact, and exploit the extreme value theory to study the delay violation probability in the computation phase. Following the characterizations, in the perfect CSI scenario, a design framework minimizing the instantaneous end-to-end error probability is provided, i.e., via optimally choosing the time length for each user's offloading and the time length for the computation phase. We rigorously prove the convexity of the problem, investigate the relationships among the variables in the optimal solution, based on which a low-complexity method is proposed achieving the optimal solution. In addition, for the scenario with only the outdated CSI, after deriving the expected end-to-end error probability conditioned on the outdated CSI value, a corresponding optimal time allocation design is provided as well, where the convexity of the formulated problem is characterized and the optimal solution is obtained. Via simulations, we validate our analytical model and evaluate the network performance under the design.

关键词： Mobile edge computing   Service caching   Task partitioning   Computation offloading   Deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) federations aim to establish a joint edge service model between Edge Infrastructure Providers (EIPs) and clouds, facilitating the sharing and utilization of MEC services and resources. However, in such a hierarchical multi-EIP MEC federation environment, optimizing service caching, task partitioning, and pricing strategies to maximize the profits gained by EIPs and minimize the cost of mobile devices (MDs) is challenging. To address this challenge, This paper first formulates a two -stage multi -leader, multi -follower Stackelberg game between EIPs and MDs. Then, a new method combining a Stackelberg-based Multi -Agent Deep Deterministic Policy Gradient (STMADDPG) algorithm and a Transformer -based Popularity Prediction (TPP) model is devised to learn the optimal strategies. Extensive experiments with real -world datasets are conducted to demonstrate the excellent performance of the proposed STMADDPG-TPP method.

关键词： Low earth orbit satellites   Satellites   Task analysis   Computational modeling   Edge computing   Heuristic algorithms   Energy consumption   Mobile edge computing   low earth orbit satellite   D3QN   computation offloading   queue  

摘要： Efficient computation offloading is crucial for resource-constrained users in low earth orbit (LEO) satellite edge computing system. The proposed Dueling Double Deep Q Network (D3QN)-based computation offloading algorithm considers LEO satellite mobility, dynamic load levels, queuing theory, and jointly optimizes system delay and energy consumption. Simulation results show that the proposed algorithm has better system cost than other comparison algorithms.

关键词： Adaptive routing protocol   Cloud computing   Energy efficiency   Internet of Things   Link quality prediction   Mobile edge computing  

摘要： The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices' energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network's objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object's movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding.

关键词： Unmanned Aerial Vehicles   H-NOMA   Energy consumption   OMA   Mobile Edge Computing   Clustering  

摘要： In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (HNOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAVhosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.

关键词： Millimeter wave communication   Task analysis   Resource management   Autonomous aerial vehicles   Antenna arrays   Energy consumption   Servers   Mobile edge computing   millimeter-wave communication   computation offloading   unmanned aerial vehicle  

摘要： In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.

关键词： 5G/6G vehicular networks   Graph neural network   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a key technology that emerged to address the increasing computational demands and communication requirements of vehicular networks. It is a form of edge computing that brings cloud computing capabilities closer to end-users, specifically within the context of vehicular networks, which are part of the broader Internet of Vehicles (IoV) ecosystem. However, the dynamic nature of traffic flows in MEC in 5G/6G vehicular networks poses challenges for accurate prediction and resource allocation when aiming to provide edge service for mobile vehicles. In this paper, we present a novel approach to predict the traffic flow of MEC in 5G/6G vehicular networks using graph-based learning. In our framework, MEC servers in vehicular networks are construed as nodes to construct a dynamic similarity graph and a dynamic transition graph over a duration of multiple days. We utilize Graph Attention Networks (GAT) to learn and fuse the node embeddings of these dynamic graphs. A transformer model is subsequently employed to predict the vehicle frequency accessing the edge computing services for the next day. Our experimental results have shown that the model achieves high accuracy in predicting edge service access volumes with low error metrics.

关键词： Mobile edge computing   Bi-level optimization   Nash equilibrium   Game theory  

摘要： In the mobile edge computing system subject to wireless interference, the Edge Server Provider (ESP) aims to offer profitable computing resources to Device Managers (DMs), who make optimal strategies based on the provided prices. However, the existence of mixed variables typically constitutes an NP -hard problem, posing a significant challenge for optimization. In response to address this issue, we formulate a bi-level optimization problem, where the upper level is devoted to optimizing the pricing of computing resources. The lower level optimizes DM migration strategies and resource allocation at specified prices. Leveraging game theory, we achieve distributed and efficient computation offloading by formulating the distributed computing offloading strategy among lower -level DMs as a task offloading game. Our analysis identifies Nash equilibrium and finite improvement characteristics within the game. Based on these insights, we present a bi-level distributed computation offloading algorithm capable of reaching Nash equilibrium, thus optimizing the profit for both DMs and ESP. The experiments have demonstrated the algorithm's effectiveness in reducing system costs and maximizing the profits of ESP with DMs across diverse scenarios.

关键词： Task analysis   Processor scheduling   Scheduling   System performance   Probability density function   Probability   Power system reliability   Mobile edge computing   user scheduling   computational access point selection   system outage probability  

摘要： The advent of mobile edge computing (MEC) has effectively resolved the challenges associated with computation-intensive applications. In this letter, we investigate a multi-user MEC system with multiple computational access points (CAPs) in the presence of an eavesdropper. In contrast to existing works that only considered user scheduling or CAP selection, we propose a joint user scheduling and CAP selection policy, and derive the system outage probability under the proposed policy in closed form. Simulations are provided to validate the correctness of the theoretical analysis and demonstrate the effectiveness of the proposed policy. The results confirm that the proposed joint user scheduling and CAP selection policy outperforms various existing policies that independently consider user scheduling and CAP selection.