关键词： 360 degrees virtual reality   multiuser access control   proactive caching   mobile edge computing  

摘要： Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider's cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360 degrees VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server's computing capacity (the BS's bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%).

关键词： deep reinforcement learning   internet of vehicles   mobile edge computing   service placement  

摘要： Edge computing offers crucial computational and storage support to vehicles by providing various services within the framework of the Internet of Vehicles in intelligent transportation systems. Service placement (SP) becomes particularly challenging when edge resources are limited and vehicles exhibit high-mobility. Many current dynamic placement methods rely on real-time placement, often leading to increased costs, instability, and frequent changes. This paper proposes SACRF-SP, an adaptive dynamic service placement algorithm based on Soft Actor-Critic (SAC) and Random Forest (RF), for dynamic urban traffic scenarios. This algorithm utilizes the SAC method to identify optimal placement nodes and integrates an RF model to predict service request trends. A decision network is constructed to assess the necessity of redeployment. Extensive simulation experiments demonstrate that SACRF-SP significantly reduces latency, resource usage, and the frequency of redeployment.

关键词： UAV swarm   mobile edge computing   computational offloading   data compression   deep reinforcement learning  

摘要： The combination of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) effectively meets the demands of user equipments (UEs) for high-quality computing services, low energy consumption, and low latency. However, in complex environments such as disaster rescue scenarios, a single UAV is still constrained by limited transmission power and computing resources, making it difficult to efficiently complete computational tasks. To address this issue, we propose a UAV swarm-enabled MEC system that integrates data compression technology, in which the only swarm head UAV (USH) offloads the compressed computing tasks compressed by the UEs and partially distributes them to the swarm member UAV (USM) for collaborative processing. To minimize the total energy and time cost of the system, we utilize Markov Decision Process (MDP) for modeling and construct a deep deterministic policy gradient offloading algorithm with a prioritized experience replay mechanism (PER-DDPG) to jointly optimize compression ratio, task offloading rate, resource allocation and swarm positioning. Simulation results show that compared with deep Q-network (DQN) and deep deterministic policy gradient (DDPG) baseline algorithms, the proposed scheme performs excellently in terms of convergence and robustness, reducing system latency and energy consumption by about 32.7%.

关键词： Mobile edge computing   Resource reservation   Energy harvesting   Contract theory   Information asymmetry  

摘要： To address bottleneck of energy performance, this paper considers an energy harvesting (EH)-enabled mobile edge computing (MEC) system with contract users (CUs), random users (RUs) and an agent node (AN). AN can manage resource interactions between MEC server and users. The resource demands of CUs are known in advance, while RUs are mobile and arrive randomly. We propose a resource reservation framework based on contract theory with the goal of maximizing the utility of AN under information asymmetry and demand uncertainty. First, for symmetric information scenario, CUs are willing to share information with MEC server to obtain the relationship between the optimal resource reservation and the demands of CUs. Then, for asymmetric information scenario, a contract-based resource reservation scheme is proposed to motivate wireless access point to share the demand information of CUs and undertake partial over-reservation risk cost. Based on this, the theoretical expression for the optimal reserved resource is obtained by exploring the features of the feasible contracts under individual rationality and incentive compatibility constraints. We leverage simulation to validate the proposed contract-based resource reservation scheme and verify its effectiveness through simulation results.

关键词： Integrated access and backhaul   Mobile edge computing   Deep learning   Task offloading   Energy efficiency  

摘要： In the B5G era, the number of Internet of Things (IoT) devices is surging, leading to more diverse demands for intelligent applications, most of which are latency-sensitive and resource-intensive. This trend places a demand on communication networks for ultra-high bandwidth, low latency, and high reliability. In this context, UAV-assisted networks combine the advantages of drones and edge computing, providing computational and communication resources over wide areas, thereby enhancing the service capabilities of edge computing. This study explores the issues of task offloading and resource allocation problem in hierarchical edge computing within UAV-assisted Integrated Access and Backhaul Network (IABN) operating in the millimeter-wave band. For hierarchical mobile edge computing systems in integrated access and backhaul networks, we model the optimization problem of minimizing task offloading energy consumption, considering power and latency constraints, and decompose it into two more tractable subproblems. A method based on deep neural networks (DNN) and iterative order-preserving optimization is proposed to obtain the optimal solution for subproblem 1. Additionally, a Whale Optimization-based Power Allocation (WOPA) approach is proposed to solve subproblem 2. Finally, we compared the proposed algorithm's energy consumption and delay in various configuration scenarios against baseline algorithms. The results demonstrate that the proposed algorithm achieves minimal energy consumption with almost no overdue tasks.

关键词： Optimization   Resource management   Computational modeling   Games   Wireless communication   Servers   Real-time systems   Delays   Collaboration   Autonomous aerial vehicles   Collaborative intelligent transportation systems   mobile edge computing   Stackelberg game  

摘要： The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs' offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

关键词： game theory   mobile edge computing   Nash bargaining   resource allocation  

摘要： While mobile edge computing (MEC) holds promise to enhance users' mobile experience, how to make appropriate offloading decisions under consideration of quality of service (QoS) and energy consumption requirements is challenging. In this research, we focus on obtaining an equilibrium solution that ensures not only the fairness of terminal users and utility maximization for the edge service providers but also QoS and energy consumption requirements. We formulate the cooperative game resource allocation optimization problem (CGRA) under the complete/incomplete information environment and propose the Nash Bargaining Cooperative Game (NBCG) policy to solve it. We perform experiments and compare it with six schemes. Experimental results show that the proposed NBCG can gain better offloading profits than the benchmarks and improve the percent of guaranteed tasks by as much as 23% under different input data sizes, as much as 18% under different time delay tolerances, and as much as 25% under different execution weights.

关键词： Autonomous aerial vehicles   Quality of service   Optimization   Resource management   Costs   Batteries   Computational modeling   Throughput   Transformers   Trajectory   Autonomous aerial vehicle   mobile edge computing   decision transformer   deep reinforcement learning  

摘要： Deep reinforcement learning (DRL) has been widely applied to dynamic resource allocation for the autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) networks. However, it is often challenging to generalize a well-trained DRL model to new MEC scenarios once the system constraints change, since retraining DRL models from scratch is time and energy consuming. In this letter, we jointly optimize the AAV's trajectory and computing resource allocation for maximizing the fairness-based throughput under the battery capacity and quality of service (QoS) constraints. The sequential optimization problem is formulated as a constrained Markov decision process (CMDP) and solved via the constrained DRL algorithms. To generalize the optimized resource allocation policies across various energy and QoS constraints, we propose an offline pre-training and online fine-tuning based constrained Decision Transformer (CDT) framework. In particular, the CDT is first pre-trained on the training samples collected by the constrained DRL algorithm offline, and then fine-tuned online for rapid adaptation to the unseen constraint thresholds. Simulation results show that compared with the benchmark DRL algorithms, the CDT is capable of effectively improving the fairness-based throughput under the battery capacity and QoS constraints, and demonstrates rapid convergence when constraints change.

关键词： Computational efficiency   Resource management   Delays   Servers   Edge computing   Minimization   Wireless communication   Time-frequency analysis   RF signals   Radio spectrum management   Mobile edge computing   wireless power transfer   multi-agent deep reinforcement learning   energy provision minimization  

摘要： This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose a Two-stage Multi-Agent deep reinforcement learning-based Distributed computation Offloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.

关键词： Hybrid quantum model   mobile edge computing   quantum reinforcement learning   quantum reinforcement learning   resource allocation   resource allocation   task offloading   task offloading   task offloading  

摘要： Mobile edge computing (MEC) has revolutionized the way computational tasks are offloaded and latency is reduced by leveraging edge servers close to end devices. Efficient resource allocation and task offloading are crucial for enhancing system performance in MEC environments. Traditional reinforcement learning (RL) approaches have shown promise in optimizing resource allocation and task offloading problems. However, they often face challenges such as high computational complexity and the need for extensive training data. Quantum reinforcement learning (QRL) emerges as a promising solution to overcome these limitations by leveraging quantum computing principles to enhance efficiency and scalability. In this paper, we propose a hybrid quantum-classical non-sequential model for joint resource allocation and task offloading in MEC systems. Our model combines the advantages of RL in handling environmental dynamics and quantum computing in reducing adjustable parameters and accelerating the training process. Extensive experiments demonstrate that our proposed algorithm can achieve higher training and inference performance under various parameter settings compared to traditional RL models and previous QRL models.