关键词： Trajectory   Costs   Buildings   Wireless communication   Clustering algorithms   Trajectory planning   Task analysis   5G   6G networks   mobile edge computing   intelligent reflecting surface   multiunmanned aerial vehicle   distance-based clustering algorithm   differential evolution  

摘要： This article presents a multi-intelligent reflecting surface (IRS)-and multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system for 5G/6G networks. In the studied system, multiple UAVs are integrated for providing services to large-scale user equipment (UEs) with the help of multiple IRSs. This article aims to minimize the overall cost including energy consumption, completion time, and maintenance cost of UAVs by jointly optimizing the trajectories of UAVs and phase shifts of IRSs. When solving this problem, one has to count in mind the deployment of stop points (SPs) of UAVs, and consider the association among UEs, and UAVs (i.e., which UE will send data to which UAV at which SP), the order of SPs, and the phase shifts of IRSs. Therefore, traditional optimization techniques may not solve the above-mentioned problem in an efficient way. To tackle the above-mentioned problem, this article proposes an algorithm called TPaPBA that consists of four phases. The first phase optimizes the SPs' deployment via using a differential evolution algorithm having variable population size. As a result, all the SPs of UAVs can be obtained. Then, the second phase optimizes the association among UEs, SPs, and UAVs. Specifically, TPaPBA first adopts a clustering algorithm to optimize the SPs-UAVs association, and then a close criterion is introduced to optimize UEs-SPs association. Subsequently, third phase adopts a low-complexity greedy algorithm to optimize the order of SPs for UAVs. Finally, the phase shifts of IRSs are optimized to enhance the data rate between UEs and UAVs. The simulation results of TPaPBA on ten instances having UEs ranging from 100 to 1000, reveals that TPaPBA has significantly improved the system performance contribution and outperforms other approaches in terms of reducing the overall cost of UAVs.

关键词： Mobile edge computing   Wireless power transfer   Computation offloading   Reinforcement learning  

摘要： The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks.

关键词： mobile edge computing   multi-terminals offloading   mechanical fault diagnosis   reinforcement learning  

摘要： Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.

关键词： Protocols   Task analysis   Resource management   Energy exchange   Energy harvesting   Uplink   Servers   Wireless power transfer   mobile edge computing   reconfigurable intelligent surface   simultaneous transmission and reflection   resource allocation  

摘要： In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals, and also provides new degrees-of-freedom (DoFs) for manipulating signal propagation. We aim to maximize the total computation rate of all users, where the energy transfer time, transmit power and CPU frequencies of users, and the configuration design of STAR-RIS are jointly optimized. Considering the characteristics of STAR-RIS, three operating protocols, namely energy splitting (ES), mode switching (MS), and time splitting (TS) are studied, respectively. For the ES protocol, based on the penalty method, successive convex approximation (SCA), and the linear search method, an iterative algorithm is proposed to solve the formulated non-convex problem. Then, the proposed algorithm for ES protocol is extended to solve the MS and TS problems. Simulation results illustrate that the STAR-RIS outperforms traditional reflecting/transmitting-only RIS. More importantly, the TS protocol can achieve the largest computation rate among the three operating protocols of STAR-RIS.

关键词： Internet of Things   NOMA   Task analysis   Millimeter wave communication   Array signal processing   Resource management   Minimization   Hybrid beamforming   massive MIMO   mobile edge computing   resource allocation  

摘要： Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.

关键词： mobile edge computing   blockchain   stackelberg game   resource allocation  

摘要： Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network.

关键词： Mobile edge computing   Deep reinforcement learning   DDPG   Microservice selection   Cloud-edge collaboration  

摘要： In mobile edge computing environment, due to resources constraints of edge devices, when user locations continue changing, the network will be delayed or interrupted, which affects the quality of user's service access. Previous studies have shown that deploying multiple microservice instances with the same function on multiple edge servers through container technology can solve this problem. However, how to choose the optimal microservice instance from multiple servers in a cloud-edge hybrid environment needs to be further investigated. This paper studies the selection of microservices problem based on the dynamic and heterogeneous characters of the cloud-edge collaborative environment, which is defined as a microservice selection and scheduling optimization problem (MSSP) to minimize users' service access delay. To cope with the complexity of cloud-edge collaborative environment and improve learning efficiency, MSSP is regarded as a Markov decision-making process, a Deep Deterministic Policy Gradient algorithm for microservice selection called MS_DDPG is then proposed to solve this problem, and the microservice selection strategy experience pool is established in MS_DDPG. Performance evaluations of MS_DDPG based on a real dataset and some synthetic dataset have been conducted, and the results show that MS_DDPG outperforms the other three baseline algorithms. In terms of average access delay, MS_DDPG is reduced by 23.82%. We also validate the performance of MS_DDPG by increasing the number of user requests, and the results also show that MS_DDPG obtains better performance in scalability.

关键词： high-speed railway   mobile edge computing   mobile 5G   picocell base station  

摘要： The fifth-generation (5G) wireless communication has experienced an upsurge of interest for empowering vertical industries, due to its high data volume, extremely low latency, high reliability, and significant improvement in user experience. Specifically, deploying 5G on high-speed railway (HSR) is critical for the promotion of smart travelling such that passengers can connect to the Internet and utilize the on-board time to continue their usual activities. However, there remains a series of challenges in practical implementation, such as the serious Doppler shift caused by the high mobility, the carriage penetration loss especially in the high-frequency bands, frequent handovers, and economic issues. To address these challenges, we propose three schemes in this article to improve the coverage of 5G networks on the train. In particular, we provide a comprehensive description of each scheme in terms of their network architecture and service establishment procedures. Specifically, the mobile edge computing (MEC) is used as the key technology to provide low-latency services for on-board passengers. Moreover, these three schemes are compared among themselves regarding the quality-of-service, the scalability of service, and the related industry development status. Finally, we discuss various potential research directions and open issues in terms of deploying 5G networks on HSR.

关键词： Training   Relays   Data models   Energy consumption   Mobile handsets   Computational modeling   Costs   Federated learning   mobile edge computing   resource management   cooperative networks  

摘要： Federated learning (FL) is a promising training paradigm to achieve ubiquitous intelligence for future 6G communication systems. However, it is challenging to apply FL in 6G-enabled edge system since decentralized training consumes considerable energy and mobile devices are mostly battery-powered and resource-constrained. The intensive computation and communication cost of local updates accumulated by hundreds of global rounds bring about the energy bottleneck, which is exacerbated when the data is non identically and independently distributed (non-IID). To address this issue, we propose FedRelay, a generic multi-flow relay learning framework in which local updates are performed relay-by-relay in the training flow via model propagation. We also present a decentralized relay selection protocol that takes advantage of the diversity of cooperative communication networks. Following that, we investigate a FedRelay optimization problem to simultaneously minimize the energy consumption of local updates and alleviate the global non-IIDness. Technically, an approximation algorithm is proposed to jointly optimize computation frequency and transmission power, thus reducing the local training overhead. We further regulate the training topology of each flow by proposing a greedy relay policy that encourages effective information exchange among devices. Experiment results show that, compared to state-of-the-art federated learning algorithms, our learning framework can save up to 5 times the total energy required to achieve a reasonable global test accuracy.

关键词： Task analysis   Servers   Stability analysis   Optimization   TV   Cloud computing   Trajectory   Computation offloading   deep reinforcement learning   intelligent vehicles   mobile edge computing  

摘要： Computation offloading is considered as a promising method to improve computation performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry applications to Mobile Edge Computing (MEC) servers. However, due to the dynamic environment of Intelligent Transportation Systems, it's challenging to develop an effective offloading scheme that ensures stable operation of the system. Furthermore, IVs only offload tasks to MEC servers or parked vehicles, how to take full advantage of resources in a random traffic flow is a main challenge. In this paper, we propose a vehicular-cloud-assisted MEC network in urban cities to fully explore the idle resources and ensure the stability of task queue. Specifically, we develop a novel virtual platform based on deep neural network that predicts vehicles trajectory and creates vehicular cloud to integrate computation resources. We then design a vehicular-cloud-assisted offloading scheme that aims to maximize task throughput in continuous time slots subject to multi-constraint. To address the coupling among multiple variables and different time slots, we put forward a lightweight framework to apply the Lyapunov optimization in combination with deep reinforcement learning. In particular, we apply Lyapunov drift-plus-penalty approach to decompose the initial problem into per-slot subproblems and control the long-term task queue stability. To further reduce the computation complexity, we decouple the subproblem into three processes, and the deep Q-network is put forward to solve it. Extensive simulations demonstrate that under different task arrivals, our proposed scheme can achieve the task queue stability within 10 time slots with a low transmission power.