关键词： Mobile edge computing  

摘要： Edge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency. © 2020, CC BY.

关键词： Mobile edge computing  

摘要： With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： mobile edge computing   age of information   constrained Markov decision process   structural analysis   Q-learning  

摘要： Fresh status updates are vital to the efficient operation of network monitoring and real-time control applications. In this paper, we consider a mobile edge computing(MEC)-assisted status update system, where smart devices extract valuable status updates from sensed data to achieve timely awareness of the surroundings by exploiting computational resources at the device and edge server. To quantify the freshness of status updates obtained by executing computation tasks, we employ the concept of age of information(Ao I) to characterize the timeliness of status updates. To cope with the limited energy at devices, we investigate a joint task generation and computation offloading scheme under a given energy budget for minimizing the age of obtained status updates. The age minimization problem is modeled as a constrained Markov decision process(CMDP). To obtain the optimal policy, we derive the structural properties of the optimal deterministic policy and propose a lightweight structure-based status update algorithm in the case of known channel statistics. Moreover, we consider a more realistic scenario without prior knowledge of channel statistics, and propose a Q-learning-based status update algorithm to make online decisions. Simulation results show that the performance of our proposed algorithms is competitive when compared with existing schemes.

关键词： cooperative communication   mobile edge computing   non-orthogonal multiple access   offloading outage probability  

摘要： In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access(OMA) in terms of offload performance;2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases;and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.

关键词： Mobile edge computing  

摘要： In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), where multiple users have some computational tasks assisted by multiple computational access points (CAPs). By offloading some tasks to the CAPs, the system performance can be improved through reducing the latency and energy consumption, which are the two important metrics of interest in the MEC networks. We devise the system by proposing the offloading strategy intelligently through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to automatically learn the offloading decision in order to optimize the system performance, and a neural network (NN) is trained to predict the offloading action, where the training data is generated from the environmental system. Moreover, we employ the bandwidth allocation in order to optimize the wireless spectrum for the links between the users and CAPs, where several bandwidth allocation schemes are proposed. In further, we use the CAP selection in order to choose one best CAP to assist the computational tasks from the users. Simulation results are finally presented to show the effectiveness of the proposed reinforcement learning offloading strategy. In particular, the system cost of latency and energy consumption can be reduced significantly by the proposed deep reinforcement learning based algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Task analysis   Wireless communication   Computational modeling   Servers   Resource management   Edge computing   Optimization   Mobile edge computing   optimization algorithm   deep reinforcement learning   resource allocation  

摘要： In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. Conventional numerical optimization methods are inefficient to solve such a problem, especially when the problem size is large. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a DNN to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, by analyzing the structure of the optimal solution, we derive a low-complexity algorithm for the critic network to quickly evaluate the ETC performance of the offloading decisions output by the actor network. With the low-complexity critic network, we can quickly select the best offloading action and subsequently store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. To further reduce the complexity, we show that the optimal offloading decision exhibits an one-climb structure, which can be utilized to significantly reduce the search space of action generation. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.1% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization me

关键词： Task analysis   Quality of service   Energy consumption   Servers   Delays   Energy harvesting   Microcell networks   Dynamic computation offloading   energy harvesting   small cell networks   mobile edge computing   POSG  

摘要： The combination of energy harvesting small cell networks (EH-SCNs) and mobile edge computing (MEC) has been considered as an effective means to improve the performance of mobile networks and provide users with a higher quality of service (QoS). In this letter, we investigate the decentralized computation offloading problem in heterogeneous EH-SCNs with MEC, where heterogeneous small cell base stations (SBSs) are rational individuals with interests to maximize their own benefits while considering their QoS requirements. Different from existing works, we address the challenge that heterogeneous SBSs may unwilling to expose their own information about the system state and offloading decisions. We formulate the problem as a partially observable stochastic game (POSG), in which SBSs can make optimal offloading decisions with imperfect state information. We analyze the local equilibrium, and propose a stochastic offloading algorithm to obtain the approximate optimal solution. Numerical results validate the effectiveness of the proposed scheme.

关键词： Accidents   Computational modeling   Task analysis   Delays   Data integrity   Data models   Autonomous vehicles   Autonomous driving   mobile edge computing   deep learning   computation offloading  

摘要： This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulated to maximize the inference accuracy subject to the offloading probability, the pre-braking probability, and data quality. Simulations demonstrate the superiority of the proposed offloading framework.

关键词： Reinforcement learning   Actor-critic algorithm   Eligibility traces   Mobile edge computing   Resource allocation  

摘要： Reinforcement learning (RL) as an effective tool has attracted great attention in wireless communication field nowadays. In this paper, we investigate the offloading decision and resource allocation problem in mobile edge computing (MEC) systems based on RL methods. Different from existing literature, our research focuses on improving mobile operators' revenue by maximizing the amount of the offloaded tasks while decreasing the energy expenditure and time-delays. Considering the dynamic characteristics of wireless environment, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multidimensional continuous variables mixed with discrete variables, traditional RL algorithms are powerless. Therefore, an actor-critic (AC) with eligibility traces algorithm is proposed to resolve the problem. The actor part introduces the parameterized normal distribution to generate the probabilities of continuous stochastic actions, and the critic part employs a linear approximator to estimate the value of states, based on which the actor part updates policy parameters in the direction of performance improvement. Furthermore, an advantage function is designed to reduce the variance of the learning process. Simulation results indicate that the proposed algorithm can find the best strategy to maximize the amount of the tasks executed by the MEC server while decreasing the energy consumption and time-delays.

关键词： Mobile edge computing   DASH   Layered encoding video   Delay analysis   Caching optimization  

摘要： Recently, the demand for video applications has become more and more pressing. To save backhaul bandwidth and reduce the network delay, Mobile Edge Computing (MEC) has arisen as a promising technique in future mobile networks. It can provide the storage, computing and networking resources in close-proximity to the mobile users through deploying multiple MEC servers at mobile edge. However, how to effectively utilize the limited storage capacity of these MEC servers is still the major challenge, especially when Dynamic Adaptive Streaming over HTTP (DASH) is used for video transmission. Recent studies have shown that layered encoding is very suitable for streaming service using DASH standard. Therefore, in our paper, combining with the feature of DASH-based scalable video transmission, we first design a novel multi-server cooperative scheme with software-defined networking (SDN) architecture for future MEC-assisted heterogeneous networks, to effectively manage and schedule the available resources, thus guaranteeing the requested video delivered to the clients with higher quality and lower delay. And then, based on the above scheme, the end-end delay is carefully analyzed for each real video streaming. With a full consideration of the averag end-to-end delay of video streaming and the provided video quality of MEC servers, we finally propose a Dynamic Programming based Adaptive Caching Algorithm (DPACA) to obtain an optimized caching strategy under the constraint of cache storage capacity at each MEC sever. Simulation results show that, our algorithm can improve the cache hit ratio while significantly reducing the end-to-end delay. Moreover, our algorithm has higher flexibility and adaptability.