Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

Huang, Liang; Xu, Feng; Feng, Ao; Huang, Yupin; Qian, Li Ping

doi:10.1007/s11036-018-1177-x

Cited by 125 publications

(72 citation statements)

References 28 publications

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“…There exists limited work on deep reinforcement learning-based offloading for MEC networks [18]- [22]. By taking advantage of parallel computing, [19] proposed a distributed deep learning-based offloading (DDLO) algorithm for MEC networks. For an energy-harvesting MEC networks, [20] proposed a deep Q-network (DQN) based offloading policy to optimize the computational performance.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Huang

Zhang

2020

IEEE Trans. on Mobile Comput.

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721

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Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.Index Terms-Mobile-edge computing, wireless power transfer, reinforcement learning, resource allocation. ! • L. Huang is with the College

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Huang

Zhang

2020

IEEE Trans. on Mobile Comput.

Self Cite

721

313

View full text Add to dashboard Cite

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“…B ENEFITING from the improvement of computing power and big data, deep learning has achieved unprecedented development in many applications, i.e., speech and audio processing [1], natural language processing [2], object detection [3], and so on. In recent years, it also achieves dramatic development in the field of wireless communications, e.g., modulation classification [4], symbol detection [5], end-to-end communication [6], and mobile edge computing [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation for Deep Learning-Based Radio Modulation Classification

et al. 2020

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Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-ofthe-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning algorithm. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and shorter the classification response time.

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“…Finally, the structure of DL is constructed. DL has been used for task offloading in EC [8], [122], [123]. • Reinforcement learning (RL) [124], [125] determines an optimal policy dictating which actions to take at certain states to achieve the highest possible reward.…”

Section: Lbsmentioning

confidence: 99%

“…Huang et al [122] proposed distributed DL-based offloading in MEC. They formulated joint offloading decision and bandwidth allocation as a mixedinteger programming problem.…”

Section: ) Lbs For Joint Issuesmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

Cited by 125 publications

References 28 publications

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Data Augmentation for Deep Learning-Based Radio Modulation Classification

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

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