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

Huang, Liang; Bi, Suzhi; Zhang, Ying Jun

doi:10.1109/tmc.2019.2928811

Cited by 721 publications

(314 citation statements)

References 31 publications

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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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Section: Introductionmentioning

confidence: 99%

Data Augmentation for Deep Learning-Based Radio Modulation Classification

et al. 2020

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“…The authors of [17] proposed an efficient reinforcement learning-based resource management algorithm to incorporate renewable energy into MEC systems. More recently, a deep reinforcement learning framework for task offloading was studied in a single-AP scenario [18].…”

Section: Related Workmentioning

confidence: 99%

“…With this scheme, there is no exploration and the output of the DNN will be used as the user association scheme. Some similar studies focused on offloading and resource allocation with a single AP [8,18]. The implicit assumption on the user association is that the users are served by the nearest AP or the AP with the highest large-scale channel gain.…”

Section: DL Algorithm For User Associationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Dong

She

Hardjawana

et al. 2019

IEEE Trans. Wireless Commun.

191

119

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In this work, we consider a mobile edge computing system with both ultra-reliable and lowlatency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. Simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

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“…Deep reinforcement learning (DRL) is a goal-oriented algorithm which can learn an optimal policy by using DNN for offloading decision making [16]. In this paper, similarly, DRL is applied to predict computation offloading, i.e., P 1, while convex optimization technique is used to solve P 2 and evaluate the reward of DRL, which guarantees that all the physical constraints are satisfied.…”

Section: The Online Joint Resource Scheduling Framework (Ojrs)mentioning

confidence: 99%

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

Jiang

Wang

Dong

et al. 2020

IEEE Internet Things J.

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An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment.Index Terms-Stacked auto encoder, deep reinforcement learning, adaptive simulated annealing, mobile edge computing.

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

Cited by 721 publications

References 31 publications

Data Augmentation for Deep Learning-Based Radio Modulation Classification

Data Augmentation for Deep Learning-Based Radio Modulation Classification

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

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