Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Chen, Xianfu; Zhao, Zhifeng; Wu, Celimuge; Bennis, Mehdi; Liu, Hang; Ji, Yusheng; Zhang, Honggang

doi:10.1109/jsac.2019.2933893

Cited by 122 publications

(75 citation statements)

References 59 publications

(104 reference statements)

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“…Second, by employing transfer learning, knowledge about resource allocation plans for different use cases in one environment can act as useful knowledge in another environment, which can speed up the learning process. Recently, authors in [154] and [155] have applied DRL to network slicing, and the advantages of DRL are demonstrated via simulations.…”

Section: Machine Learning Based Network Slicingmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

462

290

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As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

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Section: Machine Learning Based Network Slicingmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

462

290

View full text Add to dashboard Cite

show abstract

“…Deep RL has been found effective in network slicing [64], integrated design of caching, computing, and communication for software-defined and virtualized vehicular networks [65], multi-tenant cross-slice resource orchestration in cellular RANs [66], proactive channel selection for LTE in unlicensed spectrum [67], and beam selection in millimeter wave MIMO systems in [68]. In this section, we highlight some exemplary cases where deep RL shows impressive promises in wireless resource allocation, in particular, for dynamic spectrum access, power allocation, and joint spectrum and power allocation in vehicular networks.…”

Section: Deep Reinforcement Learning Based Resource Allocationmentioning

confidence: 99%

Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

et al. 2020

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It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, e.g., in the highmobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this paper, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep learning assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

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“…Considering the uncertainty of access conditions and user mobility, some researchers proposed to optimize the long-term network performance by using conventional RL algorithms, such as actor-critic (A3C) and DRL. In [26], the authors designed an on-line scheme based on DRL to accomplish the optimal resource orchestration in the virtualized network. The authors of [27] exploited a collaborative A3C learning framework to manage the resources in RAN slicing.…”

Section: Related Workmentioning

confidence: 99%

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Liu

Feng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

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Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Cited by 122 publications

References 59 publications

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

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