Resource Allocation in Software-Defined and Information-Centric Vehicular Networks with Mobile Edge Computing

He, Ying; Liang, Chengchao; Zhang, Zheng; Yu, F. Richard; Zhao, Nan; Yin, Hujun; Zhang, Yanhua

doi:10.1109/vtcfall.2017.8288203

Cited by 29 publications

(29 citation statements)

References 18 publications

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“…The DQL scheme proposed in [84] has been applied to improve the performance of Vehicular Ad doc NETworks (VANETs) in [85]- [87]. The network model includes multiple BSs, Road Side Units (RSUs), MEC servers, and content servers.…”

Section: A Wireless Proactive Cachingmentioning

confidence: 99%

“…The vehicles request for video contents that can be cached at the BSs or retrieved from remote content servers. The authors in [85] formulate the resource allocation problem as a joint optimization of caching, networking, and computing, e.g., compressing and encoding operations of the video contents. The system states include the CSI from each BS, the computational capability, and cache size of each MEC/content server.…”

Section: A Wireless Proactive Cachingmentioning

confidence: 99%

“…Secondly, the dueling DQN approach is also integrated in the design with the intuition that it is not always necessary to estimate the reward by taking some action. The state-action Qvalue in dueling DQN is decomposed into one value function representing the reward in the current state, and the advantage optimize cache hit rate [70], cache expiration time [74], interference alignment [76]- [78], Quality of Experience [79], [81], energy efficiency [84], resource allocation [85]- [87], traffic latency, or redundancy [89], [91]. function that measures the relative importance of a certain action compared with other actions.…”

Section: A Wireless Proactive Cachingmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,275

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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Section: A Wireless Proactive Cachingmentioning

confidence: 99%

Section: A Wireless Proactive Cachingmentioning

confidence: 99%

Section: A Wireless Proactive Cachingmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,275

439

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show abstract

“…Because the parameters of the satellite network are designed in advance, the location information of each virtual node is determined when the parameters of the constellation are determined, that is, the latitude and longitude range of each virtual node is known. As shown in Figure 2, when the partial parameters of the constellation are (5,12), the sphere of the earth is divided into logical grids of 2*5*12. The definition of the virtual node partition rule is as follows:…”

Section: Virtual Node Partitionmentioning

confidence: 99%

“…It cannot improve the speed of data transmission in nature in the face of the new big data network environment . In the SDN‐based information centric network, the application layer implements such services as name resolution, name routing, and content caching in the ICN idea and implements service deployment through an open interface provided by the control layer . However, the information centric network based on SDN is mainly based on the ground network.…”

Section: Introductionmentioning

confidence: 99%

Routing algorithm design of satellite network architecture based on SDN and ICN

Liu

Zhu

Zhang

et al. 2019

Satell Commun Network

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Summary In view of the problems of low routing efficiency, complex control process, and difficult network management in big data environment in the traditional integrated space‐terrestrial network, in the paper, we propose a satellite network architecture called software‐defined information centric satellite networking (SDICSN) based on software‐defined networking (SDN) and information‐centric networking (ICN), and we design a virtual node matrix routing algorithm (VNMR) under the SDICSN architecture. The SDICSN architecture realizes the flexibility of network management and business deployment through the features of the separation of forwarding and controlling by the SDN architecture and improves the response speed of requests in the network by the centric of “content” as the ICN idea. According to the periodicity and predictability of the satellite network, the VNMR algorithm obtains the routing matrix through the relative orientation of the source and destination nodes, thus reducing the spatial complexity of the input matrix of the Dijkstra algorithm and then reducing the time complexity of the routing algorithm. For forwarding information base (FIB), the mechanism of combination of event driven and polling can be quickly updated in real time. Finally, the advantages of the SDICSN architecture in routing efficiency, request delay, and request aggregation are verified by simulation.

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DRL at the Application and Service Layer

2023

Deep Reinforcement Learning for Wireless Communications and Networking

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Resource Allocation in Software-Defined and Information-Centric Vehicular Networks with Mobile Edge Computing

Cited by 29 publications

References 18 publications

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Routing algorithm design of satellite network architecture based on SDN and ICN

DRL at the Application and Service Layer

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