Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

By Ying He, Nan Zhao, Hongxi Yin. 2018

487 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, methodology): “Particularly, related studies on edge computing and caching, such as [7] [8], have shown that reinforcement”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “[134] proposed a DRL-based approach for optimized orchestration of computing resources in vehicular networks.”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “Instead of adopting traditional RL approaches, in [76], authors propose a novel framework based on DRL for a connected vehicular network to orchestrate computing, networking, and cache resource.”
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks (2019, background): “Other evaluation parameters are listed in Table I [28], [29].”
Deep Reinforcement Learning Based Resource Allocation for V2V Communications (2018, background): “A new deep reinforcement learning based approach has been proposed in [23] to deal with the highly complex joint resource optimization problem in the virtualized vehicular networks.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “In [251], the authors proposed an integrated framework of networking, caching, and computing for connected vehicle networks and”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “can be efficiently solved by deep reinforcement learning at a low computational complexity [153], [346]–[351].”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “Indeed, vehicular edge computing will play a pivotal role to support delay-sensitive as well as future emerging multimedia-rich applications in vehicular networks, which is buttressed by the growing body of literature devoted to the area of content-centric applications of vehicular MEC [83]–[86] whi”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, methodology): “[80], [81] proposed employing DRL to solve the joint resource allocation problem in next-generation vehicular networks.”
Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities (2018, background): “VANET-based ICN influences contents in the stated models in a perfect way among VANET nodes (e.g., RSUs and vehicles).”
Vehicular Edge Computing and Networking: A Survey (2019, methodology): “n and SRSU energy consumption for improving the QoS. F. AI-enabled Ofﬂoading There have been several work to apply AI into task ofﬂoading in VEC. For example, machine learning is widely used [68]. In [65], a resource allocation strategy by joint taking networking, caching and computing into account ”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, methodology): “Focusing on resource allocation in the integrated architecture, two joint optimization models were formulated in [77], [78] to determine the optimal caching and computing decisions, which were then solved by deep reinforcement learning based methods.”
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (2019, methodology): “In [24], a deep RL based method has been proposed to jointly manage the networking, caching, and computing resources in virtualized vehicular networks with information-centric networking and mobile edge computing capabilities.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “, combining Double DQL and Dueling DQL) with more robust performance, can be used to orchestrate available resources to improve the performance of future IoVs [110].”
Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning (2018, background): “Machine learning is an emerging tool to tackle problems encountered in caching, computing and communications in 5G wireless communications [21], [23], [26], [30]–[40].”
Deep Reinforcement Learning for Resource Management in Network Slicing (2018, background): “The well-known success of AlphaGo [14] and following exciting results to apply DRL to solve resource allocation issues in some specific fields like power control [16], green communications [17], cloud radio access networks [18], mobile edge computing and caching [19]–[21], have aroused some research”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “Two schemes have been introduced in [94] to improve stability and performance of the ordinary DQN method.”