Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach

By Ying He, F. Yu, Nan Zhao, Victor C. M. Leung, Hongxi Yin. IEEE Communications Magazine, 2017

299 citations recorded by the source corpus.

Subfields: Building and Construction, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/f8f756d29245b15c191368bfc04eea4ff35c1cc0

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Citations in the corpus

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “, [92]–[94], has also been generalized to smart city applications in [96], which necessitates dynamic orchestration of networking, caching, and computation to meet different servicing requirements.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “In [273], an SDNenabled edge computing network catering for smart cities is investigated.”
AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT (2019, background): “communications [8] Reinforcement learning Allocate resources adaptive to variable network conditions in social”
Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach (2018, methodology): “Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function [17], and has been exploited in wireless networks to achieve automatic resource allocation [18], [19].”
Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution (2021, methodology): “However, DQN speeds up the B&B algorithm by only 2.06 times and the system performance drops 3.94%.”
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”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “The edge caching problem is also addressed in [403], where He et al.”
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].”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “Later, literature [141] proposed a big data deep reinforcement learning approach to enable dynamic orchestration of networking, caching, and computing resources for improving the performance of applications in smart cities.”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, background): “The rapid development of deep learning has led to some successful applications in communications [23]–[25], including modulation classification.”
Deep Reinforcement Learning for Cyber Security (2019, background): “Applications Goals/Objectives Algorithms States Actions Rewards Virtualized smart city network resource allocation [44] Optimally assign virtualized resources to a specific user.”