【调研】DRL优化网络通信领域顶会及期刊

1 顶会及期刊

计算机网络方面国际三大顶会：

• ACM SigComm, Association for Computing Machinery Special Interest Group on Data Communication, 国际计算机学会网络通信领域顶级会议（三大A类会议）
• ACM MobiCom, The Annual International Conference on Mobile Computing and Networking，是ACM SIGMobile开办的无线和移动通信领域的顶会
• IEEE InfoCom, IEEE Conference on Computer Communications, IEEE计算机通信会议

期刊：

• IEEE Access 速度快

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中国计算机学会推荐国际学术刊物

2 强化学习与网络优化相关论文

2.1 网络层

2.1.1 Routing

• Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
  code: https://github.com/knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization

2.2 传输层

2.2.1 Congestion Control (CC)

• QTCP: Adaptive congestion control with reinforcement learning

• TCP ex Machina: Computer-Generated Congestion Control

• An Experimental Study of the Learnability of Congestion Control

• Internet Congestion Control via Deep Reinforcement Learning

• TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning

• Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning

• SmartCC: A Reinforcement Learning Approach for Multipath TCP Congestion Control in Heterogeneous

• QTCP: Adaptive Congestion Control with Reinforcement Learning

• How network monitoring and reinforcement learning can improve tcp fairness in wireless multi-hop networks

• Improving the Congestion Control Performance for Mobile Networks in High-Speed Railway via Deep R

• PCC Vivace: Online-Learning Congestion Control

• Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network

• Multi-Armed Bandit Congestion Control in Multi-Hop Infrastructure Wireless Mesh Networks

• Improving TCP Congestion Control with Machine Intelligence

• Multi-Armed Bandit in Action: Optimizing Performance in Dynamic Hybrid Networks

• Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
  code https://github.com/dcgym/iroko

2.2.2 mptcp

• A Reinforcement Learning Approach for Multipath TCP Data Scheduling
• ReLeS: A Neural Adaptive Multipath Scheduler based on Deep Reinforcement Learning
• SmartCC: A Reinforcement Learning Approach for Multipath TCP Congestion Control in Heterogeneous Networks
• Experience-driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning
• Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments

2.3 应用层

2.3.1 dash

• D-DASH: A deep Q-learning framework for DASH video streaming
• Online learning adaptation strategy for DASH clients
• Continual learning improves Internet video streaming
• Neural Adaptive Video Streaming with Pensieve
• Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming
• PiTree: Practical Implementation of ABR Algorithms Using Decision Trees
• HotDASH: Hotspot Aware Adaptive Video Streaming using Deep Reinforcement Learning (code:https://github.com/SatadalSengupta/hotdash)

2.3.2 multimedia streaming

• QARC: Video Quality Aware Rate Control for Real-Time Video Streaming based on Deep Reinforcement Learning
• LEAP: Learning-Based Smart Edge with Caching and Prefetching for Adaptive Video Streaming

2.4 综述

• Applications of Deep Reinforcement Learning in Communications and Networking A Survey
• 深度强化学习在典型网络系统中的应用综述