OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications
Jeongyoon Eo, Zhixiong Niu, Wenxue Cheng, Francis Y. Yan, Rui Gao, Jorina Kardhashi, Scott Inglis, Michael Revow, Byung-Gon Chun, Peng Cheng, Yongqiang Xiong
Abstract
With the growing importance of real-time communications (RTC), designing congestion control (CC) algorithms for RTC that achieve high network performance and QoE is gaining attention. Recently, data-driven, reinforcement learning (RL)-based CC algorithms for RTC have shown great potential, outperforming traditional rule-based counterparts. However, there are no open platforms tailored for training, evaluation, and validation of the algorithms that can facilitate this emerging research area.
Topics & Concepts
Reinforcement learningComputer scienceNetwork congestionControl (management)Real-time computingComputer networkDistributed computingArtificial intelligenceNetwork packetNetwork Traffic and Congestion ControlAdvanced Wireless Network OptimizationWireless Networks and Protocols