强化学习
计算机科学
全球定位系统
链路自适应
编码(社会科学)
更安全的
服务质量
实时计算
信道状态信息
频道(广播)
适应(眼睛)
机器学习
计算机网络
衰退
无线
电信
物理
光学
统计
计算机安全
数学
作者
Somnath Banerjee,Joy Bose,Sleeba Paul Puthepurakel,Pratyush Kiran Uppuluri,Subhadip Bandyopadhyay,Y. Dwarakanadha Reddy,H. G. Ranjani
标识
DOI:10.1145/3564121.3564122
摘要
For autonomous driving, safer travel, and fleet management, Vehicle-to-Vehicle (V2V) communication protocols are an emerging area of research and development. State-of-the-art techniques include machine learning (ML) and reinforcement learning (RL) to adapt modulation and coding rates as the vehicle moves. However, channel state estimations are often incorrect and rapidly changing in a V2V scenario. We propose a combination of input features, including (a) sensor inputs from other parameters in the vehicle, such as speed and global positioning system (GPS), (b) estimation of interference and load for each of the vehicles, and (c) channel state estimation to find the optimal rate that would maximize Quality-of-Service. Our model uses an ensemble of RL-agents to predict trends in the input parameters and to find the inter-dependencies of these input parameters. An RL agent then utilizes these inputs to find the best modulation and coding rate as the vehicle moves. We demonstrate our results through prototype experiments using real data collected from customer networks.
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