A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication
A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication
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DOI：
10.1109/sam53842.2022.9827815
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Lifan Xu;Ru-dan Zheng;Shunqiao Sun
中科院分区：
文献类型：
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作者：
Lifan Xu;Ru-dan Zheng;Shunqiao Sun
我们提出了一种深度强化学习方法来设计具有集成传感和通信功能的汽车雷达系统。在所提出的系统中，使用带有量化移相器的稀疏发射阵列来进行发射波束成形，以增强雷达传感和通信的性能。通过与环境的交互，汽车雷达学习反映雷达感知模式下主瓣峰值和峰值旁瓣电平之间的差异或通信模式下通信用户反馈的奖励，并智能地调整其波束形成矢量。引入基于Wolpertinger策略的动作批评网络进行波束成形矢量学习，解决了巨大的波束成形动作空间带来的维数灾难。
We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space.