Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication

Published:2022-04-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Wireless Communications and Networking Conference (WCNC)
language:
Short-container-title:

Author:

Lee Joash¹,Cheng Yanyu²,Niyato Dusit²,Guan Yong Liang³,David Gonzalez G.⁴

Affiliation:

1. Nanyang Technological University,Energy Research Institute @ NTU,Singapore

2. Nanyang Technological University,School of Computer Science and Engineering,Singapore

3. Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore

4. Continental AG,Advanced Connectivity Technologies Group,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9771381/9771540/09771580.pdf?arnumber=9771580

Reference20 articles.

1. Minimizing age of information in vehicular networks

2. Autonomous driving at Ulm University: A modular, robust, and sensor-independent fusion approach

3. Next generation radar sensors in automotive sensor fusion systems

4. Real-time sampling rate adaptation based on continuous risk level evaluation in wireless body sensor networks

5. On the Feasibility of Distributed Sampling Rate Adaptation in Heterogeneous and Collaborative Wireless Sensor Networks

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Edge Computing and Wireless Power Transfer for Integrated Radar and Communication-Equipped IoT Systems;IEEE Wireless Communications Letters;2024-09

2. Deep Reinforcement Learning-based Beamforming Design in ISAC-assisted Vehicular Networks;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

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