Reconfigurable Beamforming for Automotive Radar Sensing and Communication: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Reconfigurable Beamforming for Automotive Radar Sensing and Communication: A Deep Reinforcement Learning Approach

Published:2024 Issue: Volume:1 Page:124-138
ISSN:2836-2071
Container-title:IEEE Journal of Selected Areas in Sensors
language:
Short-container-title:IEEE J. Sel. Areas Sensors

Author:

Xu Lifan¹^ORCID,Sun Shunqiao¹^ORCID,Zhang Yimin D.²^ORCID,Petropulu Athina P.³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USA

2. Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA

3. Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA

Funder

National Science Foundation

ONR

Alabama Transportation Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/10057477/10185156/10605037-aam.pdf

Reference77 articles.

1. A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication

2. Joint Antenna Selection and Beamforming in Integrated Automotive Radar Sensing-Communications with Quantized Double Phase Shifters

3. Securing Vehicle-to-Everything (V2X) Communication Platforms

4. Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G

5. Intrapulse Radar-Embedded Communications

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