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https://hdl.handle.net/10356/150718
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DC Field | Value | Language |
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dc.contributor.author | Lee, Joash | en_US |
dc.contributor.author | Niyato, Dusit | en_US |
dc.contributor.author | Guan, Yong Liang | en_US |
dc.contributor.author | Kim, Dong In | en_US |
dc.date.accessioned | 2021-07-02T01:20:53Z | - |
dc.date.available | 2021-07-02T01:20:53Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Lee, J., Niyato, D., Guan, Y. L. & Kim, D. I. (2021). Learning to schedule joint radar-communication requests for optimal information freshness. 2021 IEEE Intelligent Vehicles Symposium (IV), 8-15. https://dx.doi.org/10.1109/IV48863.2021.9575131 | en_US |
dc.identifier.isbn | 978-1-7281-5394-0 | - |
dc.identifier.uri | https://hdl.handle.net/10356/150718 | - |
dc.description.abstract | Radar detection and communication are two of several sub-tasks essential for the operation of next-generation autonomous vehicles (AVs). The former is required for sensing and perception, more frequently so under various unfavorable environmental conditions such as heavy precipitation; the latter is needed to transmit time-critical data. Forthcoming proliferation of faster 5G networks utilizing mmWave is likely to lead to interference with automotive radar sensors, which has led to a body of research on the development of Joint Radar Communication (JRC) systems and solutions. This paper considers the problem of time-sharing for JRC, with the additional simultaneous objective of minimizing the average age of information (AoI) transmitted by a JRC-equipped AV. We formulate the problem as a Markov Decision Process (MDP) where the JRC agent determines in a real-time manner when radar detection is necessary, and how to manage a multi-class data queue where each class represents different urgency levels of data packets. Simulations are run with a range of environmental parameters to mimic variations in real-world operation. The results show that deep reinforcement learning allows the agent to obtain good results with minimal a priori knowledge about the environment. | en_US |
dc.description.sponsorship | Agency for Science, Technology and Research (A*STAR) | en_US |
dc.description.sponsorship | Ministry of Education (MOE) | en_US |
dc.description.sponsorship | Nanyang Technological University | en_US |
dc.description.sponsorship | National Research Foundation (NRF) | en_US |
dc.language.iso | en | en_US |
dc.relation | AISG-GC-2019-003 | en_US |
dc.relation | WASP/NTU (M4082187)(4080) | en_US |
dc.relation | RG16/20 | en_US |
dc.relation | A19D6a0053 | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.subject | Engineering::Electrical and electronic engineering::Wireless communication systems | en_US |
dc.title | Learning to schedule joint radar-communication requests for optimal information freshness | en_US |
dc.type | Conference Paper | en |
dc.contributor.school | Interdisciplinary Graduate School (IGS) | en_US |
dc.contributor.school | School of Computer Science and Engineering | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.contributor.conference | 2021 IEEE Intelligent Vehicles Symposium (IV) | en_US |
dc.contributor.organization | Sungkyunkwan University, South Korea | en_US |
dc.contributor.research | Energy Research Institute @ NTU (ERI@N) | en_US |
dc.contributor.research | Continental-NTU Corporate Lab | en_US |
dc.identifier.doi | 10.1109/IV48863.2021.9575131 | - |
dc.description.version | Accepted version | en_US |
dc.identifier.spage | 8 | en_US |
dc.identifier.epage | 15 | en_US |
dc.subject.keywords | Reinforcement Learning | en_US |
dc.subject.keywords | Deep Learning | en_US |
dc.citation.conferencelocation | Nagoya, Japan (online) | en_US |
dc.description.acknowledgement | This research is supported, in part, by Alibaba Innovative Research (AIR) Program, Alibaba-NTU Singapore Joint Research Institute, National Research Foundation, Singapore, under AI Singapore Programme (AISG-GC-2019-003), WASP/NTU grant M4082187 (4080), Singapore Ministry of Education Tier 1 (RG16/20), A*STAR under its RIE2020 Advanced Manufacturing and Engineering Industry Alignment Fund — Pre Positioning (A19D6a0053), and Ministry of Science and ICT, Korea, under the ICT Creative Consilience program (IITP-2020-0-01821) supervised by the IITP. Any opinions, findings, conclusions or recommendations in this material are those of the authors and do not reflect the views of the mentioned organizations. | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | EEE Conference Papers ERI@N Conference Papers IGS Conference Papers SCSE Conference Papers |
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JRC_AoI_IV_2021__Copy_for_DR_NTU_ (002).pdf | 1.83 MB | Adobe PDF | View/Open |
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