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dc.contributor.authorLee, Joashen_US
dc.contributor.authorNiyato, Dusiten_US
dc.contributor.authorGuan, Yong Liangen_US
dc.contributor.authorKim, Dong Inen_US
dc.identifier.citationLee, J., Niyato, D., Guan, Y. L. & Kim, D. I. (2021). Learning to schedule joint radar-communication requests for optimal information freshness. 32nd IEEE Intelligent Vehicles Symposium (IV 2021), 1-8.en_US
dc.description.abstractRadar 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.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relationWASP/NTU (M4082187)(4080)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.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Electrical and electronic engineering::Wireless communication systemsen_US
dc.titleLearning to schedule joint radar-communication requests for optimal information freshnessen_US
dc.typeConference Paperen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.conference32nd IEEE Intelligent Vehicles Symposium (IV 2021)en_US
dc.contributor.organizationSungkyunkwan University, South Koreaen_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.contributor.researchContinental-NTU Corporate Laben_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsReinforcement Learningen_US
dc.subject.keywordsDeep Learningen_US
dc.citation.conferencelocationNagoya, Japan (online)en_US
dc.description.acknowledgementThis 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
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