Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150718
Title: Learning to schedule joint radar-communication requests for optimal information freshness
Authors: Lee, Joash
Niyato, Dusit
Guan, Yong Liang
Kim, Dong In
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2021
Source: Lee, 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.
Project: AISG-GC-2019-003 
WASP/NTU (M4082187)(4080) 
RG16/20 
A19D6a0053 
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.
URI: https://hdl.handle.net/10356/150718
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.
Fulltext Permission: embargo_20220702
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
ERI@N Conference Papers
IGS Conference Papers
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