Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164646
Title: Intelligent resource allocation in joint radar-communication with graph neural networks
Authors: Lee, Joash
Cheng, Yanyu
Niyato, Dusit
Guan, Yong Liang
González G., David
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Lee, J., Cheng, Y., Niyato, D., Guan, Y. L. & González G., D. (2022). Intelligent resource allocation in joint radar-communication with graph neural networks. IEEE Transactions On Vehicular Technology, 71(10), 11120-11135. https://dx.doi.org/10.1109/TVT.2022.3187377
Project: IAF-ICP
FCP-NTU-RG-2021-014
AISG2-RP-2020-019
RG16/20
Journal: IEEE Transactions on Vehicular Technology
Abstract: Autonomous vehicles produce high data rates of sensory information from sensing systems. To achieve the advantages of sensor fusion among different vehicles in a cooperative driving scenario, high data-rate communication becomes essential. Current strategies for joint radar-communication (JRC) often rely on specialized hardware, prior knowledge of the system model, and entail diminished capability in either radar or communication functions. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help an intelligent vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a generalized form of the Markov Decision Process (MDP). We identify deep reinforcement learning algorithms (DRL) and algorithmic extensions suitable for solving our JRC problem. For multi-agent scenarios, we introduce a Graph Neural Network (GNN) framework via a control channel. This framework enables modular and fair comparisons of various algorithmic extensions. Our experiments show that DRL results in superior performance compared to non-learning algorithms. Learning of inter-agent coordination in the GNN framework, based only on the Markov task reward, further improves performance.
URI: https://hdl.handle.net/10356/164646
ISSN: 0018-9545
DOI: 10.1109/TVT.2022.3187377
Schools: School of Electrical and Electronic Engineering 
School of Computer Science and Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
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