{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intelligent-resource-allocation-in-joint","title":"Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks","arxiv_id":null,"date":"2022-10-17","proceeding":"IEEE Transactions on Vehicular Technology 2022 10","authors":["Joash Lee","Yanyu Cheng","Dusit Niyato","Yong Liang Guan","David González G."],"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.","url_abs":"https://ieeexplore.ieee.org/document/9921194","url_pdf":"https://ieeexplore.ieee.org/document/9921194","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intelligent-resource-allocation-in-joint","repo_url":"https://github.com/joleeson/Directional-JRC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"distributional-reinforcement-learning","task_name":"Distributional Reinforcement Learning"},{"task_slug":"general-reinforcement-learning","task_name":"General Reinforcement Learning"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"joint-radar-communication","task_name":"Joint Radar-Communication"},{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"gru","method_name":"GRU"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}