Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 10; pp. 11120 - 11135
• Main Authors: Lee, Joash, Cheng, Yanyu, Niyato, Dusit, Guan, Yong Liang, Gonzalez G., David
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communication; Deep learning; deep reinforcement learning; Graph neural networks; Intelligent vehicles; joint radar-communication; Machine learning; Markov processes; Multiagent systems; Neural networks; Performance enhancement; Protocols; Radar; Radar detection; Reinforcement learning; Resource allocation; Vehicle-to-everything (V2X); Wireless sensor networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3187377

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.