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

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

17 Oct 2022IEEE Transactions on Vehicular Technology 2022 10archive 2025-07-28

Joash Lee, Yanyu Cheng, Dusit Niyato, Yong Liang Guan, David González G.

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.

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joleeson/Directional-JRC pytorch report

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Tasks

Autonomous Driving Autonomous Vehicles Deep Reinforcement Learning Distributional Reinforcement Learning General Reinforcement Learning Graph Neural Network Joint Radar-Communication Sensor Fusion

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Adam Attention GRU Graph Neural Network Linear Layer Multi-Head Attention Residual Connection Softmax Transformer

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