Doubly-Dynamic ISAC Precoding for Vehicular Networks: A Constrained Deep Reinforcement Learning (CDRL) Approach

TL;DR

This paper introduces a constrained deep reinforcement learning approach for real-time ISAC precoding in vehicular networks with double dynamics, outperforming traditional optimization methods.

Contribution

It develops a novel CDRL framework with primal dual-deep deterministic policy gradient and Wolpertinger architecture tailored for dynamic vehicular environments.

Findings

The proposed method adapts to rapid channel and target movements.

It reduces complexity compared to optimization-based solutions.

Experimental results validate its superior performance.

Abstract

Integrated sensing and communication (ISAC) technology is essential for supporting vehicular networks. However, the communication channel in this scenario exhibits time variations, and the potential targets may move rapidly, resulting in double dynamics. This nature poses a challenge for real-time precoder design. While optimization-based solutions are widely researched, they are complex and heavily rely on perfect channel-related information, which is impractical in double dynamics. To address this challenge, we propose using constrained deep reinforcement learning to facilitate dynamic updates to the ISAC precoder. Additionally, the primal dual-deep deterministic policy gradient and Wolpertinger architecture are tailored to efficiently train the algorithm under complex constraints and varying numbers of users. The proposed scheme not only adapts to the dynamics based on observations but also leverages environmental information to enhance performance and reduce complexity. Its superiority over existing candidates has been validated through experiments.