This letter investigates a symbol-level precoder design for movable antenna (MA)-enhanced dual-functional radar-communication (DFRC) systems. To enhance radar sensing capabilities, we formulate an optimization problem aimed at maximizing the minimum radar signal-to-interference-plus-noise ratio (SINR) across multiple targets in a cluttered environment. Our approach jointly designs the space-time transmitted waveforms, receiving filters, and antenna placement. However, the resulting problem is intractable to solve due to practical waveform constraints and the non-linear mapping from antenna positions to the corresponding channel coefficients. To address these challenges, we develop a bi-level optimization framework by leveraging deep reinforcement learning (DRL). Specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm is employed in the outer layer to optimize antenna placement, while penalty convex-concave procedure (CCP) and majorization-minimization (MM) techniques are incorporated in the inner layer for regularizing waveform design. Simulation results demonstrate that the proposed method significantly improves radar SINR and achieves a superior sensing-communication trade-off compared to benchmark schemes.
@article{arxiv.2605.27839,
title = {Movable Antenna Enhanced Dual-Functional Radar-Communication: A Symbol-Level Precoding Approach},
author = {Ran Yang and Ning Wei and Zheng Dong and Chadi Assi and You Li and Fei Xu and Yue Xiu},
journal= {arXiv preprint arXiv:2605.27839},
year = {2026}
}