Movable-Antenna Assisted Cooperative ISAC in Near-Field Scenarios: Joint Transmit Beamforming and Antenna Positioning

This work investigates a multi-base station (BS) cooperative integrated sensing and communication (ISAC) system assisted by movable antennas (MAs) to enhance spatial degrees of freedom (DoFs). Given the larger antenna apertures introduced by MAs, the traditional far-field channel assumption between transceivers becomes invalid, and thus the channel model is extended to near-field propagation incorporating distance-dependent phase variations. To guarantee cooperative sensing performance, we formulate a robust optimization problem to jointly design multi-BS transmit beamforming and antenna positioning to maximize the worst-case sensing signal-to-noise ratio (SNR), accounting for both target location uncertainty and channel state information (CSI) estimation error. To tackle this nonconvex problem, we first reformulate it using convex hull approximation for location uncertainty and the S-Procedure for CSI error, transforming constraints into tractable forms. We then propose an efficient alternating optimization (AO) framework that decouples the variables into subproblems, which includes updating worst-case weights via closed-form expressions derived from the reverse Hölder inequality, solving the beamforming subproblem via the sequential rank-one constraint relaxation (SROCR) method, and optimizing the antenna positioning subproblem using the minorization-maximization (MM) method to construct surrogates for the nonconvex elements. Numerical results validate the effectiveness of the proposed algorithm and demonstrate that the proposed scheme achieves significant improvements in sensing performance compared to non-cooperative baselines. Furthermore, by exploiting enhanced spatial DoFs through antenna repositioning, the proposed scheme also yields substantial gains over fixed-position antenna (FPA) baselines.