Joint Antenna Position and Beamforming Optimization with Self-Interference Mitigation in MA-ISAC System

TL;DR

This work tackles joint optimization of transmit/receive beamforming and movable-antenna positions in a full-duplex monostatic integrated sensing and communication (ISAC) system. The self-interference (SI) channel is modeled in the near-field as a function of transmitter and receiver antenna positions, enabling active suppression through position tuning and beamforming. A fractional programming framework combined with alternating optimization decouples the non-convex problem, and a two-stage coarse-to-fine-grained search (CFGS) is proposed to obtain high-quality antenna placements; closed-form beamformers are derived via KKT conditions, and auxiliary variables (\boldsymbol{\mu}, \boldsymbol{\xi}^c, \boldsymbol{\xi}^s) are updated within the FP framework. Numerical results show CFGS-MA significantly outperforms fixed-position designs across power levels, antenna configurations, movable ranges, and SI levels, validating movable antennas as a practical means to boost ISAC performance under strong SI suppression. This work demonstrates the practical value of movable antennas for robust ISAC in FD monostatic deployments with near-field SI modeling.

Abstract

Movable antennas (MAs) have demonstrated significant potential in enhancing the performance of integrated sensing and communication (ISAC) systems. However, the application in the integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To address this research gap, we develop an MA-ISAC model within a monostatic framework, where the self-interference channel is modeled in the near field and characterized by antenna position vectors. This model allows us to investigate the use of MAs with the goal of maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To overcome this, we employ fractional programming (FP) to propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions for both transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained search (CFGS) approach is employed to determine the high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

Figures (5)

• Figure 1: System model of the monostatic MA-ISAC system.
• Figure 2: Movable antenna model at Tx and Rx.
• Figure 3: ISAC performance with different transmit power, $X_{\max}=12\lambda$, $Y_{\max}=8\lambda$, $\varpi_s=0.5$ and $\varpi_c=0.5$, $r_{0}=20\lambda$.
• Figure 4: ISAC performance with different antenna movable range, $P_0=30$ dBm, $N_T=8$, $N_R=4$, $\varpi_s=0.5$ and $\varpi_c=0.5$, $r_{0}=20\lambda$.
• Figure 5: Trade-off between communication and sensing with different SI, $P_0=30$ dBm, $N_T=8$, $N_R=4$, $X_{\max}=12\lambda$ and $Y_{\max}=8\lambda$.