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

TL;DR

This paper introduces a joint optimization framework for antenna positioning and beamforming in full-duplex MA-ISAC systems, leveraging fractional programming and a novel search method to improve sensing and communication performance.

Contribution

It develops a new MA-ISAC model with joint antenna position and beamforming optimization, including a fractional programming-based algorithm and a coarse-to-fine search for near-optimal antenna placement.

Findings

MAs with strong SIC significantly improve system performance.

The proposed algorithm outperforms fixed-position antenna designs.

Joint optimization enhances sensing and communication capabilities.

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.