Flexible Beamforming for Movable Antenna-Enabled Integrated Sensing and Communication

TL;DR

This paper introduces a flexible beamforming approach for integrated sensing and communication systems using movable antennas, optimizing both antenna positions and beamforming to significantly improve performance.

Contribution

It proposes a joint optimization framework for beamforming and antenna positioning in ISAC networks with movable antennas, employing fractional programming and gradient methods.

Findings

Achieves up to 59.8% performance gain over fixed arrays.

Demonstrates the effectiveness of movable antennas in enhancing ISAC performance.

Provides a novel algorithm combining FP and SPGA for joint optimization.

Abstract

This paper investigates flexible beamforming design in an integrated sensing and communication (ISAC) network with movable antennas (MAs). A bistatic radar system is integrated into a multi-user multiple-input-single-output (MU-MISO) system, with the base station (BS) equipped with MAs. This enables array response reconfiguration by adjusting the positions of antennas. Thus, a joint beamforming and antenna position optimization problem, namely flexible beamforming, is proposed to maximize communication rate and sensing mutual information (MI). The fractional programming (FP) method is adopted to transform the non-convex objective function, and we alternatively update the beamforming matrix and antenna positions. Karush-Kuhn-Tucker (KKT) conditions are employed to derive the close-form solution of the beamforming matrix, while we propose an efficient search-based projected gradient ascent (SPGA) method to update the antenna positions. Simulation results demonstrate that MAs significantly enhance the ISAC performance when employing our proposed algorithm, achieving a 59.8% performance gain compared to fixed uniform arrays.