Polarforming Antenna Enhanced Sensing and Communication: Modeling and Optimization

TL;DR

This paper introduces a novel polarforming antenna system that adaptively controls polarization and position to enhance wireless sensing and communication, with a joint optimization framework validated by simulations.

Contribution

The paper presents a new polarforming antenna model, a two-timescale ISAC protocol, and a structured localization method based on PARAFAC tensor modeling, advancing integrated sensing and communication.

Findings

Polarforming improves channel reconfiguration and performance.

Joint optimization of antenna placement and polarforming enhances sum-rate.

Simulation confirms the effectiveness of the proposed methods.

Abstract

In this paper, we propose a novel polarforming antenna (PA) to achieve cost-effective wireless sensing and communication. Specifically, the PA can enable polarforming to adaptively control the antenna's polarization electrically as well as tune its position/rotation mechanically, so as to effectively exploit polarization and spatial diversity to reconfigure wireless channels for improving sensing and communication performance. We study an PA-enhanced integrated sensing and communication (ISAC) system that utilizes user location sensing to facilitate communication between an PA-equipped base station (BS) and PA-equipped users. First, we model the PA channel in terms of transceiver antenna polarforming vectors and antenna positions/rotations. We then propose a two-timescale ISAC protocol, where in the slow timescale, user localization is first performed, followed by the optimization of the BS antennas' positions and rotations based on the sensed user locations; subsequently, in the fast timescale, transceiver polarforming is adapted to cater to the instantaneous channel state information (CSI), with the optimized BS antennas' positions and rotations. We propose a new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the PA channel across different polarizations based on the parallel factor (PARAFAC) tensor model. Moreover, we maximize the achievable average sum-rate of users by jointly optimizing the fast-timescale transceiver polarforming, including phase shifts and amplitude variations, along with the slow-timescale antenna rotations and positions at the BS. Simulation results validate the effectiveness of polarforming-based localization algorithm and demonstrate the performance advantages of polarforming, antenna placement, and their joint design.