Polarforming Design for Movable Antenna Systems

TL;DR

The work addresses maximizing the rate of a SISO MA-enabled link by jointly optimizing movable antenna positions ${\bf t}$, ${\bf r}$ and polarforming phases $(\theta,\phi)$ under a polarized channel model. It develops a polarized channel expression $h({\bf t},{\bf r},\theta,\phi) = {\bf g}({\bf r},\phi)^H {\bf \Lambda} {\bf f}({\bf t},\theta)$ with a path polarization response matrix ${\bf \Lambda}$ and solves the nonconvex problem via alternating optimization with successive convex approximation (SCA) to obtain a low-complexity convex surrogate. The contributions include (i) a PS-based polarization-reconfigurable antenna model and polarized MA channel formulation, (ii) an SCA-based alternating optimization algorithm with convergence guarantees and polynomial complexity, and (iii) numerical results showing MA-PF outperforming fixed-position or fixed-polarization baselines and robustness to channel variations. This work demonstrates substantial gains by exploiting both spatial and polarization degrees of freedom, offering a practical pathway to mitigate depolarization and adapt to fading in MA-enabled wireless links.

Abstract

Polarforming has emerged as a promising technique to enable the antenna to shape its polarization into a desired state for aligning with that of the received electromagnetic (EM) wave or reconfiguring that of the transmitted EM wave. In this letter, we investigate polarforming design for the movable antenna (MA)-enabled communication system. Specifically, we consider a single-input single-output (SISO) system with reconfigurable antenna positions and polarizations to leverage both spatial and polarization degrees of freedom (DoFs). First, we present a polarized channel model and characterize the channel response as a function of antenna positions and polarforming phase shifts. To maximize the achievable rate of the proposed system, we then develop a successive convex approximation (SCA)-based optimization algorithm by iteratively optimizing the antenna positions and phase shifts at both the transmitter and receiver. Furthermore, simulation results demonstrate the performance gains of the proposed system over conventional systems in mitigating channel depolarization and adapting to channel fading.

Figures (5)

• Figure 1: A schematic of polarforming design for the MA-enabled communication system.
• Figure 2: Illustration of the coordinates and spatial angles for the transmit and receive regions.
• Figure 3: Convergence behavior of Algorithm \ref{['alg']}.
• Figure 4: Achievable rate versus SNR.
• Figure 5: Impact of the number of paths, moving region size, and Rician factor on the performance of the considered schemes.