Movable and Reconfigurable Antennas for 6G: Unlocking Electromagnetic-Domain Design and Optimization

TL;DR

This paper addresses how movable antennas (MAs) and reconfigurable antennas (RAs) can unlock electromagnetic-domain design freedom for 6G networks by enabling dynamic control over position, orientation, radiation, polarization, and frequency response. It provides a structured survey of hardware architectures at element- and array-levels, compares design tradeoffs, and outlines deployment considerations, including ISAC integration and passive reconfiguration options. The authors categorize antenna movement/configuration methods into CSI-based optimization, CSI-free design, and AI-driven approaches, and validate gains with field tests in SISO and simulations in multiuser MISO scenarios, showing notable improvements over conventional fixed antennas. They also identify open challenges in advanced architectures, unified modeling, efficient algorithms, prototyping, and standardization to guide future work toward practical MA/RA-enabled 6G systems. The work highlights the potential for interdisciplinary collaboration across electromagnetics, communications, signal processing, and AI to realize flexible, energy-efficient, and high-performance wireless networks.

Abstract

The growing demands of 6G mobile communication networks necessitate advanced antenna technologies. Movable antennas (MAs) and reconfigurable antennas (RAs) enable dynamic control over antenna's position, orientation, radiation, polarization, and frequency response, introducing rich electromagnetic-domain degrees of freedom for the design and performance enhancement of wireless systems. This article overviews their application scenarios, hardware architectures, and design methods. Field test and simulation results highlight their performance benefits over conventional fixed/non-reconfigurable antennas.

Figures (5)

• Figure 1: Typical usage scenarios for MAs and RAs towards 6G.
• Figure 2: Hardware architectures of typical element-level MAs and RAs.
• Figure 3: Hardware architectures of typical array-level MAs and RAs.
• Figure 4: Prototype of the MA-aided SISO receiver and field-test results. (Experimental setup: The system consists of an FPA-based transmitter, an FPA-based jammer, and an MA-based receiver. The carrier frequency is 2.49 GHz (i.e., wavelength $\lambda=12$ cm). The maximum antenna moving region is a 3D cube of size $3.125\lambda=37.53$ cm, which is divided into equally spaced $25 \times 25 \times 25$ grids. The optimal MA position is selected within a 3D subspace (with varying size) of the moving region for maximizing the received SNR or SINR. For multi-FPA receivers, the antennas are spaced by distance $\lambda/2$, and the optimal minimum mean square error (MMSE) beamforming is employed for maximizing the received SINR.)
• Figure 5: Multiuser MISO communication scenario and simulation results based on ray-tracing. (Simulation setup: The single-antenna users are randomly distributed within an urban area of size $220 \text{m} \times 410 \text{m}$ at Clementi, Singapore. Each channel realization is obtained via ray-tracing based on the randomly generated user locations within the target area. The dense uniform planar array (UPA) and sparse UPA schemes adopt $4 \times 4$ FPAs with inter-antenna spacing given by $\lambda/2$ and $2\lambda$, respectively, where $\lambda$ denotes the carrier wavelength and the carrier frequency is $5$ GHz. For MA systems, the BS is equipped with 16 MAs with the size of the two-dimensional (2D) antenna moving region given by $8\lambda \times 8 \lambda$, where the antenna positions are optimized based on gradient ascent for maximizing the ergodic sum rate of users yan2025movable. Each RA has 4 candidate radiation patterns zheng2025tri, with the optimal one selected via alternate refinement for each antenna through exhaustive search. For all schemes, zero-forcing (ZF) beamforming is adopted with the optimal power allocation determined by water-filling.)