Exploiting Near-Field Dynamics with Movable Antennas to Enhance Discrete Transmissive RIS

TL;DR

This paper introduces a novel base station design combining movable antennas and transmissive reconfigurable intelligent surfaces in the near-field, optimizing their joint configuration to improve wireless performance with low complexity.

Contribution

It proposes a compact BS architecture integrating a movable antenna with a TRIS, and develops an alternating optimization framework for joint positioning and phase configuration.

Findings

Significant performance improvement over fixed antenna arrays.

Repositioning of MA mitigates effects of phase quantization.

Demonstrates hardware complexity vs. performance trade-offs.

Abstract

The design of low-complexity transceivers is crucial for the deployment of next-generation wireless systems. In this work, we combine two emerging concepts, movable antennas (MA) and transmissive reconfigurable intelligent surfaces (TRIS), which have recently attracted significant attention for enhancing wireless communication performance. In particular, we propose a compact base station (BS) architecture that integrates a single MA with a TRIS operating in their near-field region. We address the joint optimization of the MA location and the quantized TRIS phase configuration. Due to the non-convex coupling between spatial positioning and discrete phase constraints, an alternating optimization (AO) framework is developed, where the MA position is updated via gradient ascent (GA) and the TRIS phases are optimized through quantized phase alignment. Simulation results demonstrate that the proposed architecture significantly outperforms conventional BS designs equipped with fixed fully-active antenna arrays under the same channel model and transmit power constraint. Moreover, MA repositioning effectively mitigates the performance degradation caused by discrete TRIS phase quantization in near-field propagation environments. This reveals a favorable trade-off between hardware complexity and spatial signal processing, where the spatial adaptability of the MA can compensate for low-resolution TRIS phase control.