Achievable Rate Optimization for Large Flexible Intelligent Metasurface Assisted Downlink MISO under Statistical CSI

TL;DR

This paper develops a robust optimization framework for large-scale FIM-assisted downlink MISO systems using statistical CSI, enabling adaptive wave control without requiring perfect instantaneous CSI, and demonstrates significant performance improvements.

Contribution

It introduces a novel statistical-CSI-based optimization method for FIM-assisted MISO systems, addressing practical channel estimation challenges in large networks.

Findings

Proposed a BCA-based algorithm for joint power and FIM optimization.

Achieved significant sum rate improvements over traditional RAA systems.

Validated the effectiveness of the approach through simulations.

Abstract

The integration of electromagnetic metasurfaces into wireless communications enables intelligent control of the propagation environment. Recently, flexible intelligent metasurfaces (FIMs) have evolved beyond conventional reconfigurable intelligent surfaces (RISs), enabling three-dimensional surface deformation for adaptive wave manipulation. However, most existing FIM-aided system designs assume perfect instantaneous channel state information (CSI), which is impractical in large-scale networks due to the high training overhead and complicated channel estimation. To overcome this limitation, we propose a robust statistical-CSI-based optimization framework for downlink multiple-input single-output (MISO) systems with FIM-assisted transmitters. A block coordinate ascent (BCA)-based iterative algorithm is developed to jointly optimize power allocation and FIM morphing, maximizing the average achievable sum rate. Simulation results show that the proposed statistical-CSI-driven FIM design significantly outperforms conventional rigid antenna arrays (RAAs), validating its effectiveness and practicality.