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

Ling He, Vaibhav Kumar, Anastasios Papazafeiropoulos, Miaowen Wen, Le-Nam Tran, Marwa Chafii

TL;DR

This work proposes a robust statistical-CSI framework for downlink MISO systems aided by flexible intelligent metasurfaces (FIMs) operating in sub-6 GHz. It models the FIM-induced spatial correlation and derives an MMSE-based channel estimator, yielding a rate expression under UaTF with MRT precoding, e.g., $R_k(p,y)=\bar{\tau}\ln(1+\gamma_k)$ where $\gamma_k=S_k/I_k$. A block-coordinate ascent algorithm is developed to jointly optimize transmit power $\mathbf{p}$ and morphing $\mathbf{y}$, alternating with SCA for power updates and augmented-Lagrangian gradient methods for morphing, achieving a stationary solution without instantaneous CSI. Simulations show substantial gains over rigid arrays and equal-power baselines across varied morphing ranges, spacing, and user distributions, highlighting the practical potential of FIMs for adaptive, high-throughput wireless environments.

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.

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

TL;DR

This work proposes a robust statistical-CSI framework for downlink MISO systems aided by flexible intelligent metasurfaces (FIMs) operating in sub-6 GHz. It models the FIM-induced spatial correlation and derives an MMSE-based channel estimator, yielding a rate expression under UaTF with MRT precoding, e.g., where . A block-coordinate ascent algorithm is developed to jointly optimize transmit power and morphing , alternating with SCA for power updates and augmented-Lagrangian gradient methods for morphing, achieving a stationary solution without instantaneous CSI. Simulations show substantial gains over rigid arrays and equal-power baselines across varied morphing ranges, spacing, and user distributions, highlighting the practical potential of FIMs for adaptive, high-throughput wireless environments.

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.
Paper Structure (12 sections, 2 theorems, 24 equations, 4 figures, 1 algorithm)

This paper contains 12 sections, 2 theorems, 24 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Estimation
  4. Downlink Data Transmission and Reception
  5. Problem Formulation
  6. BCA-Based Proposed Solution
  7. Optimal Power Allocation for a Fixed Morphing
  8. Optimal Morphing for a Fixed Power Allocation
  9. Simulation Results
  10. Conclusion
  11. Proof of Theorem \ref{['thm:convex_reform']}
  12. Proof of Theorem \ref{['thm:grad_theorem']}

Key Result

Theorem 1

At iteration $\varkappa$, a convex surrogate of eq:P2 can be formulated as where $R_{k}(\widehat{\mathbf{p}};\widehat{\mathbf{p}}^{(\varkappa)})$ denotes the concave lower-bound approximation of $R_{k}(\mathbf{p})$ around the current iterate $\widehat{\mathbf{p}}^{(\varkappa)}$.

Figures (4)

  • Figure 1: A large FIM-aided MISO system with multiple users.
  • Figure 2: Impact of the transmit power budget on the average achievable sum rate.
  • Figure 3: Impact of the number of transmit elements on the average achievable sum rate.
  • Figure 4: Impact of the morphing range on the average achievable sum rate.

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2