Weighted Sum-Rate Enhancement for Flexible Intelligent Metasurface-Assisted Multicell Systems

2026-06-04

AI Summary

This work addresses the challenges of interference management and rate enhancement in multi-cell multi-user MISO systems by introducing, for the first time, the physical deformation degrees of freedom of flexible intelligent metasurfaces (FIMs) into system optimization. It jointly designs base station beamforming, FIM phase-shift matrices, and surface deformation to maximize the weighted sum rate under constraints on transmit power, reflection coefficient magnitudes, and deformation ranges. By integrating WMMSE-based reformulation, block coordinate descent, Riemannian conjugate gradient, and projected gradient descent methods, the proposed framework enables efficient joint optimization and yields a closed-form optimal beamforming solution. This approach transcends the performance limitations of conventional rigid reconfigurable intelligent surfaces (RISs), achieving substantial improvements in both weighted sum rate and interference suppression capability.

Abstract

Flexible intelligent metasurface (FIM) technology has emerged as a promising technology for enhancing wireless communication performance by dynamically reshaping the propagation environment. Compared with conventional rigid reconfigurable intelligent surfaces (RIS), an FIM is composed of multiple electromagnetic (EM) scattering units, each of which can flexibly modify its displacement in the direction normal to the surface, thereby cooperatively morphing the overall surface shape. This additional degree of freedom (DoF) enables improved beamforming and interference mitigation, particularly in complex multicell scenarios. In this paper, an optimization problem for maximizing the weighted sum-rate (WSR) in a multicell multi-user multiple-input single-output (MU-MISO) system assisted by an FIM deployed at the cell boundary is investigated. We jointly optimize the transmit beamforming at the base station (BS), the phase shift matrix, and the FIM surface shape, subject to constraints on the transmit power budget, unit-modulus reflection coefficients, and surface shape morphing range. Due to the non-convex objective function with highly coupled variables, solving the formulated optimization problem is challenging. To tackle this challenge, we propose an efficient alternating optimization framework that leverages the weighted minimum mean square error (WMMSE) method to reformulate the problem and the block coordinate descent (BCD) algorithm to iteratively update the variables. Specifically, the Riemannian conjugate gradient (RCG) algorithm is leveraged to optimize the phase shift matrix, while the projected gradient descent (PGD) method is adopted to optimize the surface shape of the FIM. Additionally, the optimal beamforming vectors are obtained in closed form.

Problem

• weighted sum-rate
• flexible intelligent metasurface
• multicell systems
• beamforming
• interference mitigation

Innovation

• Flexible Intelligent Metasurface
• Weighted Sum-Rate Maximization
• Alternating Optimization
• Riemannian Conjugate Gradient
• Projected Gradient Descent