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Sparsity Realization in User-Side Multilayer RIS

Hasan M. Boudi, Taissir Y. Elganimi

TL;DR

The paper tackles the challenge of achieving high-rate uplink communication with compact user-side hardware by introducing sparsity realizations on a multilayer US-RIS. It combines element-wise sparsity and a novel foldable geometric sparsity to realize a larger effective aperture, and develops a two-timescale joint optimization framework (Stage 1: element topology via tabu search; Stage 2: folding geometry via tabu search; plus beamforming via alternating optimization) to maximize SNR. Key contributions include the foldable sparse US-RIS architecture, a practical two-stage optimization scheme, and demonstrated rate gains over traditional dense RIS setups, aided by an expanded idle-to-active element distribution and geometric degrees of freedom. The results signal a viable path for deploying high-performance large-scale arrays in user devices, with potential impact on vehicular communications and fixed wireless access.

Abstract

User-side reconfigurable intelligent surface (US-RIS)-aided communication has recently emerged as a promising solution to overcome the high hardware cost and physical size limitations of large-scale user side antenna arrays. This letter proposes, for the first time, a framework that realizes sparsity in multilayer US-RIS using two strategies, namely element-wise sparsity and geometric sparsity. The element-wise approach distributes a limited number of active elements irregularly across multiple layers, thereby exploiting additional spatial degrees of freedom and boosting the achievable rate. For further performance enhancement, a novel foldable RIS architecture leveraging geometric sparsity is proposed, achieving additional gains by optimizing the folding topology of its multilayer structure. Simulation results show that the proposed sparse architectures provide consistently higher achievable rates than existing designs.

Sparsity Realization in User-Side Multilayer RIS

TL;DR

The paper tackles the challenge of achieving high-rate uplink communication with compact user-side hardware by introducing sparsity realizations on a multilayer US-RIS. It combines element-wise sparsity and a novel foldable geometric sparsity to realize a larger effective aperture, and develops a two-timescale joint optimization framework (Stage 1: element topology via tabu search; Stage 2: folding geometry via tabu search; plus beamforming via alternating optimization) to maximize SNR. Key contributions include the foldable sparse US-RIS architecture, a practical two-stage optimization scheme, and demonstrated rate gains over traditional dense RIS setups, aided by an expanded idle-to-active element distribution and geometric degrees of freedom. The results signal a viable path for deploying high-performance large-scale arrays in user devices, with potential impact on vehicular communications and fixed wireless access.

Abstract

User-side reconfigurable intelligent surface (US-RIS)-aided communication has recently emerged as a promising solution to overcome the high hardware cost and physical size limitations of large-scale user side antenna arrays. This letter proposes, for the first time, a framework that realizes sparsity in multilayer US-RIS using two strategies, namely element-wise sparsity and geometric sparsity. The element-wise approach distributes a limited number of active elements irregularly across multiple layers, thereby exploiting additional spatial degrees of freedom and boosting the achievable rate. For further performance enhancement, a novel foldable RIS architecture leveraging geometric sparsity is proposed, achieving additional gains by optimizing the folding topology of its multilayer structure. Simulation results show that the proposed sparse architectures provide consistently higher achievable rates than existing designs.
Paper Structure (17 sections, 13 equations, 5 figures)

This paper contains 17 sections, 13 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model And Problem Formulation
  3. Sparse US-RIS Model
  4. Foldable Sparse US-RIS Model
  5. Achievable Rate Maximization Problem
  6. Proposed Joint Optimization Framework
  7. Stage 1: Element-Wise Topology Optimization
  8. Stage 2: Geometric Folding Optimization
  9. Beamforming via Alternating Optimization (AO)
  10. Optimal Receiver Combining Vector ($\mathbf{v}^*$)
  11. Optimal Phase Shifts ($\mathbf{\theta}^{(l)*}$)
  12. Optimal Transmit Beamforming Vector ($\mathbf{w}^*$)
  13. Numerical Results
  14. Simulation Setup
  15. Achievable Rate Analysis
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Illustration of (a) sparse US-RIS-aided communications; and (b) foldable sparse US-RIS-aided communications, showing the user device, the multilayer RIS, and the BS.
  • Figure 2: Illustration of the foldable sparse RIS architecture for the $l$-th layer.
  • Figure 3: Achievable rate versus transmit power.
  • Figure 4: Power distribution of non-sparse multilayer US-RIS: (a) Layer 1, (b) Layer 2.
  • Figure 5: Power distribution of optimized foldable sparse US-RIS: (a) Layer 1, (b) Layer 2, (c) Layer 3.