Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Two-Scale Spatial Deployment for Cost-Effective Wireless Networks via Cooperative IRSs and Movable Antennas

Ying Gao, Qingqing Wu, Ziyuan Zheng, Yanze Zhu, Wen Chen, Xin Lin, Shanpu Shen

TL;DR

The paper tackles cost-efficient downlink coverage by jointly deploying macroscopic IRSs and microscopic movable antennas, formulating a mixed-integer non-convex problem to minimize infrastructure cost under per-area SNR guarantees. A penalty-based alternating optimization framework with successive convex approximation and a feasibility-check auxiliary problem enables tractable subproblems for MA placement, IRS site selection, beamforming, and IRS phase shift design. An additional element-refinement stage prunes IRS elements to further reduce cost without sacrificing QoS. Simulations show substantial cost savings over benchmarks, with MAs preferred in large apertures and FPAs in compact regions, and provide actionable insights on grid resolution and budget-driven strategy choices.

Abstract

This paper proposes a two-scale spatial deployment strategy to ensure reliable coverage for multiple target areas, integrating macroscopic intelligent reflecting surfaces (IRSs) and fine-grained movable antennas (MAs). Specifically, IRSs are selectively deployed from candidate sites to shape the propagation geometry, while MAs are locally repositioned among discretized locations to exploit small-scale channel variations. The objective is to minimize the total deployment cost of MAs and IRSs by jointly optimizing the IRS site selection, MA positions, transmit precoding, and IRS phase shifts, subject to the signal-to-noise ratio (SNR) requirements for all target areas. This leads to a challenging mixed-integer non-convex optimization problem that is intractable to solve directly. To address this, we first formulate an auxiliary problem to verify the feasibility. A penalty-based double-loop algorithm integrating alternating optimization and successive convex approximation (SCA) is developed to solve this feasibility issue, which is subsequently adapted to obtain a suboptimal solution for the original cost minimization problem. Finally, based on the obtained solution, we formulate an element refinement problem to further reduce the deployment cost, which is solved by a penalty-based SCA algorithm. Simulation results demonstrate that the proposed designs consistently outperform benchmarks relying on independent area planning or full IRS deployment in terms of cost-efficiency. Moreover, for cost minimization, MA architectures are preferable in large placement apertures, whereas fully populated FPA architectures excel in compact ones; for worst-case SNR maximization, MA architectures exhibit a lower cost threshold for feasibility, while FPA architectures can attain peak SNR at a lower total cost.

Two-Scale Spatial Deployment for Cost-Effective Wireless Networks via Cooperative IRSs and Movable Antennas

TL;DR

The paper tackles cost-efficient downlink coverage by jointly deploying macroscopic IRSs and microscopic movable antennas, formulating a mixed-integer non-convex problem to minimize infrastructure cost under per-area SNR guarantees. A penalty-based alternating optimization framework with successive convex approximation and a feasibility-check auxiliary problem enables tractable subproblems for MA placement, IRS site selection, beamforming, and IRS phase shift design. An additional element-refinement stage prunes IRS elements to further reduce cost without sacrificing QoS. Simulations show substantial cost savings over benchmarks, with MAs preferred in large apertures and FPAs in compact regions, and provide actionable insights on grid resolution and budget-driven strategy choices.

Abstract

This paper proposes a two-scale spatial deployment strategy to ensure reliable coverage for multiple target areas, integrating macroscopic intelligent reflecting surfaces (IRSs) and fine-grained movable antennas (MAs). Specifically, IRSs are selectively deployed from candidate sites to shape the propagation geometry, while MAs are locally repositioned among discretized locations to exploit small-scale channel variations. The objective is to minimize the total deployment cost of MAs and IRSs by jointly optimizing the IRS site selection, MA positions, transmit precoding, and IRS phase shifts, subject to the signal-to-noise ratio (SNR) requirements for all target areas. This leads to a challenging mixed-integer non-convex optimization problem that is intractable to solve directly. To address this, we first formulate an auxiliary problem to verify the feasibility. A penalty-based double-loop algorithm integrating alternating optimization and successive convex approximation (SCA) is developed to solve this feasibility issue, which is subsequently adapted to obtain a suboptimal solution for the original cost minimization problem. Finally, based on the obtained solution, we formulate an element refinement problem to further reduce the deployment cost, which is solved by a penalty-based SCA algorithm. Simulation results demonstrate that the proposed designs consistently outperform benchmarks relying on independent area planning or full IRS deployment in terms of cost-efficiency. Moreover, for cost minimization, MA architectures are preferable in large placement apertures, whereas fully populated FPA architectures excel in compact ones; for worst-case SNR maximization, MA architectures exhibit a lower cost threshold for feasibility, while FPA architectures can attain peak SNR at a lower total cost.
Paper Structure (24 sections, 48 equations, 7 figures)

This paper contains 24 sections, 48 equations, 7 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Feasibility Check
  6. Optimizing $\left\lbrace x_{m,j}\right\rbrace$ for Given $\left\lbrace \mathbf \Theta_{\ell,j}\right\rbrace$
  7. Optimizing $\left\lbrace \mathbf \Theta_{\ell,j}\right\rbrace$ for Given $\left\lbrace x_{m,j}\right\rbrace$
  8. Overall Algorithm
  9. Proposed Algorithm for Problem (P1)
  10. Optimizing $\left\lbrace x_{m,j}, z_\ell\right\rbrace$ for Given $\left\lbrace \mathbf \Theta_{\ell,j}\right\rbrace$
  11. Optimizing $\left\lbrace \mathbf \Theta_{\ell,j}\right\rbrace$ for Given $\left\lbrace x_{m,j}, z_\ell\right\rbrace$
  12. Overall Algorithm
  13. IRS Element Pruning for Further Cost Reduction
  14. Problem Formulation
  15. Proposed Algorithm for Problem (P3)
  16. ...and 9 more sections

Figures (7)

  • Figure 1: Illustration of an IRS-aided MA system, involving joint IRS and MA deployment and configuration for cost-effective coverage.
  • Figure 2: Average worst-case SNR versus normalized step size.
  • Figure 3: Impact of the MA unit cost $c_{\rm MA}$. (a) Average normalized deployment cost. (b) Average MA count per area and shared IRS panel count under the proposed joint MA-IRS design.
  • Figure 4: Average normalized deployment cost versus normalized region size.
  • Figure 5: Average normalized deployment cost versus number of target areas.
  • ...and 2 more figures

Theorems & Definitions (1)

  • Remark 1