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Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks

Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang

TL;DR

The paper studies joint deployment and orientation of STAR-RIS in a downlink MU-MISO system to maximize sum rate. It introduces a point-point user-grouping representation and a differential evolution-based solver (DEBG) that combines DE with SDP for hybrid beamforming, plus a balanced grouping mechanism to select boundary users. Key contributions include offline optimization of location, orientation, and beamforming, as well as a structured framework that demonstrates substantial performance gains and robustness under imperfect CSI. The approach provides practical guidance for deploying STAR-RIS in quasi-static scenarios, highlighting that symmetric, user-near deployments yield the best outcomes and that balancing groupings across transmission and reflection sides is crucial for fully leveraging STAR-RIS capabilities.

Abstract

This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems. Unlike existing works that use fixed deployment setups or solely optimize the location, this paper emphasizes the joint optimization of the location and orientation of STAR-RIS. This enables searching across all user grouping possibilities and fully boosting the system's performance. We consider a sum rate maximization problem with joint optimization and hybrid beamforming design. An offline heuristic solution is proposed for the problem, developed based on differential evolution and semi-definite programming methods. In particular, a point-point representation is proposed for characterizing and exploiting the user-grouping. A balanced grouping method is designed to achieve a desired user grouping with low complexity. Numerical results demonstrate the substantial performance gains achievable through optimal deployment design.

Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks

TL;DR

The paper studies joint deployment and orientation of STAR-RIS in a downlink MU-MISO system to maximize sum rate. It introduces a point-point user-grouping representation and a differential evolution-based solver (DEBG) that combines DE with SDP for hybrid beamforming, plus a balanced grouping mechanism to select boundary users. Key contributions include offline optimization of location, orientation, and beamforming, as well as a structured framework that demonstrates substantial performance gains and robustness under imperfect CSI. The approach provides practical guidance for deploying STAR-RIS in quasi-static scenarios, highlighting that symmetric, user-near deployments yield the best outcomes and that balancing groupings across transmission and reflection sides is crucial for fully leveraging STAR-RIS capabilities.

Abstract

This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems. Unlike existing works that use fixed deployment setups or solely optimize the location, this paper emphasizes the joint optimization of the location and orientation of STAR-RIS. This enables searching across all user grouping possibilities and fully boosting the system's performance. We consider a sum rate maximization problem with joint optimization and hybrid beamforming design. An offline heuristic solution is proposed for the problem, developed based on differential evolution and semi-definite programming methods. In particular, a point-point representation is proposed for characterizing and exploiting the user-grouping. A balanced grouping method is designed to achieve a desired user grouping with low complexity. Numerical results demonstrate the substantial performance gains achievable through optimal deployment design.
Paper Structure (25 sections, 21 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 25 sections, 21 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. STAR-RIS Model
  4. Transmission Model
  5. Proposed Problem Formulation
  6. Proposed Heuristic Solution
  7. Point-Point Representation for User-Grouping
  8. Framework
  9. DE Reproduction and DE Selection
  10. Balanced Grouping
  11. SDP Method
  12. Deployment Orientation Refinement
  13. Convergence and Complexity Analysis
  14. Simulation Results
  15. Setup, Metrics and Algorithms
  16. ...and 10 more sections

Figures (7)

  • Figure 1: A downlink STAR-RIS assisted MU-MISO communication network.
  • Figure 2: User-grouping based on the point-point representation (top view).
  • Figure 3: DEBG workflow.
  • Figure 4: Optimal STAR-RIS deployment (top view).
  • Figure 5: Average worst-case sum rate versus the uncertainty level of cascaded channels under $P_{max}=20 dBm$, $N_a=4$ and $M=20$.
  • ...and 2 more figures