# Optimal Configuration of Reconfigurable Intelligent Surfaces With Non-uniform Phase Quantization

arXiv: https://arxiv.org/abs/2405.06967 · HTML: https://mlsanity.com/paper/2405.06967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 19 of 19)

- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-user Communications: Achievable Rates with Limited Discrete Phase Shifts](https://mlsanity.com/paper/1910.14328/) (2019)
- [Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1906.03165/) (2019)
- [Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing](https://mlsanity.com/paper/1910.07990/) (2019)
- [Intelligent Surfaces Empowered Wireless Network: Recent Advances and The Road to 6G](https://mlsanity.com/paper/2312.16918/) (2023)
- [Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds](https://mlsanity.com/paper/2009.02818/) (2020)
- [Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization Approach](https://mlsanity.com/paper/2211.04167/) (2022)
- [A Linear Time Algorithm for the Optimal Discrete IRS Beamforming](https://mlsanity.com/paper/2211.04704/) (2022)
- [Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems](https://mlsanity.com/paper/1911.06237/) (2019)
- [Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming](https://mlsanity.com/paper/2112.02260/) (2021)
- [IRS Aided MEC Systems with Binary Offloading: A Unified Framework for Dynamic IRS Beamforming](https://mlsanity.com/paper/2205.14661/) (2022)
- [Secure Intelligent Reflecting Surface Aided Integrated Sensing and Communication](https://mlsanity.com/paper/2207.09095/) (2022)
- [A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink](https://mlsanity.com/paper/2208.12053/) (2022)
- [Optimal Beamforming for MISO Communications via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2001.11429/) (2020)
- [Optimizing a Binary Intelligent Reflecting Surface for OFDM Communications under Mutual Coupling](https://mlsanity.com/paper/2106.04280/) (2021)
- [Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review](https://mlsanity.com/paper/2304.14232/) (2023)
- [Intelligent Reflecting Surface Assisted Localization: Performance Analysis and Algorithm Design](https://mlsanity.com/paper/2307.09232/) (2023)
- [Optimal Configuration of Reconfigurable Intelligent Surfaces with Arbitrary Discrete Phase Shifts](https://mlsanity.com/paper/2311.07096/) (2023)

## Abstract (arXiv)

The existing methods for reconfigurable intelligent surface (RIS) beamforming in wireless communications are typically limited to uniform phase quantization. However, in practical applications, engineering challenges and design requirements often lead to non-uniform phase and bit resolution of RIS units, which limits the performance potential of these methods. To address this issue, this paper pioneers the study of discrete non-uniform phase configuration in RIS-assisted multiple-input single-output (MISO) communication and formulates an optimization model to characterize the problem. For single-user scenarios, the paper proposes a partition-andtraversal (PAT) algorithm that efficiently achieves the global optimal solution through systematic search and traversal. For larger-scale multi-user scenarios, aiming to balance performance and computational complexity, an enhanced PAT-based algorithm (E-PAT) is developed. By optimizing the search strategy, the E-PAT algorithm significantly reduces computational overhead and achieves linear complexity. Numerical simulations confirm the effectiveness and superiority of the proposed PAT and EPAT algorithms. Additionally, we provide a detailed analysis of the impact of non-uniform phase quantization on system performance.