Discrete Beamforming Optimization for RISs with a Limited Phase Range and Amplitude Attenuation
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Abstract (arXiv)
This paper addresses the problem of maximizing the received power at a user equipment via reconfigurable intelligent surface (RIS) characterized by phase-dependent amplitude (PDA) and discrete phase shifts over a limited phase range. Given complex RIS coefficients, that is, discrete phase shifts and PDAs, we derive the necessary and sufficient conditions to achieve the optimal solution. To this end, we propose an optimal search algorithm that is proven to converge in linear time within at most NK steps, significantly outperforming the exhaustive search approach that would otherwise be needed for RISs with amplitude attenuation. Furthermore, we introduce a practical quantization framework for PDA-introduced RISs termed amplitude-introduced polar quantization (APQ), and extend it to a novel algorithm named extended amplitude-introduced polar quantization (EAPQ) that works with geometric projections. We derive closed-form expressions to assess how closely the performance of the proposed RIS configuration can approximate the ideal case with continuous phases and no attenuation. Our analysis reveals that increasing the number of discrete phases beyond K = 4 yields only marginal gains, regardless of attenuation levels, provided the RIS has a sufficiently wide phase range R. Furthermore, we also show and quantify that when the phase range R is limited, the performance is sensitive to attenuation for larger R, and sensitive to R when there is less attenuation. Finally, the proposed optimal algorithm provides a generic upper bound that could serve as a benchmark for discrete beamforming in RISs with amplitude constraints.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 9 Jul 2025.
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8 of 14 references.
References · 8
- 2019Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimizationcited by 14
- 2021RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trialscited by 14
- 2022A Linear Time Algorithm for the Optimal Discrete IRS Beamformingcited by 3
- 2020Practical Modeling and Beamforming for Intelligent Reflecting Surface Aided Wideband Systemscited by 2
- 2020Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: From practical model to reflection optimizationcited by 2
- 2022RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variationscited by 1
- 2023RIS with insufficient phase shifting capability: Modeling, beamforming, and experimental validationscited by 1
- 2024Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Rangecited by 1
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- 2022Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization Approach
- 2024Optimal Configuration of Reconfigurable Intelligent Surfaces With Non-uniform Phase Quantization
- 2023Quantized Phase Alignment by Discrete Phase Shifts for Reconfigurable Intelligent Surface-Assisted Communication Systems
- 2024Optimal Beamforming of RIS-Aided Wireless Communications: An Alternating Inner Product Maximization Approach
- 2024Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment
- 2025Receiving RISs: Enabling Channel Estimation and Autonomous Configuration
- 2022Max-Min Data Rate Optimization for RIS-aided Uplink Communications with Green Constraints ↗
- 2026Characterization of Beam-Squint and Beam-Split Effects in RIS-assisted Multi-Frequency Networks ↗