An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems
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Abstract (arXiv)
In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating at two endpoints (i.e., transmitter and receiver) only are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and revolutionary technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next-/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective. We also highlight promising research directions that are worthy of investigation in the future.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2021.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 3.
8 papers.
- 2023Localization with Reconfigurable Intelligent Surface: An Active Sensing Approachcited by 3
- 2025Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Modelscited by 2
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approachcited by 1
- 2024IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocationcited by 1
- 2023A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systemscited by 0
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusingcited by 0
- 2026Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrayscited by 0
- 2026Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Modelscited by 0
- 2026Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models30 Jun 2026
- 2026Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays6 Apr 2026
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing7 Mar 2026
- 2025Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models22 Jul 2025
- 2024IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation4 Nov 2024
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach19 Jun 2024
- 2023Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach14 Dec 2023
- 2023A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems24 Jan 2023