Interplay between RIS and AI in Wireless Communications: Fundamentals, Architectures, Applications, and Open Research Problems
arXiv:2101.00250 · doi:10.1109/JSAC.2021.3087259
Abstract
Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI; specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.
References in corpus (26)
- Deep Learning in Neural Networks: An Overview
- Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
- Deep Reinforcement Learning: An Overview
- Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
- MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation
- Wireless Communications with Programmable Metasurface: New Paradigms, Opportunities, and Challenges on Transceiver Design
- White Paper on Broadband Connectivity in 6G
- Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
- MISO Wireless Communication Systems via Intelligent Reflecting Surfaces
- 6G White Paper on Localization and Sensing
- Optimal Communication Channels in a Disordered World with Tamed Randomness
- Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial
- Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems
- Reconfigurable Intelligent Surfaces vs. Relaying: Differences, Similarities, and Performance Comparison
- 6G White Paper on Machine Learning in Wireless Communication Networks
- Arbitrary manipulations of dual harmonics and their wave behaviors based on space-time-coding digital metasurface
- Deep-Learning-Aided Detection for Reconfigurable Intelligent Surfaces
- Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
- Federated Learning in Multi-RIS Aided Systems
- Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
- Channel Estimation via Direct Calculation and Deep Learning for RIS-Aided mmWave Systems
- Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks
- Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method
- Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
- Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
- Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications
Cited by in corpus (4)
- Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
- Intelligent metaphotonics empowered by machine learning
- Deep Learning-Empowered Predictive Beamforming for IRS-Assisted Multi-User Communications
- Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications