AIRIS: Artificial intelligence enhanced signal processing in reconfigurable intelligent surface communications

Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beam-forming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.

[1]  Shi Jin,et al.  3D Scene-Based Beam Selection for mmWave Communications , 2020, IEEE Wireless Communications Letters.

[2]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[3]  David Duvenaud,et al.  Neural Ordinary Differential Equations , 2018, NeurIPS.

[4]  Chadi Assi,et al.  Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamforming , 2021, IEEE Transactions on Vehicular Technology.

[5]  Sinem Coleri,et al.  Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO , 2021, IEEE Transactions on Wireless Communications.

[6]  Zhu Han,et al.  Practical Hybrid Beamforming With Finite-Resolution Phase Shifters for Reconfigurable Intelligent Surface Based Multi-User Communications , 2020, IEEE Transactions on Vehicular Technology.

[7]  Chau Yuen,et al.  Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, IEEE Transactions on Wireless Communications.

[8]  Octavia A. Dobre,et al.  Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication , 2020, IEEE Transactions on Communications.

[9]  Caijun Zhong,et al.  Ordinary Differential Equation-Based CNN for Channel Extrapolation Over RIS-Assisted Communication , 2020, IEEE Communications Letters.

[10]  Miaowen Wen,et al.  Adaptive Transmission for Reconfigurable Intelligent Surface-Assisted OFDM Wireless Communications , 2020, IEEE Journal on Selected Areas in Communications.

[11]  Hui Tian,et al.  Federated Learning in Multi-RIS-Aided Systems , 2020, IEEE Internet of Things Journal.

[12]  Zhu Han,et al.  Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts , 2019, IEEE Journal on Selected Areas in Communications.

[13]  K. Mishra,et al.  A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces , 2020, ArXiv.

[14]  Yuan He,et al.  Machine learning empowered beam management for intelligent reflecting surface assisted MmWave networks , 2020, China Communications.

[15]  Mohamed-Slim Alouini,et al.  Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces , 2020, IEEE Transactions on Vehicular Technology.

[16]  Beixiong Zheng,et al.  Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization , 2020, IEEE Wireless Communications Letters.

[17]  Ronghong Mo,et al.  Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning , 2020, IEEE Journal on Selected Areas in Communications.

[18]  Henk Wymeersch,et al.  Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens , 2020, ICC 2021 - IEEE International Conference on Communications.

[19]  L. Dai,et al.  Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications , 2020, ArXiv.

[20]  Shi Jin,et al.  Sparse Bayesian Learning for the Time-Varying Massive MIMO Channels: Acquisition and Tracking , 2019, IEEE Transactions on Communications.

[21]  Gabriele Gradoni,et al.  Reconfigurable Intelligent Surfaces and Machine Learning for Wireless Fingerprinting Localization , 2020, ArXiv.

[22]  Christopher G. Brinton,et al.  Exploiting Multiple Intelligent Reflecting Surfaces in Multi-Cell Uplink MIMO Communications , 2020, ArXiv.

[23]  Tao Jiang,et al.  Millimeter-Wave Massive MIMO Systems Relying on Generalized Sub-Array-Connected Hybrid Precoding , 2018, IEEE Transactions on Vehicular Technology.

[24]  Li Wei,et al.  Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design , 2021 .

[25]  Dawei Wang,et al.  Enhanced reconfigurable intelligent surface assisted mmWave communication: A federated learning approach , 2020, China Communications.

[26]  Elisabeth de Carvalho,et al.  A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting , 2020, CrownCom.

[27]  Yonina C. Eldar,et al.  Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications , 2020, IEEE Wireless Communications.

[28]  David Duvenaud,et al.  Latent ODEs for Irregularly-Sampled Time Series , 2019, ArXiv.

[29]  Lajos Hanzo,et al.  Channel-Covariance and Angle-of-Departure Aided Hybrid Precoding for Wideband Multiuser Millimeter Wave MIMO Systems , 2019, IEEE Transactions on Communications.

[30]  Shuowen Zhang,et al.  Intelligent Reflecting Surface Aided Multi-User Communication: Capacity Region and Deployment Strategy , 2020, IEEE Transactions on Communications.

[31]  Derrick Wing Kwan Ng,et al.  Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications , 2021 .

[32]  Yuanwei Liu,et al.  Machine Learning for User Partitioning and Phase Shifters Design in RIS-Aided NOMA Networks , 2021, IEEE Transactions on Communications.

[33]  Shuowen Zhang,et al.  Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization , 2019, IEEE Transactions on Communications.

[34]  Tao Jiang,et al.  Non-Uniform Quantization Codebook-Based Hybrid Precoding to Reduce Feedback Overhead in Millimeter Wave MIMO Systems , 2019, IEEE Transactions on Communications.

[35]  Tao Jiang,et al.  Hybrid Precoding for WideBand Millimeter Wave MIMO Systems in the Face of Beam Squint , 2021, IEEE Transactions on Wireless Communications.

[36]  Vuk Marojevic,et al.  UAVs with Reconfigurable Intelligent Surfaces: Applications, Challenges, and Opportunities , 2020, ArXiv.

[37]  Li Wei,et al.  Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method , 2020, 2020 IEEE Globecom Workshops (GC Wkshps.

[38]  Chau Yuen,et al.  Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[39]  Walid Saad,et al.  Deep Reinforcement Learning for Energy-Efficient Networking with Reconfigurable Intelligent Surfaces , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[40]  Ahmed Alkhateeb,et al.  Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning , 2019, IEEE Access.

[41]  Xiaojun Yuan,et al.  Hierarchical Passive Beamforming for Reconfigurable Intelligent Surface Aided Communications , 2021, IEEE Wireless Communications Letters.

[42]  F. Gao,et al.  Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems , 2019, IEEE Transactions on Communications.

[43]  Changsheng You,et al.  Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial , 2020, IEEE Transactions on Communications.

[44]  Tao Jiang,et al.  Beam-Squint Mitigating for Reconfigurable Intelligent Surface Aided Wideband MmWave Communications , 2021, ArXiv.

[45]  Rui Zhang,et al.  Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network , 2019, IEEE Communications Magazine.

[46]  Shuangfeng Han,et al.  Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems , 2017, 2017 IEEE International Conference on Communications (ICC).

[47]  Mohamed-Slim Alouini,et al.  GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO , 2020, IEEE Access.

[48]  Ahmed Alkhateeb,et al.  Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction , 2021, 2021 IEEE International Conference on Communications Workshops (ICC Workshops).