Stacked intelligent metasurfaces (SIMs) represent a novel signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Their multi-layer architecture exhibits customizable computational capabilities compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems through a low complexity manner with reduced numbers of transmit radio frequency chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently tackled via a customized deep reinforcement learning (DRL) approach that systematically explores pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results demonstrate the proposed method's capability to effectively learn from the wireless environment, while consistently outperforming conventional precoding schemes under low transmit power conditions. Furthermore, the implementation of hyperparameter tuning and whitening process significantly enhance the robustness of the proposed DRL framework.
Enhanced Channel Estimation for Flexible Intelligent Metasurface-Aided Communication Systems
University of Electronic Science and Technology of China（电子科技大学）；Yibin Institute of UESTC（电子科技大学宜宾研究院）；Nanyang Technological University（南洋理工大学）；The University of Texas at Dallas（达拉斯德州大学）；Queen Mary University of London（伦敦大学皇家玛丽学院）；Kyung Hee University（庆熙大学）；University of Houston（休斯顿大学）
Anti-Jamming Sensing with Distributed Reconfigurable Intelligent Metasurface Antennas
School of Information and Communications Engineering, Xi’an Jiaotong University（信息与通信工程学院，西安交通大学）；Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University（教育部智能网络与网络安全重点实验室，西安交通大学）
Weighted Sum-Rate Enhancement for Flexible Intelligent Metasurface-Assisted Multicell Systems
University of Electronic Science and Technology of China (UESTC)（电子科技大学）；Yibin Institute of UESTC（电子科技大学宜宾研究院）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Queen Mary University of London（伦敦大学玛丽女王学院）
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；Technology Innovation Institute（技术创新研究院）；Zhejiang University（浙江大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Oulu（奥卢大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
