34 citations · 34 across the 12 of their papers we have counts for
11 papers · 1 filter
Low-Complexity Multi-Agent Continual Learning for Stacked Intelligent Metasurface-Assisted Secure Communications
Enyu Shi, Yiyang Zhu, Jiayi Zhang +6
Stacked intelligent metasurfaces (SIMs), composed of multiple layers of reconfigurable transmissive metasurfaces, are gaining prominence as a transformative technology for future w…
Beamforming Design for Beyond Diagonal RIS-Aided Cell-Free Massive MIMO Systems
Yizhuo Li, Jiakang Zheng, Bokai Xu +4
Reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technology for further improving spectral efficiency (SE…
Joint Power Allocation and Phase Shift Design for Stacked Intelligent Metasurfaces-aided Cell-Free Massive MIMO Systems with MARL
Yiyang Zhu, Jiayi Zhang, Enyu Shi +3
Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems offer high spectral efficiency (SE) through multiple distributed access points (APs). However, the large numbe…
Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
Jiayi Zhang, Ziheng Liu, Yiyang Zhu +9
The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive…
Mobile Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning: A Scalable Framework
Ziheng Liu, Jiayi Zhang, Yiyang Zhu +2
Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro dive…
Joint Precoding and AP Selection for Energy Efficient RIS-aided Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning
Enyu Shi, Jiayi Zhang, Ziheng Liu +5
Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-gene…
Cooperative Multi-Target Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning
Ziheng Liu, Jiayi Zhang, Enyu Shi +3
Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computati…
Distributed Collaborative User Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning
Ziheng Liu, Jiayi Zhang, Enyu Shi +3
In this paper, we investigate a cell-free massive multiple-input multiple-output system, which exhibits great potential in enhancing the capabilities of next-generation mobile comm…
Harnessing Stacked Intelligent Metasurface for Enhanced Cell-Free Massive MIMO Systems: A Low-Power and Cost Approach
Enyu Shi, Jiayi Zhang, Yiyang Zhu +3
In this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into cell-free (CF) massive multiple-input multiple-output (mMIMO) systems t…
Performance Analysis of RIS-aided MISO Systems with EMI and Channel Aging
Taoyu Song, Enyu Shi, Yu Lu +3
In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) system in the presence of electromagnetic interference (EMI) and…
Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems
Yiyang Zhu, Enyu Shi, Ziheng Liu +2
Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). H…