34 citations · 48 across the 19 of their papers we have counts for
20 papers
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…
Rydberg Atomic Receivers for Wireless Communications: Fundamentals, Potential, Applications, and Challenges
Yin Zhang, Jiayi Zhang, Bokai Xu +10
Rydberg atomic receivers (RARs) leverage the quantum coherence of highly excited atoms to overcome the intrinsic physical limitations of conventional radio frequency receivers (RFR…
Joint Power Control and Precoding for Cell-Free Massive MIMO Systems With Sparse Multi-Dimensional Graph Neural Networks
Yukun Ma, Jiayi Zhang, Ziheng Liu +2
Cell-free massive multiple-input multiple-output (CF mMIMO) has emerged as a prominent candidate for future networks due to its ability to significantly enhance spectral efficiency…
White-Box AI Model: Next Frontier of Wireless Communications
Jiayao Yang, Jiayi Zhang, Bokai Xu +7
White-box AI (WAI), or explainable AI (XAI) model, a novel tool to achieve the reasoning behind decisions and predictions made by the AI algorithms, makes it more understandable an…
Channel Estimation for Rydberg Atomic Receivers
Bokai Xu, Jiayi Zhang, Zhongtao Chen +4
The rapid development of the quantum technology presents huge opportunities for 6G communications. Leveraging the quantum properties of highly excited Rydberg atoms, Rydberg atom-b…
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…
Robust Multidimensional Graph Neural Networks for Signal Processing in Wireless Communications with Edge-Graph Information Bottleneck
Ziheng Liu, Jiayi Zhang, Yiyang Zhu +2
Signal processing is crucial for satisfying the high data rate requirements of future sixth-generation (6G) wireless networks. However, the rapid growth of wireless networks has br…
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…
Joint AP-UE Association and Precoding for SIM-Aided Cell-Free Massive MIMO Systems
Enyu Shi, Jiayi Zhang, Jiancheng An +4
Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However,…
Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3
Xiaolei Yang, Jiayi Zhang, Enyu Shi +4
The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the op…
Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning
Ziheng Liu, Jiayi Zhang, Zhilong Liu +2
In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a…
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…
Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions
Ziheng Liu, Jiayi Zhang, Enyu Shi +5
Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of l…
Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning
Ziheng Liu, Jiayi Zhang, Zhilong Liu +2
Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant…
Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning: Architectures, Challenges, and Future Directions
Zhilong Liu, Jiayi Zhang, Ziheng Liu +5
Cell-free massive multiple-input multiple-output (mMIMO) and extremely large-scale MIMO (XL-MIMO) are regarded as promising innovations for the forthcoming generation of wireless c…
Uplink Power Control for Extremely Large-Scale MIMO with Multi-Agent Reinforcement Learning and Fuzzy Logic
Ziheng Liu, Zhilong Liu, Jiayi Zhang +3
In this paper, we investigate the uplink transmit power optimization problem in cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Instead of ap…