42 citations · 114 across the 40 of their papers we have counts for
25 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…
Flexible MIMO for Future Wireless Communications: Which Flexibilities are Possible?
Zhe Wang, Jiayi Zhang, Bokai Xu +3
In conventional multiple-input multiple-output (MIMO), static array configurations struggle in dynamic environments, and further antenna scaling is bounded by cost, energy, and foo…
Energy-Efficient SIM-assisted Communications: How Many Layers Do We Need?
Enyu Shi, Jiayi Zhang, Jiancheng An +3
The stacked intelligent metasurface (SIM), comprising multiple layers of reconfigurable transmissive metasurfaces, is becoming an increasingly viable solution for future wireless c…
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…
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…
Deep Unfolding Beamforming and Power Control Designs for Multi-Port Matching Networks
Bokai Xu, Jiayi Zhang, Qingfeng Lin +3
The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation…
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…
Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach
Jiakang Zheng, Jiayi Zhang, Hongyang Du +4
Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) e…
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,…
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…
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…
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…
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…
Analytical Framework for Effective Degrees of Freedom in Near-Field XL-MIMO
Zhe Wang, Jiayi Zhang, Wenhui Yi +5
Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for su…
Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
Bokai Xu, Jiayi Zhang, Hongyang Du +5
Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless syst…
A Tutorial on Near-Field XL-MIMO Communications Towards 6G
Haiquan Lu, Yong Zeng, Changsheng You +9
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for the sixth-generation (6G) mobile communication networks. By significantly boosting the…
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
Enyu Shi, Jiayi Zhang, Hongyang Du +5
An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral effici…