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Qimei Chen - IEEE Xplore Author Profile
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Qimei Chen

Publications
74
Citations
987
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (117)
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Qimei Chen

Affiliation

School of Electronic Information
Wuhan University
Wuhan, China

Publication Topics

Biography

Qimei Chen (Member, IEEE) received the Ph.D. degree from the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China, in 2017. From 2015 to 2016, she was a visiting student with the University of California, Davis, CA, USA. She is currently an Associate Researcher with the School of Electronic Information, Wuhan University, Wuhan, China. Her research interests include the advantage technologies in 5G wireless communication, unlicensed spectrum, green communication, and intelligent edge.(Based on document published on 8 April 2024).
Publications
74
Citations
987
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (117)
Author's Published Works

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Clustered Federated Multi-Task Learning: A Communication-and-Computation Efficient Sparse Sharing Approach

Yuhan Ai;Qimei Chen;Guangxu Zhu;Dingzhu Wen;Hao Jiang;Jun Zeng;Ming Li

IEEE Transactions on Wireless Communications
Year: 2025 | Early Access Article |
Federated multi-task learning (FMTL) is a promising technology to tackle one of the most severe non-independent and identically distributed (non-IID) data challenge in federated learning (FL), which treats each client as a single task and learns personalized models by exploiting task correlations. However, the transmission of individual task models generally results in a significant amount of comm...Show More

Clustered Federated Multi-Task Learning: A Communication-and-Computation Efficient Sparse Sharing Approach

Yuhan Ai;Qimei Chen;Guangxu Zhu;Dingzhu Wen;Hao Jiang;Jun Zeng;Ming Li

Year: 2025 | Early Access Article |

CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network

Zijian Zhao;Tingwei Chen;Zhijie Cai;Xiaoyang Li;Hang Li;Qimei Chen;Guangxu Zhu

IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fa...Show More

CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network

Zijian Zhao;Tingwei Chen;Zhijie Cai;Xiaoyang Li;Hang Li;Qimei Chen;Guangxu Zhu

Year: 2025 | Early Access Article |

Exploiting Beam Split Effect on Wideband Beam Alignment: A Deep Unfolding Based Posterior Matching Approach

Qimei Chen;Yang Yang;Xiaoxia Xu;Guangxu Zhu;Jian Zhang;Hao Jiang

IEEE Journal on Selected Areas in Communications
Year: 2025 | Volume: 43, Issue: 4 | Journal Article |
The massive-antenna wideband millimeter wave (mmWave)/terahertz (THz) systems inevitably suffer from a severe beam split effect due to the non-negligible signal propagation delays, which dramatically reduces communication efficiency. Nevertheless, if the wideband split effect is properly utilized, it can also bring benefits via sensing split directions for channel training. Hence, this paper propo...Show More

Exploiting Beam Split Effect on Wideband Beam Alignment: A Deep Unfolding Based Posterior Matching Approach

Qimei Chen;Yang Yang;Xiaoxia Xu;Guangxu Zhu;Jian Zhang;Hao Jiang

Year: 2025 | Volume: 43, Issue: 4 | Journal Article |

Communication-and-Energy Efficient Over-the-Air Federated Learning

Yipeng Liang;Qimei Chen;Guangxu Zhu;Hao Jiang;Yonina C. Eldar;Shuguang Cui

IEEE Transactions on Wireless Communications
Year: 2025 | Volume: 24, Issue: 1 | Journal Article |
Communication and energy efficiencies are two crucial objectives in the pursuit of edge intelligence in 6G networks, and become increasingly important given the prevalence of large model training. Existing designs typically focus on either communication efficiency or energy efficiency due to the fact that improving one objective generally comes at the expense of the other. Over-the-air federated l...Show More

Communication-and-Energy Efficient Over-the-Air Federated Learning

Yipeng Liang;Qimei Chen;Guangxu Zhu;Hao Jiang;Yonina C. Eldar;Shuguang Cui

Year: 2025 | Volume: 24, Issue: 1 | Journal Article |

Joint Resource Optimization for Federated Edge Learning With Integrated Sensing, Communication, and Computation

Yipeng Liang;Qimei Chen;Hao Jiang

IEEE Internet of Things Journal
Year: 2025 | Volume: 12, Issue: 5 | Journal Article |
Edge artificial intelligence (AI) is an emerging solution for pervasive intelligence service in future 6G networks, by learning machine learning (ML) models at network edge. Edge AI typically consists of three processes: sensing, communication, and computation (SC²). Edge devices first collect data samples through the sensing process, then train local models individually through the computation pr...Show More

Joint Resource Optimization for Federated Edge Learning With Integrated Sensing, Communication, and Computation

Yipeng Liang;Qimei Chen;Hao Jiang

Year: 2025 | Volume: 12, Issue: 5 | Journal Article |

Joint Device Scheduling and Resource Allocation for ISCC-Based Multiview–Multitask Inference

Diao Wang;Dingzhu Wen;Yinghui He;Qimei Chen;Guangxu Zhu;Guanding Yu

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 24 | Journal Article |
This article investigates an integrated sensing-communication-computation (ISCC)-based multiview-multitask (MVMT) edge artificial intelligence inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives and combines multiple views of data from multiple devices to complete several downstream inference ...Show More

Joint Device Scheduling and Resource Allocation for ISCC-Based Multiview–Multitask Inference

Diao Wang;Dingzhu Wen;Yinghui He;Qimei Chen;Guangxu Zhu;Guanding Yu

Year: 2024 | Volume: 11, Issue: 24 | Journal Article |

Integrated Sensing and Learning for Better Generalized Edge AI

Zhijie Cai;Xiaowen Cao;Zihan Zhang;Qimei Chen;Hang Li;Xizoyang Li;Kaifeng Han;Yuanhao Cui;Guangxu Zhu

2024 IEEE 4th International Symposium on Joint Communications & Sensing (JC&S)
Year: 2024 | Conference Paper |
In recent years, significant advancements in deep learning, wireless communication, and sensing have laid the foundation for integrated sensing and learning (ISAL), which involves machines actively and collaboratively collecting data from the environment to facilitate model training at the network edge, specifically for tactile intelligence service provisioning. Despite the progress in deep learni...Show More

Integrated Sensing and Learning for Better Generalized Edge AI

Zhijie Cai;Xiaowen Cao;Zihan Zhang;Qimei Chen;Hang Li;Xizoyang Li;Kaifeng Han;Yuanhao Cui;Guangxu Zhu

Year: 2024 | Conference Paper |

Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation

Shu Liu;Dingzhu Wen;Da Li;Qimei Chen;Guangxu Zhu;Yuanming Shi

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 12 | Journal Article |
Cited by: Papers (1)
Existing edge inference methods only consider one paradigm, i.e., one of on-device inference, on-server inference, or edge-device cooperative inference. Each paradigm has its pros and cons as well as dominant application scopes. For example, the on-device paradigm is the best choice when the inference task is not computationally intensive, the on-server paradigm is suitable if the communication ca...Show More

Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation

Shu Liu;Dingzhu Wen;Da Li;Qimei Chen;Guangxu Zhu;Yuanming Shi

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

Wideband mmWave/THz Beam Alignment: Exploiting Beam Split Effect via Posterior Matching

Yang Yang;Xiaoxia Xu;Qimei Chen;Guo Wan;Jian Zhang

2024 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2024 | Conference Paper |
The massive-antenna millimeter wave (mmWave)/ terahertz (THz) system inevitably suffers from a severe beam split effect, which will significantly decrease array gains and communication efficiency. However, the wideband split effect can be beneficial for channel training by sensing from splitting directions. This paper proposes a novel wideband beam alignment framework in True-Time-Delayers (TTDs) ...Show More

Wideband mmWave/THz Beam Alignment: Exploiting Beam Split Effect via Posterior Matching

Yang Yang;Xiaoxia Xu;Qimei Chen;Guo Wan;Jian Zhang

Year: 2024 | Conference Paper |

Collaborative Edge AI Inference Over Cloud-RAN

Pengfei Zhang;Dingzhu Wen;Guangxu Zhu;Qimei Chen;Kaifeng Han;Yuanming Shi

IEEE Transactions on Communications
Year: 2024 | Volume: 72, Issue: 9 | Journal Article |
Cited by: Papers (3)
In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors, which are then aggregated at each remote radio head (RRH) to suppress sensing noise. To realize efficient uplink feature aggregatio...Show More

Collaborative Edge AI Inference Over Cloud-RAN

Pengfei Zhang;Dingzhu Wen;Guangxu Zhu;Qimei Chen;Kaifeng Han;Yuanming Shi

Year: 2024 | Volume: 72, Issue: 9 | Journal Article |

Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

Jing Wu;Qimei Chen;Hao Jiang;Haozhao Wang;Yulai Xie;Wenzheng Xu;Pan Zhou;Zichuan Xu;Lixing Chen;Beibei Li;Xiumin Wang;Dapeng Oliver Wu

IEEE Transactions on Network Science and Engineering
Year: 2024 | Volume: 11, Issue: 4 | Journal Article |
Cited by: Papers (2)
In the context of 6G, airborne post-disaster emergency networks (PENs) could be resilient in calamities and offer hope for disaster recovery in the underserved disaster zone. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is such a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. Specially, we present possible technological solutions fo...Show More

Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

Jing Wu;Qimei Chen;Hao Jiang;Haozhao Wang;Yulai Xie;Wenzheng Xu;Pan Zhou;Zichuan Xu;Lixing Chen;Beibei Li;Xiumin Wang;Dapeng Oliver Wu

Year: 2024 | Volume: 11, Issue: 4 | Journal Article |

Integrated Sensing And Communication In Unlicensed Mmwave Bands: Joint Beamforming Training And Energy Allocation

Qimei Chen;Yipeng Liang;Hao Jiang

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Integrated sensing and communication (ISAC) within the unlicensed millimeter-wave (mmWave) frequency bands has been emerged as a pivotal technology in the next generation wireless communication era. However, the interference management issue between sensing and communication becomes much severe due to the absence of centralized scheduling function of the widely existed WiGig networks with the IEEE...Show More

Integrated Sensing And Communication In Unlicensed Mmwave Bands: Joint Beamforming Training And Energy Allocation

Qimei Chen;Yipeng Liang;Hao Jiang

Year: 2024 | Conference Paper |

Human-Aware Dynamic Hierarchical Network Control for Distributed Metaverse Services

Qimei Chen;Ruixue Li;Xiaoxia Xu;Jing Wu;Hao Jiang;Meikang Qiu

IEEE Journal on Selected Areas in Communications
Year: 2024 | Volume: 42, Issue: 3 | Journal Article |
Cited by: Papers (1)
Metaverse has emerged as a revolutionary technique for transforming the way people interact with digital content, which relies on a distributed computing and communication infrastructure, encompassing terminal users, edge servers, and cloud servers. However, the rapid evolution of the Metaverse presents challenges that surpass the capabilities of existing communication and network infrastructures,...Show More

Human-Aware Dynamic Hierarchical Network Control for Distributed Metaverse Services

Qimei Chen;Ruixue Li;Xiaoxia Xu;Jing Wu;Hao Jiang;Meikang Qiu

Year: 2024 | Volume: 42, Issue: 3 | Journal Article |

Federated Learning With Energy Harvesting Devices

Li Zeng;Dingzhu Wen;Guangxu Zhu;Changsheng You;Qimei Chen;Yuanming Shi

IEEE Transactions on Green Communications and Networking
Year: 2024 | Volume: 8, Issue: 1 | Journal Article |
Cited by: Papers (6)
Federated learning (FL) is a promising technique for distilling artificial intelligence from massive data distributed in Internet-of-Things networks, while keeping data privacy. However, the efficient deployment of FL faces several challenges due to, e.g., limited radio resources, computation capabilities, and battery lives of Internet-of-Things devices. To address these challenges, in this work, ...Show More

Federated Learning With Energy Harvesting Devices

Li Zeng;Dingzhu Wen;Guangxu Zhu;Changsheng You;Qimei Chen;Yuanming Shi

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

TinyFL: A Lightweight Federated Learning Method with Efficient Memory-and-Communication

Han Cheng;Qimei Chen;Yipeng Liang;Guangxu Zhu

2023 IEEE Globecom Workshops (GC Wkshps)
Year: 2023 | Conference Paper |
Federated Learning (FL) is a distributed learning framework that enables collaborative model training without raw data sharing. However, due to the shortage of memory resources at edge devices and the communication bandwidths at the wireless network, FL encounters significant challenges in edge model deployments. To address the above issues, we introduce a lightweight FL method that retains effici...Show More

TinyFL: A Lightweight Federated Learning Method with Efficient Memory-and-Communication

Han Cheng;Qimei Chen;Yipeng Liang;Guangxu Zhu

Year: 2023 | Conference Paper |

Federated Learning for Privacy-Preserving Prediction of Occupational Group Mobility Using Multi-Source Mobile Data

Hao Li;Hao Jiang;Haoran Xian;Qimei Chen

2023 IEEE International Conference on Data Mining (ICDM)
Year: 2023 | Conference Paper |
This paper focuses on the mobility prediction problem of specific occupational groups that rely on mobile devices, predicting the mobilities of these groups using data shared across different platforms. While the mobility patterns of general populations have been studied extensively, predicting the movements of specific occupational groups like ride-hailing drivers, who frequently move over long d...Show More

Federated Learning for Privacy-Preserving Prediction of Occupational Group Mobility Using Multi-Source Mobile Data

Hao Li;Hao Jiang;Haoran Xian;Qimei Chen

Year: 2023 | Conference Paper |

OverGNN Assisted Power Allocation for Heterogeneous Ultra-Dense Networks

Sisi Lin;Mengyuan Lee;Qimei Chen;Dingzhu Wen;Wen Du;Zhidong He

2023 International Conference on Wireless Communications and Signal Processing (WCSP)
Year: 2023 | Conference Paper |
With the development of wireless communications, heterogeneous ultra-dense networks (HUDNs) have emerged timely to meet the requirements of massive connectivity, high data rate, and low latency in the 5G era. Nevertheless, HUDN indicates large-scale and high-density scenarios, which usually lead to a high-complexity and non-convex NP-hard resource allocation problem. Graph neural network (GNN) is ...Show More

OverGNN Assisted Power Allocation for Heterogeneous Ultra-Dense Networks

Sisi Lin;Mengyuan Lee;Qimei Chen;Dingzhu Wen;Wen Du;Zhidong He

Year: 2023 | Conference Paper |

Communication-Efficient Federated Multi-Task Learning with Sparse Sharing

Yuhan Ai;Qimei Chen;Yipeng Liang;Hao Jiang

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Federated multi-task learning (FMTL) is a promising technology to deal with the severe data heterogeneity issue in federated learning (FL), where each client learns individual models locally and the server extracts similar model parameters from the tasks to keep personalization for models of clients. Hence, it is essential to precisely extract the model parameters shared among tasks. On the other ...Show More

Communication-Efficient Federated Multi-Task Learning with Sparse Sharing

Yuhan Ai;Qimei Chen;Yipeng Liang;Hao Jiang

Year: 2023 | Conference Paper |

Accelerating Distributed Cloud Storage Systems with In-Network Computing

Wei Jiang;Hao Jiang;Jing Wu;Qimei Chen

IEEE Network
Year: 2023 | Volume: 37, Issue: 4 | Magazine Article |
Cited by: Papers (1)
A distributed cloud, connecting multiple smaller and geographically distributed data centers, can provide a significant alternative to the traditional model of massive and centralized data centers. Erasure coding is a key solution for improving the efficiency of storage resources in a distributed cloud. However, current end-side based erasure coding systems require significant computing resources ...Show More

Accelerating Distributed Cloud Storage Systems with In-Network Computing

Wei Jiang;Hao Jiang;Jing Wu;Qimei Chen

Year: 2023 | Volume: 37, Issue: 4 | Magazine Article |

Theoretical Analysis and Performance Evaluation for Federated Edge Learning with Integrated Sensing, Communication and Computation

Yipeng Liang;Qimei Chen;Guangxu Zhu;Hao Jiang

2023 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Edge artificial intelligence (AI) provides potential solutions for ubiquitous intelligent access and services in future 6G networks, by integrating sensing, communication and computation (SCC) at network edges. On the other hand, federated edge learning (FEEL) is identified as one of the key candidates for edge AI due to its advantages on distributed learning and privacy protection. Nevertheless, ...Show More

Theoretical Analysis and Performance Evaluation for Federated Edge Learning with Integrated Sensing, Communication and Computation

Yipeng Liang;Qimei Chen;Guangxu Zhu;Hao Jiang

Year: 2023 | Conference Paper |

Probing-Based Topology Discovery in SDN-enabled Mega-Constellation Network

Wei Jiang;Tao Dong;Hao Jiang;Zhihui Liu;Jie Yin;Hao Li;Jing Wu;Qimei Chen

2023 International Conference on Ubiquitous Communication (Ucom)
Year: 2023 | Conference Paper |
To provide low-latency broadband Internet globally, SpaceX, Amazon, and other companies plan to launch thousands of satellites into low Earth orbit. The proposed constellations hold great promise, but they also present new networking challenges. It is crucial that mega-constellation networks discover their topology accurately and in a timely manner. Due to the mobility of LEO satellites, it is par...Show More

Probing-Based Topology Discovery in SDN-enabled Mega-Constellation Network

Wei Jiang;Tao Dong;Hao Jiang;Zhihui Liu;Jie Yin;Hao Li;Jing Wu;Qimei Chen

Year: 2023 | Conference Paper |

Enhanced Hybrid Hierarchical Federated Edge Learning Over Heterogeneous Networks

Qimei Chen;Zehua You;Dingzhu Wen;Zhaoyang Zhang

IEEE Transactions on Vehicular Technology
Year: 2023 | Volume: 72, Issue: 11 | Journal Article |
Cited by: Papers (7)
In this work, a Hybrid Hierarchical Federated Edge Learning (HHFEL) architecture that consists of a device layer, an edge layer, and a cloud layer over heterogeneous networks, is investigated for large-scale model training. In such systems, learning efficiency is severely degraded by limited communication resources and device heterogeneity in terms of local data distribution and computation capabi...Show More

Enhanced Hybrid Hierarchical Federated Edge Learning Over Heterogeneous Networks

Qimei Chen;Zehua You;Dingzhu Wen;Zhaoyang Zhang

Year: 2023 | Volume: 72, Issue: 11 | Journal Article |

Artificial Intelligence Enabled NOMA Toward Next Generation Multiple Access

Xiaoxia Xu;Yuanwei Liu;Xidong Mu;Qimei Chen;Hao Jiang;Zhiguo Ding

IEEE Wireless Communications
Year: 2023 | Volume: 30, Issue: 1 | Magazine Article |
Cited by: Papers (11)
This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications toward next generation multiple access (NGMA). First, the limitations of current scenario-specific multiple-antenna NOMA schemes are discussed, and the importance of AI for NGMA is highlighted. The...Show More

Artificial Intelligence Enabled NOMA Toward Next Generation Multiple Access

Xiaoxia Xu;Yuanwei Liu;Xidong Mu;Qimei Chen;Hao Jiang;Zhiguo Ding

Year: 2023 | Volume: 30, Issue: 1 | Magazine Article |

Cluster-Free NOMA Communications Toward Next Generation Multiple Access

Xiaoxia Xu;Yuanwei Liu;Xidong Mu;Qimei Chen;Zhiguo Ding

IEEE Transactions on Communications
Year: 2023 | Volume: 71, Issue: 4 | Journal Article |
Cited by: Papers (10)
A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be flexibly implemented between any arbitrary users to achieve eff...Show More

Cluster-Free NOMA Communications Toward Next Generation Multiple Access

Xiaoxia Xu;Yuanwei Liu;Xidong Mu;Qimei Chen;Zhiguo Ding

Year: 2023 | Volume: 71, Issue: 4 | Journal Article |

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

Xiaoxia Xu;Yuanwei Liu;Qimei Chen;Xidong Mu;Zhiguo Ding

IEEE Journal on Selected Areas in Communications
Year: 2023 | Volume: 41, Issue: 4 | Journal Article |
Cited by: Papers (11)
A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users’ minimum data rate requirements. To addre...Show More

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

Xiaoxia Xu;Yuanwei Liu;Qimei Chen;Xidong Mu;Zhiguo Ding

Year: 2023 | Volume: 41, Issue: 4 | Journal Article |

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