Query：
学者姓名：郑海峰
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
With the exponential advancement of vehicle networking applications and autonomous driving technology, the demand for efficient and secure autonomous vehicles (AVs) is increasing. AVs require the ability to gather information to navigate complex and ever-changing traffic environments, making effective communication with other vehicles or roadside units (RSUs) crucial for achieving co-awareness. Integrated Sensing and Communication (ISAC) technology emerges as a promising solution for the future of autonomous driving. However, in the dynamic and uncertain real-world road environment, the selection of sensing and communication (SC) functions becomes paramount in enhancing performance. Moreover, ambient noise often disrupts the interaction between vehicles and roadside units, leading to a partial loss of environmental states. To address this challenge, we propose a novel approach for selecting sensing and communication functions, even in the presence of partial loss of environmental states. Specifically, we approximate a partially observable Markov decision process (POMDP) to a complete Markov decision process (MDP) through matrix completion and subsequently utilize deep reinforcement learning (DRL) to solve it. Additionally, we propose a matrix completion algorithm based on the alternating direction method of multipliers (ADMM) with deep unfolding to accurately complete the missing environmental states. Finally, we demonstrate that the proposed method outperforms the other POMDP-based approaches for SC function selection in an ISAC-enabled vehicular network.
Keyword ：
Deep matrix factorization Deep matrix factorization deep unfolding deep unfolding integrated sensing and communication integrated sensing and communication partially observable Markov decision process partially observable Markov decision process vehicular network vehicular network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan et al. Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) : 4933-4945 . |
| MLA | Shen, Xiangyu et al. "Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 74 . 3 (2025) : 4933-4945 . |
| APA | Shen, Xiangyu , Zheng, Haifeng , Lin, Jiayuan , Feng, Xinxin . Joint Deep Reinforcement Learning and Unfolding for Sensing and Communication Function Selection in Vehicular Networks . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) , 4933-4945 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Multimodal information plays an important role in the advanced Internet of Things (IoT) in the era of 6G, which provides reliable and comprehensive assistance for downstream tasks through further fusion and analysis via federated learning (FL). One of the primary challenges in FL is data heterogeneity, which may lead to domain shifts and sharply different local long-tailed category distribution across nodes. These issues hinder the large-scale deployment of FL in IoT applications equipped with multiple various multimodal sensors due to performance deterioration. In this paper, we propose a novel multimodal fusion framework to tackle the aforementioned coupled problems arising during the cooperative fusion of multimodal information without privacy exposure among decentralized nodes equipped with diverse sensors. Specifically, we introduce a flexible global logit alignment (GLA) method based on multi-view domains. This method enables the fusion of diverse multimodal information with the consideration of domain shifts caused by modality-based data heterogeneity. Furthermore, we propose a novel local angular margin (LAM) scheme, which dynamically adjusts decision boundaries for locally seen categories while preserving global decision boundaries for unseen categories. This effectively mitigates severe model divergence caused by significantly different category distributions. Extensive simulations demonstrate the superiority of the proposed framework, which exhibits significant merits in tackling model degeneration caused by data heterogeneity and enhancing modality-based generalization for heterogeneous scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Federated learning Federated learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gao, Min , Zheng, Haifeng , Feng, Xinxin et al. Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning [C] . 2025 : 16736-16744 . |
| MLA | Gao, Min et al. "Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning" . (2025) : 16736-16744 . |
| APA | Gao, Min , Zheng, Haifeng , Feng, Xinxin , Tao, Ran . Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning . (2025) : 16736-16744 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated learning (FL) has been extensively studied as a means of ensuring data privacy while cooperatively training a global model across decentralized devices. Among various FL approaches, asynchronous federated learning (AFL) has distinct advantages in overcoming the straggler problem via server-side aggregation as soon as it receives a local model. However, AFL still faces several challenges in large-scale real-world applications, such as stale model problems and modality heterogeneity across geographically distributed and industrial devices with different functions. In this article, we propose a multimodal fusion framework for AFL to address the aforementioned problems. Specifically, a novel multilinear block fusion model is designed to fuse various multimodal information, which serves as an enhancement for perceiving and transmitting the important modality and block during local training. An adaptive aggregation strategy is further developed to fully utilize heterogeneous data by allowing the global model to favor the received local model based on both freshness and the importance of the local data. Extensive simulations with different data distributions demonstrate the superiority of the proposed framework in heterogeneity scenarios, which exhibits significant merits in the improvement of modality-based generalization without sacrificing convergence speed and communication consumption.
Keyword ：
Asynchronous federated learning (AFL) Asynchronous federated learning (AFL) block term (BT) decomposition block term (BT) decomposition multimodal fusion multimodal fusion
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gao, Min , Zheng, Haifeng , Du, Mengxuan et al. Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) : 14083-14093 . |
| MLA | Gao, Min et al. "Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 12 (2024) : 14083-14093 . |
| APA | Gao, Min , Zheng, Haifeng , Du, Mengxuan , Feng, Xinxin . Multimodal Fusion With Block Term Decomposition for Asynchronous Federated Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) , 14083-14093 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated learning (FL), as a privacy-enhancing distributed learning paradigm, has recently attracted much attention in wireless systems. By providing communication and computation services, the base station (BS) helps participants collaboratively train a shared model without transmitting raw data. Concurrently, with the advent of integrated sensing and communication (ISAC) and the growing demand for sensing services, it is envisioned that BS will simultaneously serve sensing services, as well as communication and computation services, e.g., FL, in future 6G wireless networks. To this end, we provide a novel integrated sensing, communication and computation (ISCC) system, called Fed-ISCC, where BS conducts sensing and FL in the same time-frequency resource, and the over-the-air computation (AirComp) is adopted to enable fast model aggregation. To mitigate the interference between sensing and FL during uplink transmission, we propose a receive beamforming approach. Subsequently, we analyze the convergence of FL in the Fed-ISCC system, which reveals that the convergence of FL is hindered by device selection error and transmission error caused by sensing interference, channel fading and receiver noise. Based on this analysis, we formulate an optimization problem that considers the optimization of transceiver beamforming vectors and device selection strategy, with the goal of minimizing transmission and device selection errors while ensuring the sensing requirement. To address this problem, we propose a joint optimization algorithm that decouples it into two main problems and then solves them iteratively. Simulation results demonstrate that our proposed algorithm is superior to other comparison schemes and nearly attains the performance of ideal FL.
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink federated learning (FL) federated learning (FL) integrated sensing and communication (ISAC) integrated sensing and communication (ISAC) Optimization Optimization over-the-air computation (AirComp) over-the-air computation (AirComp) Radar Radar Task analysis Task analysis Uplink Uplink
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Gao, Min et al. Integrated Sensing, Communication, and Computation for Over-the-Air Federated Learning in 6G Wireless Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (21) : 35551-35567 . |
| MLA | Du, Mengxuan et al. "Integrated Sensing, Communication, and Computation for Over-the-Air Federated Learning in 6G Wireless Networks" . | IEEE INTERNET OF THINGS JOURNAL 11 . 21 (2024) : 35551-35567 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Gao, Min , Feng, Xinxin , Hu, Jinsong , Chen, Youjia . Integrated Sensing, Communication, and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (21) , 35551-35567 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Hierarchical federated learning (HFL) in wireless networks significantly saves communication resources thanks to edge aggregation in edge mobile computing (MEC) servers. Considering the spatially correlated data in wireless networks, in this paper, we analyze the performance of HFL with hybrid data distributions, i.e. intra-MEC independent and identically distributed (IID) and inter-MEC non-IID data samples. We also derive the performance impacts of data heterogeneity and global aggregation interval. Based on our theoretical results, we further propose a novel aggregation weights design with loss-based heterogeneity to accelerate the training of HFL and improve learning accuracy. Our simulations verify the theoretical results and demonstrate the performance gain achieved by the proposed aggregation weights design. Moreover, we find that the performance gain of the proposed aggregation weights design is higher in a high-heterogeneity scenario than in a low-heterogeneity one.
Keyword ：
aggregation weights design aggregation weights design Hierarchical federated learning Hierarchical federated learning non-IID data non-IID data wireless networks wireless networks
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, Yuchuan , Chen, Youjia , Yang, Junnan et al. Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 , 2024 . |
| MLA | Ye, Yuchuan et al. "Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 (2024) . |
| APA | Ye, Yuchuan , Chen, Youjia , Yang, Junnan , Ding, Ming , Cheng, Peng , Hu, Jinsong et al. Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This paper explores an intelligent heavy truck solution with integrated sensing, communication, computation, and control (ISCC), capabilities based on the "vehicle-energy-road-cloud" framework for enclosed intermodal railwayroad transport parks. Enabled by communication-sensing convergence networks, the system achieves ubiquitous connectivity across Multi-Agent individual truck intelligence, vehicle coordination intelligence, edge computing, and cloud platforms. The multi-modal sensor fusion of vehicle-energy-road elements ensures comprehensive environmental cognition by combining truck-mounted devices, energy facilities, and roadside sensors. This allows collaborative decisionmaking through distributed in-vehicle and cloud-based analytics. Through intelligent dispatching, precise control commands guide trucks to safely and efficiently complete loading, weighing, vehicle interworking, and other intermodal transport tasks. This human-in-the-loop framework synergizes sensing, communication, computation, and control to fully unlock the potential of new energy heavy trucks, enhancing the safety, accuracy, and efficiency of freight haulage operations in complex enclosed parks.
Keyword ：
and Computation (ISCC) and Computation (ISCC) Communication Communication Integrated Sensing Integrated Sensing Multi-agent reinforcement learning Multi-agent reinforcement learning Multi-modal sensor fusion Multi-modal sensor fusion Vehicle-energy-road-cloud Vehicle-energy-road-cloud
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Weibin , Zheng, Haifeng , Fang, Jun et al. INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS [J]. | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 , 2024 : 650-654 . |
| MLA | Li, Weibin et al. "INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS" . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 (2024) : 650-654 . |
| APA | Li, Weibin , Zheng, Haifeng , Fang, Jun , Feng, Xinxin , Cheng, Chunyan . INTELLIGENT HEAVY TRUCK PLATOONING WITH ISCC FOR ENCLOSED INTERMODAL RAILWAY-ROAD TRANSPORT PARKS . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024 , 2024 , 650-654 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In the realm of 6G wireless networks, virtual reality (VR) 360-degree videos stand out as a pivotal application. Researches on the users' quality of experience (QoE) for VR 360-degree videos mainly focus on video coding and transmission schemes, with a limited investigation into the impacts of wireless channels. To fill this gap, this paper emulates VR 360-degree video transmission on three kinds of wireless channels: additive Gaussian white noise (AWGN), Rayleigh fading, and Rician fading channels. The performance metrics for the wireless physical layer including signal-to-noise ratio (SNR), end-to-end delay, and bit error rate are investigated for their impacts on the performance metrics of video transmission, including video bitrate, stalling time, and start-up delay. Finally, a comprehensive QoE score is derived based on measured application-layer quality. Furthermore, we fit the functions: i) a log-scaling law of QoE vs. bandwidth, and ii) a Sigmoid function-scaling law for QoE vs. SNR. The results shed light on guiding physical layer network optimization aimed at improving the subjective QoE of VR videos.
Keyword ：
VR video QoE VR video QoE Wireless link performance Wireless link performance
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Sun, Shengying , Chen, Youjia , Guo, Boyang et al. Mapping Wireless Link Performance to 360-Degree VR QoE [J]. | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| MLA | Sun, Shengying et al. "Mapping Wireless Link Performance to 360-Degree VR QoE" . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC (2024) . |
| APA | Sun, Shengying , Chen, Youjia , Guo, Boyang , Ye, Yuchuan , Hu, Jinsong , Zheng, Haifeng . Mapping Wireless Link Performance to 360-Degree VR QoE . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.
Keyword ：
ADMM ADMM deep unfolding network deep unfolding network integrated sensing and communication integrated sensing and communication Orthogonal Time Frequency Space (OTFS) Orthogonal Time Frequency Space (OTFS) target parameter estimation target parameter estimation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin et al. Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Lin, Weizhi et al. "Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin , Chen, Youjia . Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.
Keyword ：
ADMM ADMM Atomic norm Atomic norm ISAC ISAC Off-grid target parameter estimation Off-grid target parameter estimation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Ling, Muyao et al. "Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Ling, Muyao , Feng, Xinxin , Zheng, Haifeng . Off-Grid Parameter Estimation for OFDM-based ISAC Systems with Incomplete Data . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi-domain uniform sampling method (MDU-sampling) for large-scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains, reducing the computational resources for registration. The proposed method preserves more effective information compared to other algorithms. Points are only considered on the same ring in LiDAR data as neighbouring points, eliminating the need for additional neighbouring point search. This makes it efficient and suitable for large-scale outdoor LiDAR point cloud registration. image
Keyword ：
artificial intelligence artificial intelligence robot vision robot vision signal processing signal processing SLAM (robots) SLAM (robots)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration [J]. | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| MLA | Ou, Wengjun et al. "MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration" . | ELECTRONICS LETTERS 60 . 5 (2024) . |
| APA | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration . | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
