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学者姓名：郑海峰
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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
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| 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 . |
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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
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| 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 . |
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Decentralized federated learning (DFL) is a novel distributed machine-learning paradigm where participants collaborate to train machine-learning models without the assistance of the central server. The decentralized framework can effectively overcome the communication bottleneck and single-point-of-failure issues encountered in federated learning (FL). However, most existing DFL methods may ignore the communication resource constraints of the system. This may result in these methods unsuitable for many practical scenarios because the given resource constraints cannot be guaranteed. In this article, we propose a novel DFL, called DFL with adaptive compression ratio (AdapCom-DFL), that can adaptively adjust the compression ratio of transmission data to keep the communication latency within the constraint. Furthermore, we propose a communication network topology pruning approach to reduce communication overhead by pruning poor links with low data rates while ensuring the convergence. Additionally, a power allocation approach is presented to improve the performance by reallocating the power of communication links while complying with the communication energy constraint. Extensive simulation results demonstrate that the proposed AdapCom-DFL with network pruning and power allocation approach achieves better performance and requires less bandwidth under the given resource constraints compared with some existing approaches.
Keyword ：
Decentralized federated learning (DFL) Decentralized federated learning (DFL) network topology pruning network topology pruning power allocation power allocation
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| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Gao, Min et al. Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) : 10739-10753 . |
| MLA | Du, Mengxuan et al. "Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks" . | IEEE INTERNET OF THINGS JOURNAL 11 . 6 (2024) : 10739-10753 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Gao, Min , Feng, Xinxin . Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) , 10739-10753 . |
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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)
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| 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) . |
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We consider the problem of hyperspectral image (HSI) reconstruction, which aims to recover 3D hyperspectral data from 2D compressive HSI measurements acquired by a coded aperture snapshot spectral imaging (CASSI) system. Existing deep learning methods have achieved acceptable results in HSI reconstruction. However, these methods did not consider the imaging system degradation pattern. In this article, based on observing the initialized HSIs obtained by shifting and splitting the measurements, we propose a dynamic Fourier network based on degradation learning, called the degradation-aware dynamic Fourier-based network (DADF-Net). We estimate the degradation feature maps from the degraded hyperspectral images to realize the linear transformation and dynamic processing of the features. In particular, we use the Fourier transform to extract the HSI non-local features. Extensive experimental results show that the proposed model outperforms state-of-the-art algorithms on simulation and real-world HSI datasets.
Keyword ：
Convolution Convolution Deep learning Deep learning Degradation Degradation Feature extraction Feature extraction fourier transform fourier transform Heuristic algorithms Heuristic algorithms hyperspectral images hyperspectral images Image reconstruction Image reconstruction Imaging Imaging Mathematical models Mathematical models snapshot compressive imaging snapshot compressive imaging
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| GB/T 7714 | Xu, Ping , Liu, Lei , Zheng, Haifeng et al. Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 2838-2850 . |
| MLA | Xu, Ping et al. "Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 2838-2850 . |
| APA | Xu, Ping , Liu, Lei , Zheng, Haifeng , Yuan, Xin , Xu, Chen , Xue, Lingyun . Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 2838-2850 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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Due to the rapidly increasing number of base stations (BSs) in the operational cellular networks, their energy consumption is escalating. In this paper, we propose an intelligent data-driven BS sleeping mechanism relying on a wireless traffic prediction model that measures the BSs' capacity in different regions. Firstly, a spatio-temporal cellular traffic prediction model is proposed, where a multi-graph convolutional network (MGCN) is developed to capture the associated spatial features. Furthermore, a multi-channel long short-term memory (LSTM) solution involving hourly, daily, and weekly periodic data is used to capture the relevant temporal features. Secondly, the capacities of macro-cell BSs (MBSs) and small-cell BSs (SBSs) having different environment characteristics are modeled, where both clustering and transfer learning algorithms are adopted for quantifying the traffic supported by the MBSs and SBSs. Finally, an optimal BS sleeping strategy is proposed for minimizing the network's power consumption. Experimental results show that the proposed MGCN-LSTM model outperforms the existing models in terms of its cellular traffic prediction accuracy, and the proposed BS sleeping strategy using an approximated non-linear model of the associated capacity function achieves near-maximal energy-saving at a modest complexity.
Keyword ：
BS sleeping BS sleeping Cellular networks Cellular networks cellular traffic prediction cellular traffic prediction Convolution Convolution Convolutional neural networks Convolutional neural networks Energy consumption Energy consumption graph convolutional network graph convolutional network Predictive models Predictive models Real-time systems Real-time systems Roads Roads transfer learning. transfer learning.
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| GB/T 7714 | Lin, Jiansheng , Chen, Youjia , Zheng, Haifeng et al. A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) : 5627-5643 . |
| MLA | Lin, Jiansheng et al. "A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 6 (2024) : 5627-5643 . |
| APA | Lin, Jiansheng , Chen, Youjia , Zheng, Haifeng , Ding, Ming , Cheng, Peng , Hanzo, Lajos . A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) , 5627-5643 . |
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