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学者姓名：郑海峰
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Relying on a data-driven methodology, deep learning has emerged as a new approach for dynamic resource allocation in large-scale cellular networks. This paper proposes a knowledge-assisted domain adversarial network to reduce the number of poorly performing base stations (BSs) by dynamically allocating radio resources to meet real-time mobile traffic needs. Firstly, we calculate theoretical inter-cell interference and BS capacity using Voronoi tessellation and stochastic geometry, which are then incorporated into a neural network as key parameters. Secondly, following the practical assessment, a performance classifier evaluates BS performance based on given traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Our experimental results demonstrate that the proposed knowledge-assisted domain adversarial resource allocation (KDARA) strategy effectively decreases the number of poorly performing BSs in the cellular network, and in turn, outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption. IEEE
Keyword ：
domain adversarial network domain adversarial network Dynamic scheduling Dynamic scheduling knowledge-assisted knowledge-assisted Measurement Measurement Mobile big data Mobile big data Neural networks Neural networks Real-time systems Real-time systems resource allocation resource allocation Resource management Resource management transfer learning transfer learning Transfer learning Transfer learning Wireless networks Wireless networks
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| GB/T 7714 | Chen, Y. , Zheng, Y. , Xu, J. et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks [J]. | IEEE Transactions on Network and Service Management , 2024 , 21 (6) : 1-1 . |
| MLA | Chen, Y. et al. "Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks" . | IEEE Transactions on Network and Service Management 21 . 6 (2024) : 1-1 . |
| APA | Chen, Y. , Zheng, Y. , Xu, J. , Lin, H. , Cheng, P. , Ding, M. et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks . | IEEE Transactions on Network and Service Management , 2024 , 21 (6) , 1-1 . |
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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. IEEE
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink Federated learning Federated learning integrated sensing and communication integrated sensing and communication Optimization Optimization over-the-air computation over-the-air computation Radar Radar Task analysis Task analysis Uplink Uplink
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| GB/T 7714 | Du, M. , Zheng, H. , Gao, M. 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) : 1-1 . |
| MLA | Du, M. 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) : 1-1 . |
| APA | Du, M. , Zheng, H. , Gao, M. , Feng, X. , Hu, J. , Chen, Y. . Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE Internet of Things Journal , 2024 , 11 (21) , 1-1 . |
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The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark. IEEE
Keyword ：
Decoding Decoding Deep Learning for Visual Perception Deep Learning for Visual Perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR Mapping Mapping Occupancy Grid Occupancy Grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic Scene Understanding Semantic Scene Understanding Three-dimensional displays Three-dimensional displays
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| GB/T 7714 | Lai, Q. , Zheng, H. , Feng, X. et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE Robotics and Automation Letters , 2024 , 9 (10) : 1-8 . |
| MLA | Lai, Q. et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE Robotics and Automation Letters 9 . 10 (2024) : 1-8 . |
| APA | Lai, Q. , Zheng, H. , Feng, X. , Zheng, M. , Chen, H. , Chen, W. . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE Robotics and Automation Letters , 2024 , 9 (10) , 1-8 . |
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Traffic flow is considered as a critical feature of intelligent transportation systems (ITS). Accurately forecasting future vehicular volumes is an effective means of mitigating traffic congestion. However, the nonlinear and complex traffic flow characteristics make the traditional approaches unable to achieve satisfactory prediction performance. Although existing methods based on deep learning models have improved the accuracy of traffic flow prediction, the spatio-temporal features of traffic flow data are still not fully explored. Moreover, existing methods pay little attention to the task of training models in a decentralized environment where data are distributed across multiple clients. To solve the problems mentioned above, we propose a novel network model called Graph Transformer Attention Network (GTAN) for traffic flow prediction, which can effectively extract traffic flow's temporal and spatial characteristics by considering all node locations' information in the traffic networks. Then, we propose a training strategy called Graph Federated Meta-learning (FedGM), solving the problem of topological heterogeneity by combining meta-learning and federated learning, to achieve an optimal initialization model which can quickly adapt to different traffic networks under low communication cost. Finally, the experimental results on a real data set show that the GTAN model has better prediction performance and faster meta-training speed. The model trained by FedGM can quickly adapt to different graph-structured data and achieve high accuracy. IEEE
Keyword ：
Adaptation models Adaptation models Correlation Correlation Data models Data models Federated learning Federated learning Federated meta-learning Federated meta-learning Graph transformer networks Graph transformer networks Predictive models Predictive models Topological heterogeneity Topological heterogeneity Traffic flow prediction Traffic flow prediction Training Training Transformers Transformers
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| GB/T 7714 | Feng, X. , Sun, H. , Liu, S. et al. Federated Meta-Learning on Graph for Traffic Flow Prediction [J]. | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) : 1-13 . |
| MLA | Feng, X. et al. "Federated Meta-Learning on Graph for Traffic Flow Prediction" . | IEEE Transactions on Vehicular Technology 73 . 12 (2024) : 1-13 . |
| APA | Feng, X. , Sun, H. , Liu, S. , Guo, J. , Zheng, H. . Federated Meta-Learning on Graph for Traffic Flow Prediction . | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) , 1-13 . |
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The development of the extremely large-scale antenna array (ELAA) for the upcoming 6G technology indicates the significance of near-field communication. This work performs a near-field analysis to improve covertness when maximum ratio transmission (MRT) is employed with ELAA to send messages to the legitimate user. Specifically, we first derive the covertness constraint of the system by analyzing the beampattern. Based on this constraint, we introduce the concept of the vulnerable region, which is the region where covert communication is not achievable if a potential warden resides there. Furthermore, determining the vulnerable region involves deriving the range of distances by initially fixing the angle dimension, and then utilizing the covertness and the minimum effective throughput constraints to obtain the range of angle. The simulation results illustrate the efficacy of the determined vulnerable region in both distance and angle dimensions. As the azimuth angle or the distance between the legitimate user and the transmitter decreases, the area of the vulnerable region decreases. Additionally, increasing the number of warden's antennas or requiring a higher signal-to-noise ratio for legitimate user will expand the vulnerable region. IEEE
Keyword ：
Antennas Antennas Array signal processing Array signal processing Covert communication Covert communication near-field communication near-field communication Signal to noise ratio Signal to noise ratio Throughput Throughput Transmitting antennas Transmitting antennas Vectors Vectors vulnerable region vulnerable region Wireless communication Wireless communication
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| GB/T 7714 | Hu, J. , Zhou, Y. , Zheng, H. et al. Minimizing Vulnerable Region for Near-Field Covert Communication [J]. | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) : 1-6 . |
| MLA | Hu, J. et al. "Minimizing Vulnerable Region for Near-Field Covert Communication" . | IEEE Transactions on Vehicular Technology 73 . 12 (2024) : 1-6 . |
| APA | Hu, J. , Zhou, Y. , Zheng, H. , Chen, Y. , Shu, F. , Wang, J. . Minimizing Vulnerable Region for Near-Field Covert Communication . | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) , 1-6 . |
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Depth information is crucial for an autonomous driving system as it helps the system understand the environment and make decisions. Most deep learning-based depth completion methods are primarily designed for high-resolution lidars (e.g. 64 scanlines). However, when the number of lidar scanlines decreases, such as with 32 scanlines or 16 scanlines lidars, existing solutions may face challenges in reliably predicting dense depth maps. To address this issue, this paper proposes an effective framework based on knowledge distillation, which incorporates mixed-scanline resolution training and feature-level fusion to train a powerful teacher network that dynamically fuses features from high-scanline resolution and low-scanline resolution inputs. By supervising the student network based on the guidance of the teacher network, the knowledge from the multi-scale fusion teacher network is effectively transferred to the low-scanline resolution student network. For the inference process, only the student network is utilized. The proposed framework has been applied to various existing depth completion networks. The experimental results show the effectiveness of the proposed method by using the KITTI dataset, which shows that it can serve as a universal framework for depth completion tasks. © 2024 IEEE.
Keyword ：
deep learning deep learning depth completion depth completion knowledge distillation knowledge distillation LIDAR LIDAR multiple sensors multiple sensors
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| GB/T 7714 | Huang, J. , Zheng, H. , Feng, X. . Multi-Scale Distillation for Low Scanline Resolution Depth Completion [未知]. |
| MLA | Huang, J. et al. "Multi-Scale Distillation for Low Scanline Resolution Depth Completion" [未知]. |
| APA | Huang, J. , Zheng, H. , Feng, X. . Multi-Scale Distillation for Low Scanline Resolution Depth Completion [未知]. |
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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
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| 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 . |
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Cooperative perception is an advanced strategy within traffic systems designed to enhance the environmental perception capabilities of vehicles, where participants exchange cooperative perception messages (CPMs) through Vehicle-to-Everything (V2X) technology. However, most existing cooperative perception methods may ignore the communication bandwidth constraints of the system, potentially resulting in connected autonomous vehicles (CAVs) receiving outdated CPMs. In this paper, we propose a novel cooperative perception framework that enhances the accuracy of CAVs perception while reducing the transmission data size to meet the transmission delay requirements of CPMs under limited bandwidth. Furthermore, we propose a strategy for selecting cooperative partners and CPMs based on the Double Deep Q-Network (DDQN) algorithm. Additionally, an invalid action masking approach is presented to address the dynamic changes in the action space over time and reduce the size of the DDQN action space. Simulation results demonstrate that the proposed cooperative perception method consumes less data compared to some existing methods. Moreover, under limited communication bandwidth constraints, it achieves higher perception accuracy due to its ability to avoid transmission delay. © 2024 IEEE.
Keyword ：
Connected automated vehicles Connected automated vehicles cooperative perception cooperative perception deep reinforcement learning deep reinforcement learning invalid action masking invalid action masking
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| GB/T 7714 | Xu, F. , Chen, C. , Zheng, H. et al. Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
| MLA | Xu, F. et al. "Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks" [未知]. |
| APA | Xu, F. , Chen, C. , Zheng, H. , Feng, X. . Delay-Aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks [未知]. |
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Predicting traffic flow effectively alleviates congestion. However, traditional methods tend to rely solely on historical traffic flow data, overlooking the correlation between multimodal traffic data, such as speed and occupancy collected by sensors placed on the road. This limitation results in low tolerance for abnormal situations. Moreover, the decentralization of multimodal data on edge devices may pose data anomalies or partial modal missing due to equipment damage or absence. To address these challenges, we propose a Block-Term tensor decomposition-based multimodal data feature fusion algorithm for traffic prediction. This approach enhances the accuracy and robustness of traffic flow prediction by considering correlations between various modal data, such as speed and occupancy rate. In response to the issues of scattered multimodal data anomalies and missing data on edge devices, and to ensure address privacy and security issues, we employ federated learning methods to achieve adaptive extraction and fusion of multi-modal data at the edges. Our method is tested on a real highway dataset, demonstrating superior prediction performance and robustness compared to traditional methods, particularly in the context of data anomalies or missing modalities. © 2024 IEEE.
Keyword ：
Federated learning Federated learning Multimodal data Multimodal data Robustness Robustness Tensor decomposition Tensor decomposition Traffic flow Traffic flow
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| GB/T 7714 | Feng, S. , Feng, X. , Xu, L. et al. BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
| MLA | Feng, S. et al. "BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies" [未知]. |
| APA | Feng, S. , Feng, X. , Xu, L. , Zheng, H. . BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies [未知]. |
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Relying on a data-driven methodology, deep learning has emerged as a new approach for dynamic resource allocation in large-scale cellular networks. This paper proposes a knowledge-assisted domain adversarial network to reduce the number of poorly performing base stations (BSs) by dynamically allocating radio resources to meet real-time mobile traffic needs. Firstly, we calculate theoretical inter-cell interference and BS capacity using Voronoi tessellation and stochastic geometry, which are then incorporated into a neural network as key parameters. Secondly, following the practical assessment, a performance classifier evaluates BS performance based on given traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Our experimental results demonstrate that the proposed knowledge-assisted domain adversarial resource allocation (KDARA) strategy effectively decreases the number of poorly performing BSs in the cellular network, and in turn, outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption.
Keyword ：
domain adversarial network domain adversarial network Dynamic scheduling Dynamic scheduling knowledge-assisted knowledge-assisted Measurement Measurement Mobile big data Mobile big data Neural networks Neural networks Real-time systems Real-time systems resource allocation resource allocation Resource management Resource management transfer learning transfer learning Transfer learning Transfer learning Wireless networks Wireless networks
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| GB/T 7714 | Chen, Youjia , Zheng, Yuyang , Xu, Jian et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) : 6493-6504 . |
| MLA | Chen, Youjia et al. "Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 21 . 6 (2024) : 6493-6504 . |
| APA | Chen, Youjia , Zheng, Yuyang , Xu, Jian , Lin, Hanyu , Cheng, Peng , Ding, Ming et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) , 6493-6504 . |
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