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IEEE Transactions on Green Communications and networking 2024年第2期9卷 725-738页

作者： Meng, Meng Hu, Bo Chen, Shanzhi Kang, Shaoli Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China State Key Lab of Wireless Mobile Communication Beijing China

Due to the cooperative coverage characteristic of LEO satellites and non-uniform traffic demand of beam positions, allocating the limited beam and power resource to massive beam positions flexibly and effectively is a challenge in beam hopping LEO satellite communication system. The agents in existing beam hopping schemes, which rely on deep reinforcement learning, are limited to acquiring state information within the coverage area of LEO satellite. For this reason, we propose a cooperation multi-agent Value-Decomposition Networks with Dueling Double Deep Q-Learning Network (VDN-D3QN) framework to generate dynamic beam hopping pattern for assuring delay fairness and throughput among beam positions in LEO satellite communication system. The proposed VDN-D3QN dynamic beam hopping method is divided into training and test phase, where each agent is only responsible for the beam hopping pattern of one LEO satellite. During the train phase, the agents learn to cooperate with other agents to maximize the system throughput and minimize the delay fairness among beam positions by Dueling Double Deep Q-Learning Network. Then, the Value-Decomposition Networks is employed to learn the optimal policy in a centralized manner through interaction with the environment. In test phase, the trained agents are deployed to address the challenging problem of inter-satellite communication in a distributed manner, and one agent is deployed per LEO satellite. The trained agents can make decisions about the dynamic beam hopping pattern based on the available local state information in LEO satellite communication system. The evaluation results demonstrate that the proposed multi-agent VDN-D3QN algorithm can effectively handle the non-uniform traffic demand of multi-satellites simultaneously. Besides, the simulation results indicate that the proposed VDN-D3QN algorithm can allocate resource intelligently for adapting the requirements of beam positions and achieving better performance compared to

关键词： Satellite communication systems

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Transmit Beamforming Design for Multi-UE Multi-Target ISAC Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第11期13卷 3094-3098页

作者： Qiu, Ouyang Zhang, Jiupeng Yan, Shi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

This letter proposes an integrated sensing and communication (ISAC) beamforming method that senses multiple moving targets while simultaneously communicating with multiple user equipment (UE). The radar estimation rate is combined with the communication achievable rate to assess ISAC system performance. Semidefinite relaxation (SDR) and successive convex approximation (SCA) are used to handle the non-convex rank constraint and convexify the objective function, respectively. To reduce the computational complexity, a manifold-based algorithm is proposed as an alternative. Simulation results validate the effectiveness of the proposed schemes.

关键词： Array signal processing Radar Estimation Robot sensing systems Integrated sensing and communication Signal to noise ratio Measurement ISAC beamforming design radar estimation rate

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airTac: A Contactless Digital Tactile Receptor for Detecting Material and Roughness via Terahertz Sensing

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS techNOLOGIES-IMWUT 2024年第3期8卷 1-37页

作者： Zhang, Zhan Song, Denghui Zhou, Anfu Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Tactile sensing is an indispensable capability for humans or intelligent devices to engage in complex physical interactions. Mainstream tactile sensors are contact-based, which show limitation in measuring deformable objects and demanding high maintenance effort. As a complementary solution, a new paradigm of contactless tactile sensing is attracting much interest. While promising, they can only identify coarse-grained single tactile perception properties, either material type or surface roughness. In this paper, we propose airTac, which includes a novel contactless digital tactile receptor model, capable of simultaneously extracting these two basic tactile properties in a fine-grained resolution, by harnessing the massive bandwidth of terahertz(THz) frequency band. airTac is designed based on our key finding: while the impact of material type and surface roughness on THz signal intertwine with each other, they are actually separable, i.e., roughness manifests a certain pattern in distorting relative high-frequency components of the whole THz spectrum. Therefore, we custom-design a bio-inspired deep neural network model to decouple the intertwined THz signal, and distill the tactile perception properties embedded underlying the signal. We prototype airTac using a THz time domain spectroscopy, and perform extensive evaluation over 9 different daily material types with 39 different surface roughness. airTac achieves a material identification accuracy of 97.43% and a roughness classification accuracy of 91.46%, which demonstrates that airTac can identify common materials in daily life and exceed a fingertip spatial resolution of 1mm.

关键词： tactile sensing contactless sensing terahertz sensing tactile perception computing model contactless digital tactile receptor

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RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第11期46卷 7088-7101页

作者： Wang, Haowen Che, Zhengping Yang, Yufan Wang, Mingyuan Xu, Zhiyuan Qiao, Xiuquan Qi, Mengshi Feng, Feifei Tang, Jian Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Midea Grp Foshan 528311 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China

Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent materials frequently elude detection by depth sensors;surfaces may introduce measurement inaccuracies due to their polished textures, extended distances, and oblique incidence angles from the sensor. The presence of incomplete depth maps imposes significant challenges for subsequent vision applications, prompting the development of numerous depth completion techniques to mitigate this problem. Numerous methods excel at reconstructing dense depth maps from sparse samples, but they often falter when faced with extensive contiguous regions of missing depth values, a prevalent and critical challenge in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. The other branch applies an RGB-depth fusion CycleGAN, adept at translating RGB imagery into detailed, textured depth maps while ensuring high fidelity through cycle consistency. We fuse the two branches via adaptive fusion modules named W-AdaIN and train the model with the help of pseudo depth maps. Comprehensive evaluations on NYU-Depth V2 and SUN RGB-D datasets show that our method significantly enhances depth completion performance particularly in realistic indoor settings.

关键词： Depth completion generative adversarial network indoor environment indoor environment RGB-depth fusion RGB-depth fusion indoor environment RGB-depth fusion

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WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 3633-3649页

作者： Xu, Leiyang Zheng, Xiaolong Du, Xinrun Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We propose WiCamera, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only 3 x 3 MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing. WiCamera emits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane, WiCamera generates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows that WiCamera performs consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.

关键词： Channel state information vortex electromagnetic wave orbital angular momentum orbital angular momentum ray tracing ray tracing posture imaging posture imaging posture imaging

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Wideband Beamforming With RIS: A Unified Framework via Space-Frequency Transformation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 173-187页

作者： Qian, Xiaowei Hu, Xiaoling Liu, Chenxi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.

关键词： Reconfigurable intelligent surfaces Array signal processing Wideband Narrowband Sensors Millimeter wave communication Delays Time-frequency analysis Standards RF signals Reconfigurable intelligent surface wideband beamforming space-frequency Fourier transformation stationary phase method

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Multi-source data based anomaly detection through temporal and spatial characteristics

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Xu, Peng Gao, Qihong Zhang, Zhongbao Zhao, Kai Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol 10 Xitucheng Rd Beijing Peoples R China

Anomaly detection is vital in complex distributed systems. However, existing methods did not take into full account the temporal and spatial characteristics of data from multiple sources in the system. That will lead to less accurate of anomaly detection. To address this limitation, in this paper we propose a multi-source data based anomaly detection method through temporal and spatial characteristics. Specifically, we first considered the spatial characteristics. We propose a Transformer encoder to parse log templates and output template vectors, then an attention-based CNN is introduced to obtain the spatial characteristics from traces. Next, we considered the temporal characteristics. We propose Bi-LSTM network to obtain the temporal characteristics of multi-source data of the distributed systems, followed by anomaly detection. Finally, extensive comparative experiments verify the effectiveness of our method, the F1-score reaches 0.859 and improves by 2.14% compared to state-of-the-art methods.

关键词： Anomaly detection Log Distributed systems LSTM CNN

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A Joint Multiservice Transmission Scheme for RSMA-Aided Cell-Free mMIMO System

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IEEE COMMUNICATIONS LETTERS 2023年第2期27卷 591-594页

作者： Wang, Wenjie Li, Lihua Deng, Gang Li, Jilong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Acad Broadcasting Sci State Key Lab Networking & Switching Technol Beijing 100469 Peoples R China

In this letter, we propose a joint multi-service (MS) transmission scheme based on cell-free (CF) massive MIMO architecture. Based on the user-centric (UC) principle, we perform access point (AP) selection and form broadcast, multicast, and unicast messages at each AP according to the rate splitting multiple access (RSMA) technique, then we carry out the joint transmission of MS signals. In terms of system optimization, a novel three-step resource allocation algorithm is proposed to deal with the non-convex problem of achievable sum-rate maximization which downscales multidimensional variables to solve the one-dimensional problems of AP selection, intra-AP power ratio determination, and inter-AP power allocation in steps. We provide achievable sum-rate analyses backed by numerical results to show the superiority of the proposed scheme and optimization algorithm compared with the baselines.

关键词： Interference Unicast Signal to noise ratio Resource management Optimization Channel estimation NOMA Cell-free massive MIMO rate-splitting multiple access resource allocation achievable sum-rate

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A Two-layer Federated Learning for LEO Constellation in IoT 7

A Two-layer Federated Learning for LEO Constellation in IoT

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IEEE 7th International Workshop on Radio Frequency and Antenna technologies (IEEE IWRF and AT)

作者： Zhou, Ai Wang, Ying Zhang, Qiuyang State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350384765;9798350384772

The rapid growth of low earth orbit (LEO) satellite communications has become pivotal in the realm of satellite Internet of Things (IoT), facilitating data collection and transmission. Due to the wide coverage and large volume of data collected in satellites, it can be used as training data for machine learning. Federated learning (FL) emerges as an apt paradigm for collaborative training across multiple satellites, mitigating the need for excessive communication resources. However, the high mobility of LEO satellites introduces communication asynchrony, posing challenges in FL, such as learning delays. To address this challenge, we propose a two-layer FL architecture with an innovative asynchronous learning design. This design not only mitigates learning delays but also reduces transmission scheduling complexity. Simulation results show that compared with the benchmarks (FedAvg and FedAsync), our proposed scheme has better performance in accuracy and learning speed.

关键词： Federated learning ISLs satellite Internet of Things satellite constellations

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A Lightweight SAR Ship Detection Network Based on Deep Multiscale Grouped Convolution, Network Pruning, and Knowledge Distillation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 2190-2207页

作者： Hu, Boyi Miao, Hongxia Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Deep learning has proven to be highly effective in synthetic aperture radar (SAR) image target detection. However, many latest deep learning models have predominantly focused on increasing depth and size to enhance detection accuracy, often ignoring the balance between accuracy and detection speed, as well as the practical deployment of these models on hardware platforms. Therefore, a lightweight algorithm for SAR ship detection is designed in this article. First, we propose a preliminary lightweight scheme, including a multiscale feature learning augmented backbone, a lightweight feature fusion neck, and a parameter-sharing lightweight detection head. Second, unimportant branches of the network are pruned to further compress the model. Finally, the detection accuracy of the model is enhanced by knowledge distillation without augmenting the model volume, which compensates for the accuracy loss caused by model compression. Experimental validation is conducted on three SAR image ship detection datasets (SSDD, high-resolution SAR images dataset, large-scale SAR ship detection dataset-v1.0) to thoroughly assess the effectiveness of the proposed lightweight algorithm. Experimental results on the three datasets demonstrate that the proposed method achieves a model volume reduction to one-third of the baseline while maintaining a minimal decrease in detection accuracy. In SSDD, the proposed method achieved 98.7 accuracy, 0.92M parameters, 3.1G FLOPS and 2.1 MB size of 1.5X pruning rate. Furthermore, it outperforms other state-of-the-art lightweight detectors.

关键词： Computational modeling Accuracy Convolution Marine vehicles Synthetic aperture radar Solid modeling Feature extraction Detectors Adaptation models Remote sensing Convolutional neural network (CNN) knowledge distillation (KD) lightweight network pruning synthetic aperture radar (SAR) target detection

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