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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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Knowledge-enhanced semantic communication system with OFDM transmissions

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Science China(Information Sciences) 2023年第7期66卷 266-282页

作者： Xiaodong XU Huachao XIONG Yining WANG Yue CHE Shujun HAN Bizhu WANG Ping ZHANG State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Department of Broadband Communication Peng Cheng Laboratory

As a promising technology to enable effective multi-modal transmission over wireless channels,semantic communication has attracted a lot of attention from academics and industries. Different from Shannon's information theory, based on common background knowledge provided by the knowledge base, the goal of semantic communication is transmitting intended useful information from the transmitter and recovered by the receiver at the semantic level. However, the existing studies on semantic communication rarely emphasize the essence and the usage of the knowledge base. In this paper, we propose a knowledge-enhanced semantic communication(KESC) system, where the knowledge base is cloud-edge-device collaborative cached. To solve the problem that float-type symbols are difficult to transmit directly through a radio frequency(RF)system, we adopt orthogonal frequency division multiplexing(OFDM) to transmit semantic vectors directly without some traditional signal processing techniques in semantic information transmission, and the semantic pilot is designed to assist semantic reception. Furthermore, we formulate a multi-encoder transformer based neural network model for the KESC system to support text transmission(KESC-T), where the decoder is implemented with a knowledge graph to enhance the performance of semantic decoding. Besides, we define knowledge-enhanced efficiency(KEE) to measure the gain in semantic recovery accuracy brought by per unit of knowledge. Simulation results demonstrate that the recovery accuracy of our proposed KESC outperforms the compared scheme, especially in low signal-to-noise ratio(SNR) or resource-constrained scenarios.

关键词： semantic communication knowledge graph OFDM transformer deep learning

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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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Near-Field High-Speed User Sensing in Wideband mmWave Communications: Algorithms and Bounds

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 919-935页

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

Integrated sensing and communications (ISAC) has been expected to be a key technique in the sixth-generation cellular networks. With the increase of carrier frequency (to millimeter-wave or Terahertz spectrum) and antenna array size (to extremely large-scale antenna) in wireless communications, the near-field area is enlarged and cannot be ignored. Accordingly, the channel model and its estimation algorithms are changed, which bring new chances in ISAC. However, the impact of both Doppler and spatial wideband effects, caused by high mobility and multicarriers, on sensing performance using communication signals is not well studied. In this study, these two effects are shown to be helpful in user sensing. First, the channel model is proposed for a high-speed moving user transmitting an orthogonal frequency division multiplex (OFDM) signal, where there are six unknown parameters. Then, the Cramer-Rao lower bounds (CRLB) for joint six parameter estimation is determined, where the impact of the near-field parameter and the velocity on the CRLB of positioning are discussed and quantified. Further, to compensate for the deficiency that the CRLB is tight only in high signal-to-noise-ratio (SNR) scenarios, we derive the Ziv-Zakai bound (ZZB) for positioning by exploiting the prior information on positioning parameters. Subsequently, a joint position and velocity parameter estimation algorithm is designed by first performing a discrete fractional Fourier transform on the received signal to obtain a coarse estimation and then refining it by Newton-based refinement. Numerical results coincide with our analysis.

关键词： OFDM Estimation Signal processing algorithms Channel estimation Chirp Frequency estimation Training Signal to noise ratio Sensors Matching pursuit algorithms Near-field ISAC high-speed moving CRLB ZZB

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Multi-Frequency Channel Measurement in Smart Factories: Comparative Analysis with 3GPP InF Scenarios 100

Multi-Frequency Channel Measurement in Smart Factories: Comp...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Mo, Qingmei Zhang, Yuxiang Wang, Jialin Liu, Yameng Chai, Zeyong Gong, Huiwen Zhang, Jianhua Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China

ISBN: (纸本)9798331517786

Smart factory scenarios in the Industrial Internet of Things (IIoT) introduce novel challenges to wireless communication, characterized by large-scale device interaction, strong reflection, and dense scattering, diverging from typical factories. To ascertain the channel disparities between smart factories and typical factories, we analyze channel characteristics across sub-6 GHz (e.g., 6 GHz) and mmWave frequencies (e.g., 28 and 39 GHz) in smart factory scenarios, establishing a multi-frequency path loss model. Furthermore, the comparison between the large-scale fading characteristics, including path loss and root mean square delay spread (RMS DS), observed in smart factory scenarios and those in the typical indoor factory (InF) scenarios defined by the 3rd-Generation Partnership Project (3GPP). Additionally, channel characteristics of the metal and non-metal scatterer areas in the smart factories are extracted to analyze the impact of metal scatterers on angle spread (AS) and RMS DS. © 2024 IEEE.

关键词： Smart manufacturing

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Differentially private generative decomposed adversarial network for vertically partitioned data sharing

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INFORMATION SCIENCES 2023年 619卷 722-744页

作者： Wang, Zhenya Cheng, Xiang Su, Sen Wang, Guangsheng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

This paper considers the problem of differentially private vertically partitioned data shar-ing. In particular, with the assistance of a semi-honest curator, the involved parties (i.e., data owners) each holds records regarding different features of the same set of individuals collectively generate a shared dataset while satisfying differential privacy. Motivated by the superiority of the generative adversarial network (GAN) in data synthesizing, we pre-sent a differentially private generative decomposed adversarial network (DPGDAN) approach for vertically partitioned data sharing. In DPGDAN, the discriminator in the initial GAN is decomposed into several local discriminators and two relational discriminators, i.e., a real relational discriminator and a fake relational discriminator. Each party is assigned with a local discriminator while the curator holds the generator and the two relational dis-criminators. By combining the sanitized feedback from the local discriminators and the outputs of the relational discriminators, the curator can update the generator to approxi-mate the distribution of the integrated dataset without compromising each party's privacy. Moreover, to promote the shared data's utility, we design a snapshot aggregation method by aggregating the synthetic records produced by the generators acquired during training. Furthermore, we show that DPGDAN satisfies (epsilon, delta)- differential privacy. Extensive experi-ments validate the effectiveness of DPGDAN.

关键词： Differential privacy Multiple parties Data sharing Vertically partitioned data Generative adversarial network

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Classification-labeled Continuousization and Multi-Domain Spatio-Temporal Fusion for Fine-Grained Urban Crime Prediction

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第7期35卷 6725-6738页

作者： Zhao, Shuai Liu, Ruiqiang Cheng, Bo Zhao, Daxing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Beijing 100876 Peoples R China

Fine-grained urban crime prediction is of great significance to urban management and public safety. Previous crime prediction work has been done at a relatively coarse time granularity, which may suffer from two issues for fine-grained crime prediction. 1) The zero-inflation problem associated with fine-grained granularity. Crime occurrence is sparse, and when the time granularity becomes finer, it leads to a more sparse prediction label for this problem resulting in the zero inflation problem. 2) Insufficient amount of information involved in crime datasets. When the spatio-temporal granularity becomes smaller, more information from related fields needs to be introduced to extract spatio-temporal features to assist the analysis. To address the first issue, we introduce a classification-labeled continuousization strategy and a weighted loss function for sparse classification problem, making the model more likely to focus on non-zero elements in zero-inflated datasets. For the second issue, we propose a novel deep learning based model, termed attention-based spatio-temporal multi-domain fusion network, which fuses features from multiple datasets in related domains. We evaluate our method on six real-world datasets collected in New York City and experiments on our model show the advantages beyond many competitive baselines.

关键词： Spatiotemporal data mining urban computing deep learning

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Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

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China Communications 2024年第2期21卷 227-244页

作者： Meng Meng Bo Hu Shanzhi Chen Jianyin Zhang State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Lab of Wireless Mobile Communication China Academy of Telecommunication TechnologyBeijing 100191China Research Institute of China Mobile Beijing 100053China

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system *** optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method ***, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

关键词： convex optimization intelligent reflecting surface LEO satellite communication OFDM

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A novel synchrosqueezing transform associated with linear canonical transform

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SIGNAL PROCESSING 2025年 227卷

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

Synchrosqueezing transforms have aroused great attention for its ability in time-frequency energy rearranging and signal reconstruction, which are post-processing techniques of the time-frequency distribution. However, the time-frequency distributions, such as short-time Fourier transform and short-time fractional Fourier transform, cannot change the shape of the time-frequency distribution. The linear canonical transform (LCT) can simultaneously rotate and scale the time-frequency distribution, which enlarges the distance between different signal components with proper parameters. In this study, a convolution-type short-time LCT is proposed to present the time-frequency distribution of a signal, from which the signal reconstruction formula is given. Its resolutions in time and LCT domains are demonstrated, which helps to select suitable window functions. A fast implementation algorithm for the short-time LCT is provided. Further, the synchrosqueezing LCT (SLCT) transform is designed by performing synchrosqueezing technique on the short-time LCT. The SLCT inherits many properties of the LCT, and the signal reconstruction formula is obtained from the SLCT. Adaptive selections of the parameter matrix of LCT and the length of the window function are introduced, thereby enabling proper compress direction and resolution of the signal. Finally, numerical experiments are presented to verify the efficiency of the SLCT.

关键词： Synchrosqueezing transform Generalized convolution operation LCT Chirp Linear time-varying system

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Estimation of displacement operations based on Fock states considering thermal noise in preparing probes

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OPTICS LETTERS 2025年第7期50卷 2413-2416页

作者： Di, Shijie Yuan, Renzhi Yang, Chuang Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 10013 Peoples R China

Displacement based quantum receivers can beat the standard quantum limit and have gained increasing attention in recent years. The displacement operation value can be estimated and calibrated by using Fock states as probes. In this Letter, we focus on the impacts of thermal noise when preparing the Fock probes on the estimation performance. We first derive the density matrix and the photon statistics of the noisy displaced number state with a uniformly distributed displacement phase. Based on the derived density matrix, we then analyze the performance of estimating displacement operations by using the maximum likelihood estimation (MLE) method. To decrease the computational complexity of the MLE, we further derive an approximated probability density of the photon numbers under small displacement. Besides, the quantum Cram & eacute;r-Rao bound (QCRB) is also derived to analyze the performance limit of the displacement estimation. Numerical results found that, though thermal noise can greatly degrade the MLE accuracy of the displacement operations, the performance advantage of using Fock probes still persists over classical coherent probes. Besides, we demonstrate that the MLE method based on Fock probes can well approach the QCRB when the displacement strength Nc <= similar to 1. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Coherent states Optical amplifiers Phase Photon statistics Quantum beats Quantum information

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