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FINT: Freshness-Based In-Band Network-Wide Telemetry in Resource-Constrained Environments

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1314-1329页

作者： Zhong, Peiran Zhou, Fanqin Feng, Lei Li, Wenjing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

A new network monitoring technology called in-band network telemetry (INT) offers users the ability to gather precise, real-time data about the entire network. While several studies have used in-band network telemetry for network-wide monitoring, it is insufficient for the growing number of massive networks that are resource-constrained. Finding the most valuable information with the least amount of resources is challenging. In this paper, We formalize the problem of in-band network-wide telemetry with low resource overheads. This formalized problem aims to achieve high freshness of network performance data using fewer resources and reducing deployment and maintenance costs for O&M personnel. We propose a heuristic method based on path reorganization to address this issue. The heuristic algorithm for planning paths starts from greedy path planning results and finds a more appropriate planning scheme by merging and reorganizing paths. Additionally, probabilistic insertion is considered to reduce the impact of intrusiveness of in-band network telemetry. Simulation results show that our approach is effective in improving resource utilization and reducing the cost of monitoring the network compared to similar studies.

关键词： Age of information data plane programmability in-band network telemetry (INT) in-band network telemetry (INT) network management network management P4 P4 P4

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Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario

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SENSORS 2025年第1期25卷 269-269页

作者： Cao, Hui Han, Shujun Meng, Rui Xu, Xiaodong Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Satellite-ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser's model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions.

关键词： semantic communication satellite-ground scenarios channel adaptive image transmission

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Part-Level Relationship Learning for Fine-Grained Few-Shot Image Classification

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1448-1460页

作者： Wang, Chuanming Fu, Huiyuan Liu, Peiye Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Recently, an increasing number of few-shot image classification methods have been proposed, and they aim at seeking a learning paradigm to train a high-performance classification model with limited labeled samples. However, the neglect of part-level relationships causes few-shot methods to struggle to distinguish between closely similar subcategories, which makes it difficult for them to solve the fine-grained image classification problem. To tackle this challenging task, this paper proposes a fine-grained few-shot image classification method that exploits both intra-part and inter-part relationships among different samples. To establish comprehensive relationships, we first extract multiple discriminative descriptors from the input image, representing its different parts. Then, we propose to define the metric spaces by interpolating intra-part relationships, which can help the model adaptively find clear boundaries for these confusing classes. Finally, since the unlabeled image has high similarities to all classes, we project these similarities into a high-dimension space according to the inter-part relationship and interpolate a parameterized classifier to discover the subtle differences among these similar classes. To evaluate our proposed method, we conduct extensive experiments on various fine-grained datasets. Without any pre-train/fine-tuning process, our approach clearly outperforms previous few-shot learning methods, which demonstrates the effectiveness of our approach.

关键词： Measurement Feature extraction Image classification Extraterrestrial measurements Few shot learning Prototypes Neural networks Metalearning Image color analysis Hands Fine-grained image classification few-shot learning metric learning part-level relationship learning

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Growth-adaptive distillation compressed fusion model for network traffic identification based on IoT cloud-edge collaboration

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AD HOC NETWORKS 2025年 167卷

作者： Yang, Yang Fan, Chengwen Chen, Shaoyin Gao, Zhipeng Rui, Lanlan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The development of the Internet of Things (IoT) has led to the rapid growth of the types and number of connected devices and has generated large amounts of complex and diverse traffic data. Traffic identification on edge servers solves the real-time and privacy requirements of IoT management and has attracted much attention, but still faces several problems: (1) traditional machine learning (ML) models rely on artificially constructed features, and the existing deep learning (DL) traffic identification models have reached their performance limit;and (2) insufficient computing resources of edge servers limit the possible improvement in the performance of deep learning models by increasing the number of parameters and structural complexity. To address these issues, we propose a lightweight fusion model. First, the Network-in-Network (NiN) model and Random Forest (RF) model are used on the cloud server to construct a traffic identification fusion model. The excellent representation extraction capability of the NiN compensates for the RF's dependence on manual feature extraction, and its modular structure is suitable for the subsequent model compression operations. Then, the NiN was distilled. We propose Growth-Adaptive Distillation to lightweight the NiN model, which can reduce the operation of manually adjusting the structure of the student model and ensure the efficiency and low power consumption of the fusion model deployment. In addition, both the RF in the cloud and the distilled NiN are deployed on the edge server. Comparisons with multiple algorithms on two network traffic datasets show that the proposed model achieves state-of-the-art performance while ensuring the use of minimal computational resources.

关键词： IoT Traffic identification Network-in-network Random forest Knowledge distillation Fusion model

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Quantum-Assisted Hierarchical Fuzzy Neural Network for Image Classification

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第1期33卷 491-502页

作者： Wu, Shengyao Li, Runze Song, Yanqi Qin, Sujuan Wen, Qiaoyan Gao, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem. In this article, we proposed a novel quantum assisted hierarchical fuzzy neural network (QA-HFNN). Different from classical fuzzy neural networks, QA-HFNN uses quantum neural networks (QNNs) to learn fuzzy membership functions. The model is a multifeature fusion learning algorithm with a parallel structural design that integrates quantum and classical neural networks. The classical network is used to capture high-dimensional neural features, the QNNs are designed to capture fuzzy logic features of the data, then, the two features are fused to form the final features to be classified. The experiment is performed on a classical computer, and the quantum circuit is built through a simulated quantum environment. The results indicate that the accuracy of QA-HFNN can equal to or even surpass classical methods in image classification tasks. The quantum circuit utilizes only a single qubit which is easy to implement. In addition, the fidelity of quantum circuit in a quantum noise environment is assessed, demonstrating that QA-HFNN has strong robustness. The time and computational complexity of QNNs was analyzed, further proving the effectiveness of the model.

关键词： Fuzzy logic Quantum computing Feature extraction Classification algorithms Image classification Task analysis Fuzzy neural networks Fuzzy member function fuzzy neural network image classification quantum neural network (QNN)

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Fast and Efficient Beam Alignment for Terahertz Communication via Sensing DoA of Leaky Waves From Intermediate Frequency Ports

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 6196-6207页

作者： Yang, Chuang Zhang, Hongjie Wang, Yang Liu, Yuanwei Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Terahertz (THz) offers the availability of huge bandwidth to provide unprecedented data rate for the sixth-generation mobile communication and beyond. Since the narrow beam would be transmitted in the terminals to address severe path loss, THz beam alignment has been a source of significant overhead. The Direction of Arrival (DoA) sensed from a low-frequency band communication system could help fast beam alignment of THz communication. However, it is inefficient since it wastes hardware sources and requires exchange information between the sub-6 GHz and THz systems. In this article, based on the spatial similarity of the intermediate frequency (IF) channel and THz channel, a THz communication system is designed by sensing the DoA of the leaky waves from IF ports of the classical superheterodyne communication structure to aid the THz beam alignment, in which the IF part is controlled by a switching network. It is fast and efficient, since the DoA is employed and no additional communication baseband is used. Compared to the conventional beam alignment methods, the proposed shows well performance on accuracy, reactiveness and overhead, especially in scenarios for limited channel measurement as well as massive multiple-input multiple-output which are regular in THz communication.

关键词： Terahertz communications Switches OFDM Channel models Vectors Sensors Internet of Things Baseband Antennas Receivers Beam alignment Direction of Arrival (DoA) intermediate frequency (IF) leaky waves terahertz (THz) communication

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A congestion-aware user-cell association scheme to assist with traffic offloading in a three-tier heterogeneous network

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COMPUTER NETWORKS 2024年 245卷

作者： Han, Xiaoli Hu, Bo Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

In this work, we aim to expand the cell range in congestion areas. To achieve this goal, we integrate UAV network into macro-micro cellular network to enhance coverage. However, the gain of deploying heterogeneous nodes is limit if there are no CRE (cell range expanding) -based method to steer users towards low -power nodes. Therefore, in order to further improve system access capacity, we propose a congestionaware UA (user-cell association) scheme to offload queued traffic of cellular network into UAV network. To verify the superiority of proposed scheme, we utilize the low -complexity SG (stochastic -geometry) methods. Then in simulation analysis phase, we obtain five primary results: ( a ). The SG -based methods have lower computation complexity, and the convergence of derived expressions are verified. ( b ). The SG -based methods do not need the exact users' location but the users' density, which have improved the operability and robustness in infrastructure construction of wireless network. ( c ). The congestion -aware UA scheme can prompt network to adjust association tier flexibly, which is very suitable for traffic unevenly -distributed regions. ( d ). The optimal offsets of virtual power are estimated as B 2 = -4 dB for small -cell and B 3 = 3 dB for drone -cell, the resulting improvement in communication capacity is 8%. ( e ). We suggest the deployment density of UAV sites will no need to exceed 2 3 = 41 (pks/km 2 ).

关键词： Traffic offloading User-cell association scheme Integrated aerial-terrestrial network Stochastic geometry

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Enhancing QoE of Adaptive Video Streaming by Generating Fine-Grained Throughput

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO techNOLOGY 2025年第4期35卷 3853-3866页

作者： An, Congkai Zhang, Huanhuan Kang, Jingyang Liu, Zhuo Zhou, Anfu Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

On-demand video streaming continues to dominate the Internet, posing a formidable challenge in designing efficient adaptive bitrate (ABR) algorithms to enhance user quality-of-experience (QoE), particularly amplified by increasing video resolutions (e.g., from 1080P to 2K, 4K, and even 8K) and dynamic Internet conditions. Through a comprehensive study, we identify a common limitation in both existing throughput-based and hybrid-based ABR algorithms: they rely on coarse-grained network bandwidth estimation, missing detailed and accurate (i.e., millisecond-level) network variations. This often leads to misguided resolution (corresponding to bitrate level) decisions, resulting in unsatisfactory QoE. In this work, we propose SuperABR, a fine-grained throughput-driven ABR solution aimed at achieving the optimal bitrate adaptation. To accomplish this, SuperABR first incorporates a two-stage learning module, generating fine-grained future throughput to provide a near-Oracle network view. SuperABR then uses this fine-grained throughput to accurately calculate the download duration for a video chunk, transforming it into the optimal resolution decision via a custom-designed QoE benefit model. We have implemented SuperABR as a lightweight plug-in interface on a standard DASH framework and evaluate it over extensive real-world network traces. Extensive experiments demonstrate that SuperABR can generate accurate future throughput, resulting in a remarkable 1.21 similar to 1.46x QoE improvement over classic ABR solutions.

关键词： ABR algorithm fine-grained throughput QoE optimization

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Terahertz Hybrid Precoding With Low-Resolution PSs Under Frequency Selective Channel: A Partial Decoupling Method

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IEEE TRANSACTIONS ON BROADCASTING 2025年

作者： Wang, Yang Yang, Chuang Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Terahertz (THz) communication is considered as one of the most critical technologies for 6G broadcasting communications because of its abundant bandwidth. To compensate for the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend the broadcasting communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO, and low-resolution PSs are typically adopted in practice. However, low-resolution PSs cause severe performance degradation, which also poses challenges for the design of analog precoders for multi-carrier systems. Moreover, THz communication with broadband suffers severe frequency selective fading, further increasing the analog precoder design difficulty. Motivated by the above factors, in this paper, we propose a new heuristic algorithm under a fully connected (FC) structure and partially-connected (PC) architecture, which firstly decouples partially the digital precoder and the analog precoder and then optimizes alternately. To further improve the performance, we extend our partial decoupling method to dynamic subarrays in which each RF chain is connected to an antenna that does not duplicate. The numerical results demonstrate that our proposed THz hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.

关键词： Precoding Radio frequency Terahertz communications Broadcasting Optimization OFDM Communication systems Massive MIMO Antennas Heuristic algorithms Hybrid precoding low-resolution THz broadcasting communication frequency selective channel

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LoRadar: An Efficient LoRa Channel Occupancy Acquirer Based on Cross-Channel Scanning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1699-1714页

作者： Zheng, Xiaolong Yu, Fu Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of many aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of possible channels. In this paper, we propose ${\sf LoRadar}$LoRadar, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on this finding, we design a cross-channel scanning mechanism that infers the channel occupancy states of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features and design a pattern correction method to cope with distribution distortions. We also design a CAD scheduler to deal with the low duty-cycle LoRa operations. We implement ${\sf LoRadar}$LoRadar on commercial LoRa platforms and evaluate its performance in the indoor testbed and two outdoor deployed networks. The experimental results show that ${\sf LoRadar}$LoRadar can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 90%, compared to the traversal-based methods.

关键词： Channel activity detection cross-channel scanning LPWAN Channel activity detection cross-channel scanning LPWAN

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