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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Li, Yi Zhang, Jianhua Tang, Pan Tian, Lei Zhao, Xinyu Xu, Huixin Gong, Huiwen Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China

ISBN: (纸本)9781665459754

Reconfigurable intelligent surface (RIS) has been envisioned as a promising technology for the sixth generation (6G) wireless mobile communication systems because of its ability to change the channel actively and low power consumption. The study of path loss modeling is an important part of RIS-assisted channel research. This paper analyzes the path loss of the RIS-assisted wideband channel based on the field channel measurements at 26 GHz in an indoor corridor scenario. First, we introduce three path loss models considering RIS-assisted channel features and compare their performance via the root mean square error (RMSE) and the path loss exponent. This study verifies that a variant of the multiplicative-distance model is more suitable for RIS-assisted channel path loss modeling. Secondly, by using this model, we carry out path loss modeling in the corridor scenario. The corridor scenario is a typical waveguide-like structure, hence we consider the waveguide effect in the path loss modeling and propose a dual slope path loss model suitable for the RIS-assisted channel in a waveguide-like structure scenario. © 2022 IEEE.

关键词： Radio broadcasting

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Deep Residual Learning for Channel Estimation in UAV mmWave Systems: A Model-Driven Approach

Deep Residual Learning for Channel Estimation in UAV mmWave ...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Xie, Daixin Liu, Chenxi Wang, Wei Hu, Xiaoling Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350378412

Accurate channel estimation is pivotal in satisfying the requirement of ultra-high data rates in future wireless networks. In this paper, we propose a novel model-driven channel estimation algorithm for an unmanned aerial vehicle (UAV) millimeter wave (mmWave) communication system, in which a UAV equipped with a large-scale antenna array performs the channel estimation based on the pilot signals from another UAV equipped with a single antenna. Considering the sparseness of the UAV mmWave channels, in the proposed algorithm, we exploit the use of deep residual learning to judiciously learn the key parameters of the alternating direction method of multipliers (ADMM) framework, reformulating the classical model-based channel estimation algorithm into deep learning model-based model-driven channel estimator. Compared to various benchmark schemes, we validate the efficiency of our suggested model-based channel estimation algorithm, and show how it can achieve good channel estimation accuracy even when the link quality is moderate and the pilot resources are limited.

关键词： Channel estimation deep residual learning model-driven UAV mmWave systems

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MINING: Multi-Granularity Network Alignment Based on Contrastive Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第12期35卷 12785-12798页

作者： Zhang, Zhongbao Gao, Shuai Su, Sen Sun, Li Chen, Ruiyang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China North China Elect Power Univ Beijing 102206 Peoples R China

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. However, the continuous exploding of network data brings two challenges in solving the network alignment problem, i.e., large network scale and scarce labeled data. To bridge this gap, in this paper we propose a novel approach termed as Multi-granularIty Network alIgnment based on coNtrastive learninG (MINING). Specifically, in MINING, we first design multi-granularity alignment framework to solve the issue of large network scale. Then, we design intra-and inter-network contrastive learning to solve the issue of scarce labeled data. Moreover, we provide theoretical proofs to demonstrate the effectiveness of MINING. Finally, we conduct extensive experiments on the benchmark datasets of Facebook-Twitter, AMiner-LinkedIn and DBpediaZH-DBpediaEN, and results show that MINING can averagely achieve 15.93% higher Hits@ k and 14.82% higher MRR@ k compared with the state-of-the-art methods.

关键词： Contrastive learning deep learning machine learning network alignment

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Empirical Path Loss Channel Modeling at 28 GHz for Integrated Sensing and Communication System 15

Empirical Path Loss Channel Modeling at 28 GHz for Integrate...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Chai, Zeyong Zhang, Yuxiang Liu, Guangyi Liu, Yameng Wang, Jialin Jiang, Tao Zhang, Jianhua Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China China Mobile Research Institution Beijing China

ISBN: (纸本)9798350324662

Integrated sensing and communication (ISAC) is considered as a potential technology of sixth-generation (6G). The ISAC system allows for communication and sensing through an integrated waveform, where the receiving strength of the signal affects system performance, including communication quality and sensing accuracy. The path loss characteristics directly affect the signal receiving strength. Most of the existing literature on ISAC path loss is based on the free space radar path loss, however, it does not contain the affect of near and far fields and scatterers in the environment. To address this issue, we conduct a channel measurement campaign at 28 GHz to analyze the path loss. Firstly, we find that path loss exhibits differences in the near and far fields: due to the near-field effect of sensing target, path loss exponent (PLE) fluctuates in the near-field while tends to stabilize in the far-field. Then based on the close-in (CI) reference distance model and the floating-intercept (FI) model, we obtain two empirical path loss models by separating the target influence for ISAC scenarios. Finally, we present detailed steps for path loss characteristic modeling, which can be used for ISAC channel simulation in future research. © 2023 IEEE.

关键词： Channel Measurement Channel Model ISAC Near and far fields Path Loss

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Traffic Scheduling Simulation System for Time-Sensitive networking 14th

Traffic Scheduling Simulation System for Time-Sensitive Netw...

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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Zhuang, Zhihan Wang, Ying Wei, Junjie Cai, Junru Ling, Xueyun State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819642281

Time-Sensitive networking (TSN) is a set of data link layer protocol specifications developed by the IEEE 802.1 Working Group to provide low latency and high reliability network communication for real-time applications. In order to facilitate research and testing validation of Time-Sensitive networking, we have designed and developed a TSN simulation system based on NS3. This system simulates gate list configuration and traffic scheduling in time-sensitive network. In addition, the system supports dynamic changes to the topology and flow configuration while the network is running. It includes the following main functionalities: 1. Simplified gate list configuration. 2. Support for dynamic networks. 3. Support for embedding various traffic scheduling algorithms. After experimental verification, our system can support the verification and testing of TSN scheduling algorithm, and effectively improve the development efficiency of researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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How to Extend 3-D GBSM to Integrated Sensing and Communication Channel With Sharing Feature?

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2045-2049页

作者： Liu, Yameng Zhang, Jianhua Zhang, Yuxiang Gong, Huiwen Jiang, Tao Liu, Guangyi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Future Mobile Commun Res Ctr Beijing 100053 Peoples R China

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), standardized for 5G systems, addresses solely communication channel and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation parameters, which have been experimentally verified in our previous work. The proposed approach can be summarized as follows: Firstly, we propose an ISAC channel model that combines shared and non-shared clusters, where the former co-exist or disappear for communication and sensing. Secondly, to characterize the target, the sensing channel is modeled as a cascade of Tx-target link, target-Rx link, and target scattering response. A novel parameter S is introduced for the extraction of shared targets/scatterers and clusters. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of the sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be well-compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.

关键词： Sensors Antennas Three-dimensional displays Channel models Solid modeling Stochastic processes Delays ISAC channel modeling GBSM sharing feature shared cluster

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Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment 40

Towards Semantic Consistency: Dirichlet Energy Driven Robust...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Yuanyi Sun, Haifeng Wang, Jiabo Wang, Jingyu Tang, Wei Qi, Qi Sun, Shaoling Liao, Jianxin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Bejing China Software Technology Co. Ltd. Jiangsu China

ISBN: (纸本)9798350317152

Multi-Modal Entity Alignment (MMEA) is a pivotal task in Multi-Modal Knowledge Graphs (MMKGs), seeking to identify identical entities by leveraging associated modal attributes. However, real-world MMKGs confront the challenges of semantic inconsistency arising from diverse and incomplete data sources. This inconsistency is predominantly caused by the absence of specific modal attributes, manifesting in two distinct forms: disparities in attribute counts or the absence of certain modalities. Current methods address these issues through attribute interpolation, but their reliance on predefined distributions introduces modality noise, compromising original semantic information. Furthermore, the absence of a generalizable theoretical principle hampers progress towards achieving semantic consistency. In this work, we propose a generalizable theoretical principle by examining semantic consistency from the perspective of Dirichlet energy. Our research reveals that, in the presence of semantic inconsistency, models tend to overfit to modality noise, leading to over-smoothing and performance oscillations or declines, particularly in scenarios with a high rate of missing modality. To overcome these challenges, we propose DESAlign, a robust method addressing the over-smoothing caused by semantic inconsistency and interpolating missing semantics using existing modalities. Specifically, we devise a training strategy for multi-modal knowledge graph learning based on our proposed principle. Then, we introduce a propagation strategy that utilizes existing features to provide interpolation solutions for missing semantic features. DESAlign outperforms existing approaches across 60 benchmark splits, encompassing both monolingual and bilingual scenarios, achieving state-of-the-art performance. Experiments on splits with high missing modal attributes demonstrate its effectiveness, providing a robust MMEA solution to semantic inconsistency in real-world MMKGs. © 2024 IEEE.

关键词： Knowledge graph

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Secure Transmission for SLNR-based Joint Satellite-Cellular Communication System 9

Secure Transmission for SLNR-based Joint Satellite-Cellular ...

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Zhang, Yan Li, Lihua State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Haidian District Beijing China

ISBN: (纸本)9798400708909

In this paper, we present a joint satellite-cellular transmission model, incorporating signal transmission through diversity and multiplexing techniques. The objective is to maximize the signal-to-leakage-plus-noise ratio (SLNR) for system users, considering potential eavesdropping on legitimate users. To tackle the nonconvex fractional maximin optimization problem, we introduce auxiliary variables, decomposing it into primary and secondary sub-problems, along with dedicated algorithms for resolution. Optimization is subsequently performed based on the secrecy rates of system users. Simulation results indicate superior performance of the proposed joint communication system compared to Satellite-Terrestrial Integrated Networks (STIN). Furthermore, the separate optimization algorithms for diversity and multiplexing scenarios outperform the Zero Forcing (ZF) algorithm. © 2023 Copyright held by the owner/author(s)

关键词： Satellites

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Reasoning Guided by a Manual: Context-Aware Image Captioning with Novel Objects 26

Reasoning Guided by a Manual: Context-Aware Image Captioning...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Hua, Peiyao Sun, Haifeng Hao, Jiachang Liu, Cong Wang, Jingyu Qi, Qi Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China China Mobile China

ISBN: (纸本)9781643684369

Novel object captioning task aims at describing objects that are absent from training data. Due to the scarcity of novel objects, it's challenging to find a way to utilize external data to improve model's reasoning ability. While previously designed methods all follow a deep learning approach, we boost novel object captioning by incorporating reasoning with traditional deep learning framework. We design a manual from dictionaries that provides our model with sufficient and accurate external information on novel objects. We propose Manual-guided Context-aware Novel Object Captioning model (MC-NOC) that utilizes image and caption context to generate novel object captions. It contains a Manual-Guided Novel Object Reasoning module to reason about novel objects based on other objects of the given image and a Caption Reconstruction module to incorporate novel objects into generated captions according to caption context. We validate MC-NOC with state-of-the-art performance on the challenging Held-out COCO and Nocaps dataset, leading their leaderboard. In particular, we improved the CIDER metric by 6.4 points on the held-out coco dataset. Comprehensive experiments demonstrate our model's reasoning capability and the quality of generated captions. © 2023 The Authors.

关键词： Deep learning

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Cycle-Based Time-Sensitive and Deterministic Networks: Architecture, Challenges, and Open Issues

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IEEE COMMUNICATIONS MAGAZINE 2022年第6期60卷 81-87页

作者： Huang, Yudong Wang, Shuo Huang, Tao Liu, Yunjie BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

The emerging time-critical applications, such as industrial automation, smart grids, and telesurgery, pose strong demands for enabling end-to-end time-sensitive and deterministic networks. However, most of the existing research only considers the time-sensitive network in the layer 2 local area, or separately designs the deterministic network in the layer 3 wide area. The feasibility of end-to-end scheduling has been largely ignored. Moreover, the problem of multi-layer deterministic architecture design and cross-domain resource co-scheduling have not been fully considered. Therefore, this article proposes a novel architecture named C-TSDN, in which the cyclic queuing and forwarding mechanism and the cycle-specified queuing and forwarding mechanism are jointly designed to solve the bandwidth, cycle, and queue mismatch problems in end-to-end scheduling. The key functional modules of the proposed C-TSDN are detailed, including SRv6, network slicing, cycle alignment, and global computing. We elaborately exhibit the promising technical challenges, such as traffic analysis with AI. Finally, some potential open issues as future research directions are pointed out.

关键词： Job shop scheduling Processor scheduling Network slicing Computational modeling Computer architecture Bandwidth Smart grids

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