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IEEE Conference on Wireless Communications and networking

作者： Luhan Wang Haiwen Niu Zhaoming Lu Xiangming Wen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, Semantic Communication (SC) has been recognized as a crucial new paradigm in 6G, significantly improving information transmission efficiency. However, the diverse range of service types in 6G networks, such as high-data-volume services like AR/VR/MR and low-data-volume applications requiring high accuracy, such as industrial control and data collection, presents significant challenges to fully replacing the fundamental technologies with SC. Therefore, we design a Hybrid Semantic Communication Ratio Access network (HSC-RAN) protocol stack demo for 6G systems to achieve compatibility and smooth transition between SC and non-SC. Specifically, we take the Physical Downlink Shared Channel (PDSCH) as an example, to efficiently integrate SC with Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, we introduce a novel Downlink Control Information (DCI) format that jointly supports SC and non-SC, enabling real-time video transmission via SC and text transmission through non-SC. Experimental results demonstrate that our approach allows simultaneous transmission of semantic and non-semantic information while maintaining high-quality reconstruction at the receiver.

关键词： 6G mobile communication Wireless communication Protocols OFDM Receivers Streaming media Downlink Semantic communication Real-time systems Next generation networking

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Deep Learning-Based Mobile User Localization with Reconfigurable Intelligent Surface

Deep Learning-Based Mobile User Localization with Reconfigur...

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IEEE Conference on Wireless Communications and networking

作者： Chenpan He Zhenghe Zhu Yawen Chen Wei Zheng Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Reconfigurable intelligent surface (RIS) is a promising technique for positioning systems. A large number of reference signals is needed for traditional base station (BS) with RIS localization system which occupy communication resources. To solve this problem, we directly use the DMRS (Demodulation Reference Signal), which does not consume additional communication resources to estimate the user equipment (UE) position. In this paper, we consider a RIS-assisted downlink localization system that takes account for the channel time variability caused by UE mobility. To estimate the UE position, DPSCN (Depthwise Separable Convolution) network consisting of three stages is adopted to capture the relationship between the received signal and the UE *** on the estimated position, the RIS phase is dynamically adjusted, to achieve accurate and continuous tracking of the UE. Extensive simulation results are also presented to demonstrate that the proposed DPSCN learning algorithm can estimate the UE position accurately and outperforms the traditional algorithms.

关键词： Location awareness Accuracy Convolution Simulation Heuristic algorithms Neural networks Reconfigurable intelligent surfaces Prediction algorithms Downlink Demodulation

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A Neural-Based OTFS Channel Estimator

A Neural-Based OTFS Channel Estimator

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IEEE Conference on Wireless Communications and networking

作者： Zhenghe Zhu Yawen Chen Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput.

关键词： 6G mobile communication Time-frequency analysis Spectral efficiency Simulation Channel estimation Modulation Transceivers Reliability Convolutional neural networks Standards

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Semi-SemNet: A Evolved Servitization network architecture from 5G to Semantic Communication

Semi-SemNet: A Evolved Servitization Network Architecture fr...

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IEEE Conference on Wireless Communications and networking

作者： Haiwen Niu Luhan Wang Zhaoming Lu Xiangming Wen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, a new communication paradigm, semantic communication, has garnered significant attention due to its superior information transmission efficiency compared to conventional communication. However, current research on semantic communication primarily focuses on the design of end-to-end communication systems from the perspective of goal orientation or information reconstruction, without considering integration with the evolving technological landscape of the 5th generation mobile communication (5G) networks. To address this gap, in this paper, we propose an evolved servitization network architecture, called Semi-SemNet, which provides goal-level semantic service capability for the proposed converged goal-oriented semantic communication. To enable dynamic semantic-aware load balancing, the contexts of network functions (NFs) are decoupled from themselves and managed by a common NF, rendering each NF stateless. Additionally, we present an overview of enhanced NFs and system procedures for semantic communications. To meet different target nodes, such as application servers and user equipment (UE), we introduce a semantic translation mechanism to eliminate performance degradation when there are multiple independent wireless links between the source node and the target node. Finally, we use fire video remote monitoring as a case study. Experimental results show that Semi-SemNet achieves an average 26.67% PSNR and 27.21% MS-SSIM performance improvement in information reconstruction, and saves 40.51% bandwidth resources in goal execution. Moreover, with the stateless mechanism and semantic-aware load balancing decisions, it can reduce semantic data transmission delay by an average of 60.59% compared to the benchmark methods. The all code and original data of this paper are publicly available at https://***/hwniu/sbi_semantic_***.

关键词： Wireless communication Translation 5G mobile communication network architecture Semantic communication Load management Rendering (computer graphics) Noise measurement Servers Remote monitoring

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Joint Optimization of VNF Reusing and Routing in Satellite networks

Joint Optimization of VNF Reusing and Routing in Satellite N...

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IEEE Conference on Wireless Communications and networking

作者： Weixin Yan Zhilong Zhang Danpu Liu Tao Luo Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Low-earth orbit (LEO) satellite networks has attracted a lot of attention, since it is able to provide high-quality services worldwide. With the assistance of NFV technology, the flexibility and the quality of service (QoS) of LEO satellite networks can be further improved. However, most existing researches have not considered virtual network functions (VNFs) reusing during the process of service function chain (SFC) placement in NFV-enabled satellite networks. In this paper, we investigate the problem of SFC placement, and propose a joint VNF reusing and routing algorithm. By considering both of the initialization delay and sharing of VNFs, the service deployment delay can be effectively reduced, and the network profit can be largely improved. In our setting, simulation results show that our proposed algorithm outperforms baseline algorithms in profit, with an improvement of at least 23 %.

关键词： Satellites Service function chaining Simulation Low earth orbit satellites Quality of service Routing Orbits Delays Resource management Optimization

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DRLRA: A Deep Reinforcement Learning architecture Based on Resource Allocation for Mining key Players in networks

DRLRA: A Deep Reinforcement Learning Architecture Based on R...

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IEEE Conference on Wireless Communications and networking

作者： Wang Kaili Wu Muqing Zhao Min Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

network key nodes are very important in a network, after identifying them, network optimization can be carried out for these nodes. For example, increasing bandwidth or resource allocation, enhancing fault tolerance, protecting and strengthening key nodes. Or destroy key nodes to quickly break down the network, just like spreading rumors and preventing diseases. Different network optimization measures may vary in different network scenarios, but the core is to enhance or break key nodes, thereby affecting the overall network performance and effectiveness. However, most current network key node identification is based on fixed mathematical models or formulas, limiting the possibility of deep information mining in the network. To address this issue, this paper proposes a deep learning network architecture based on resource allocation to identify key nodes in the network. This allows for more in-depth feature extraction of network semantics and structures while combining the resource allocation situation of each node. In the process of gradually disintegrating the network through deep reinforcement learning, finding the strategy with the fastest decrease in cumulative connectivity rate is considered optimal. Finally, this paper compares the performance of six classical algorithms and the optimal algorithm on six real networks. The effectiveness improvement is 8.11% without sacrificing performance, validating the effectiveness of the method.

关键词： Limiting Semantics Fault tolerant systems network architecture Deep reinforcement learning Feature extraction Mathematical models Graph neural networks Resource management Optimization

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A Novel Finite Mixture Model Based on the Generalized t Distributions with Two-Sided Censored Data

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Annals of Data Science 2025年第1期12卷 341-379页

作者： Guan, Ruijie Rong, Yaohua Cheng, Weihu Xin, Zhenyu Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Faculty of Science Beijing University of Technology Beijing100124 China Master of management EBS Universität für Wirtschaft und Recht Oestrich-Winkel65375 Germany

In light of the rapid technological advancements witnessed in recent decades, numerous disciplines have been inundated with voluminous datasets characterized by multimodality, heavy-tailed distributions, and prevalent missing information. Consequently, the task of effectively modeling such intricate data poses a formidable yet indispensable challenge. This paper endeavors to address this challenge by introducing a novel finite mixture model predicated upon the generalized t distribution, tailored specifically to accommodate two-sided censored observations, thereby establishing a foundational framework for modeling this complex data structure. To facilitate parameter estimation within this model, we devise a variant of the EM-type algorithm, amalgamating the profile likelihood approach with the classical Expectation Conditional Maximization algorithm. Notably, this hybridized methodology affords analytical expressions in the E-step and a tractable M-step, thereby substantially enhancing computational expediency and efficiency. Furthermore, we furnish closed-form expressions delineating the observed information matrix, pivotal for approximating the asymptotic covariance matrix of the MLEs within this mixture model. To empirically evaluate the efficacy of the proposed algorithm, a series of simulation studies are conducted, demonstrating promising performance across various artificial datasets. Additionally, the practical applicability of the proposed methodology is elucidated through its deployment on two real-world datasets, thereby underscoring its feasibility and utility in practical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Expectation maximization algorithm

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The desertification process alters soil microbial metabolic limitations and their effects on soil carbon sequestration in a Tibetan alpine steppe

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Journal of Integrative Agriculture 2025年第3期24卷 845-858页

作者： Jialuo Yu Peili Shi Ning Zong Yongxing Cui Ge Hou Xueying Chen Tiancai Zhou Xiaofang Huang Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China Institute of Biology Free University of BerlinBerlin 14195Germany State Key Laboratory of Tibetan Plateau Earth System Resources and Environment(TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China

Tibetan alpine steppes are large and sensitive terrestrial carbon(C)reservoirs that are experiencing desertification due to global change and overgrazing,which can lead to stronger resource limitations for both above-and belowground *** nutrients,especially nitrogen(N)and phosphorus(P),are the crucial resources for plant growth and microbial ***,whether both plant and soil microbial communities in the degraded alpine steppes are limited by these soil nutrients remains unclear,which limits our understanding of the mechanisms of desertification and subsequent ecosystem ***,we evaluated potential nutrient limitations of the plant and soil microbial communities in the alpine steppe across five stages of desertification using stoichiometry-based *** results showed that soil microbial metabolism was mainly limited by C and P,and the plant N limitation and microbial C limitation were intensified while the microbial P limitation was relieved during ***-soil-microbe interactions had significant impacts on the microbial C and P limitations,explaining 72 and 61% of the variation,***,desertification ultimately affected microbial metabolic limitations by regulating soil pH,soil nutrients,and the plant N ***,the microbial C limitation further reduced microbial C use efficiency(CUE)with desertification,which is detrimental for organic C retention in the degraded ***,this study revealed that microbial metabolic limitations through plant-microbe interactions were the key drivers affecting soil microbial CUE,and it provided insights that can advance our knowledge of the microbial regulation of nutrient cycles and C sequestration.

关键词： desertification gradient ecoenzymatic stoichiometry microbial metabolic limitation carbon use efficiency alpine steppe

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Object-Centric Discriminative Learning for Text-Based Person Retrieval

Object-Centric Discriminative Learning for Text-Based Person...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiwen Li Delong Liu Fei Su Zhicheng Zhao Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-based person retrieval (TBPR) is a vision-language task that aims to find specific pedestrians in a large image gallery using the textual description. However, due to the heterogeneity between modalities and the redundancy in visual representations, it remains a challenging task. Existing methods do not explicitly reduce the influence of the background regions in images, inevitably decreasing representation ability and reducing the image-text matching performance. In this paper, we propose a novel framework for text-based person retrieval, termed Object-Centric Discriminative Learning (OCDL), which incorporates person masks to indicate attentive regions, thereby enhancing the model’s focus on the pedestrians in images while suppressing the background noise. Additionally, a novel crossmodal matching loss, namely Soft Angular Distribution Matching (SADM), is introduced to learn discriminative visual and textual representations. Extensive experiments on three widely-used TBPR datasets demonstrate the effectiveness of our approach. The code is available at https://***/JThuge/OCDL.

关键词： Visualization Pedestrians Codes Redundancy Signal processing Performance gain Benchmark testing Feature extraction Speech processing Cross modal retrieval

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Sparsely Deployed Bluetooth and Magnetic Fusion Positioning for Large Underground Parking Lots

Sparsely Deployed Bluetooth and Magnetic Fusion Positioning ...

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IEEE Conference on Wireless Communications and networking

作者： Ziwen Luo Yihong Zheng Shuya Zhou Shumin Wang Xinghe Chu Luhan Wang Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m 2 ), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices.

关键词： Global navigation satellite system Bluetooth Accuracy Costs Heuristic algorithms Magnetic fields Received signal strength indicator Vehicle dynamics Low latency communication Smart phones

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