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International Conference on Computer and Communications (ICCC)

作者： Mengxin Zhang Xiaofeng Qiu Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Deep neural networks (DNNs) are playing an increasingly significant role in the modern world. However, they are weak to adversarial samples that are generated by adding specially crafted perturbations. Most defenses against adversarial samples focused on refining the DNN models, which often sacrifice the performance and computational cost of models on benign samples. In this paper, we propose a manifold distance detection method to distinguish legitimate samples and adversarial samples by measuring the different distances on the manifold. The manifold distance detection method neither modifies the protected models nor requires knowledge of the process for generating adversarial samples. Inspired by the effectiveness of the manifold distance detection, we demonstrated a well-designed orchestrated defense strategy, named Manifold Distance Judge (MDJ), which selects the best image processing method that will effectively expand the manifold distance between legitimate and adversarial samples, and thus, enhances the performance of the following manifold distance detection method. Tests on the ImageNet dataset, the MDJ is effective against the most adversarial samples under white-box, gray-box, and black-box attack scenarios. We show empirically that the orchestration strategy MDJ is significantly better than Feature Squeezing on the recall rate. Meanwhile, MDJ achieves high detection rates against CW attack and DI-FGSM attack.

关键词： Manifolds Deep learning Computational modeling Perturbation methods Image processing Refining Neural networks

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Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities

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Journal of Communications and Information networks 2021年第2期6卷 101-109页

作者： Fangfang Liu Jiaxi Pan Xiangwei Zhou Geoffrey Ye Li Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China Louisiana State University Baton Rouge LA 70803USA Imperial College London London SW72AZU.K.

Atmospheric ducting has a significant impact on electromagnetic wave *** signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the *** ducting brings both challenges and opportunities to wireless *** one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel *** the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight *** this article,the formation of the atmospheric duct and its effects on radio wave propagation are first *** solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are ***,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.

关键词： atmospheric duct ducting channel modeling beyond line-of-sight remote interference management

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An Energy-Efficient Beam Hopping Strategy in Interference Coexistence Satellite systems

An Energy-Efficient Beam Hopping Strategy in Interference Co...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Hao Sun Wenpeng Jing Zhaoming Lu Xiangming Wen Wei Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Satellite Frequency and Orbit Resource Research Center The State Radio Monitoring Center Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

While beam hopping (BH) has revolutionized satel-lite communication by addressing non-uniform traffic demands and minimizing beam interference, existing research on multi-satellite BH scenarios often overlooks energy efficiency (EE) optimization. This can lead to increased operational costs and reduced satellite lifespans due to higher onboard energy consumption. In this paper, we propose a novel energy-efficient beam hopping strategy in the coexistence scenario of geosynchronous orbit (GSO) and non-geosynchronous orbit (NGSO) satellite systems. Specifically, the proposed strategy can both balance traffic load among NGSO satellites and avoid interference to GSO ground stations. A joint multi-satellite BH optimization problem is formulated to optimize a trade-off function between quality of service (QoS) and EE for the NGSO system. Then, the original problem is divided into the satellite-cell assignment, BH pattern design, and beam power allocation sub-problems and solved sequentially. The numerical results demonstrate the superiority of the proposed strategy in enhancing the QoS and EE of the NGSO system while effectively mitigating interference with the GSO system.

关键词： Energy consumption Satellites Interference Quality of service Telecommunication traffic Energy efficiency Orbits Resource management Optimization Satellite communications

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Fast Beam Switching Based on Machine Learning for MmWave Massive MIMO systems 3rd

Fast Beam Switching Based on Machine Learning for MmWave Mas...

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3rd EAI International Conference on Artificial Intelligence for Communications and networks, AICON 2021

作者： Chen, Kean Liu, Danpu He, Xingwen Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783030901950

Millimeter wave (mmWave) and massive multiple-input-multiple-output (MIMO) systems are two key technologies for 5G. Beamforming based on massive MIMO can produce high directional beams with array gain, and thus effectively compensate for the high path loss of mmWave. As the number of antennas increases, the beams become increasingly narrow, resulting in large overhead and high latency in the initial access and handover of the beams. For high-speed mobile scenarios, beam switching becomes more challenging since the traversal search among a large number of beams cannot be completed in a short period of time. To address this problem, this paper proposes a method based on machine learning to predict the optimal Base Station (BS) and the optimal beam pair at the successive instant for the User Equipment (UE) in motion. More specifically, a Random Forest (RF) classification model is trained to learn the channel’s features in a multi-cell scenario, and complete the nonlinear modeling of the propagation environment. Furthermore, this model is used to predict the future optimal BS and the optimal beam pair for a moving UE based on the present UE’s location, BS beam index and RSRP value. The simulation results show that the prediction accuracy is greater than 90% in most situations, thus the latency and the consumption of signaling resources for beam switch is reduced significantly while the loss in spectral efficiency is little. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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SINR-Adaptive and CBR-Controllable Semantic Cellular Communication Considering Imperfect CSI and Inter-Cell Co-channel Interference

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IEEE Transactions on Cognitive Communications and networking 2024年

作者： Niu, Haiwen Wang, Luhan Lu, Zhaoming Wu, Celimuge Wen, Xiangming Beijing University Of Posts And Telecommunications Beijing Key Laboratory Of Network System Architecture And Convergence Beijing Laboratory Of Advanced Information Networks Beijing100876 China The University Of Electro-Communications Meta-Networking Research Center 1-5-1 Chofugaoka Chofu-shi Tokyo182-8585 Japan

Deep learning (DL)-enabled semantic communications offer promising prospects for higher data transmission efficiency. However, most existing studies focus on point-to-point transmissions with perfect channel state information (CSI) and fixed channel bandwidth ratio (CBR). This paper investigates multi-point transmission in cellular networks with spectrum resource sharing and proposes a signal-to-interference plus noise ratio (SINR) adaptive and CBR-controllable semantic cellular communication system (SACC). In this system, we design a semantic encoder by a Transformer-convolutional neural network (CNN) mixture block to capture non-local and local latent image features simultaneously and a SINR-adaptive module based on the channel-spatial soft attention mechanism to scale image features according to SINR conditions. A model adaption strategy is proposed to handle varying inter-cell co-channel interference proportions under the same SINR. Additionally, a semantic-channel encoder-decoder forwarding paradigm, termed semantic translation, enables dynamic networking among intra-cell users. Extensive simulation results show that the proposed SACC achieves graceful degradation and better image reconstruction performance versus the current engineered "JPEG+LDPC+QAM"and DeepJSCC benchmarks across different wireless environments and CBRs. Furthermore, experimental results demonstrate substantial improvements in FLOPs, model sizes, and computing latency over the state-of-the-art ADJSCC and WITT with comparable performance. © 2015 IEEE.

关键词： Bandwidth

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Performance Optimization for Task-Oriented Communications

Performance Optimization for Task-Oriented Communications

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IEEE International Conference on Communications (ICC)

作者： Chuanhong Liu Caili Guo Yang Yang Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. This paper proposes a deep learning-based task-oriented communication architecture for end-to-end (E2E) semantics transmission, where extracted semantics is compressed by the proposed adaptable semantic compression (ASC) method. However, accommodating multiple users in a delay-intolerant system poses a challenge. Higher compression ratios conserve channel re-sources but cause semantic distortion, while lower ratios demand more resources and may lead to transmission failure due to delay constraints. To address this, we optimize both compression ratio and resource allocation to maximize task success probability. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm that separates the problem into two subproblems and solving them iteratively. Simulation results show that the proposed algorithm can obtain at least 14.3% success gains over baseline algorithms.

关键词： Power measurement Communication systems Simulation Semantics Collaboration Distortion Delays

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WiLink: Link Selection-Based 3D Human Pose Estimation Using Commodity Wi-Fi

WiLink: Link Selection-Based 3D Human Pose Estimation Using ...

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IEEE Conference on Wireless Communications and networking

作者： Liming Wang Lingchao Guo Zhaoming Lu Xiangming Wen Shuang Zhou Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China China Academy of Information and Communications Technology Beijing China

Previous works have verified the feasibility of WiFi-based human pose estimation (HPE). However, their crucial limitations lie in requiring favorable placement of Wi-Fi devices and only sensing human poses in a small area. To address these issues, we propose WiLink, a Wi-Fi-based 3D HPE system that selectively uses several existing Wi-Fi links to achieve accurate HPE everywhere indoors. We find that the Channel State Information (CSI) fluctuations caused by human pose changes over different Wi-Fi links are various. According to the effectiveness of Wi-Fi links for human pose sensing, we classify the links as Noise-Dominated Links, Most-Effective Links and Redundant Links. Then we propose a Dynamic Link Selection (DLS) mechanism to adaptively select Most-Effective Links for HPE. This process maximizes the importance and minimizes the redundancy of the selected links. Finally, we feed the CSI samples corresponding to Most-Effective Links into a neural network to estimate human poses. Intensive experiments are conducted, and the results show that WiLink achieves a good performance in the scenario with multiple available Wi-Fi links.

关键词： Training Three-dimensional displays Fluctuations Pose estimation Redundancy Neural networks Sensors

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Positioning and Dynamic Tracking under 3GPP Cellular network on GNURadio Testbed

Positioning and Dynamic Tracking under 3GPP Cellular Network...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Shunyu Chang Zhaoming Lu Xinghe Chu Di Zhang Luhan Wang Xiangming Wen Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Zhengzhou University Zhengzhou China

This paper addresses high precision 5G positioning and dynamic tracking, which has risen increasingly attention from industry and academia recently. The recent works show that the position of user equipment (UE) could be estimated based on reference signals (e.g. PRS) in 5G on real testbed. However, these works failed to exploit the positional information behind the consecutive nature of users' moving, which could enhance the positioning accuracy by exploiting dynamic tracking architectures. In this paper, we extract the angle of arrival (AoA) and time of arrival (ToA) under 3GPP physical layer protocol, and then introduce a dynamic tracking method to improve the accuracy of AoA and ToA estimation. The performance of the proposed method was evaluated on GNURadio. The results show that the AoA and ToA could be precisely extracted in the real propagation channel on the premise of guaranteeing the communication performance. And in the case of the same bandwidth and number of antennas, compared with traditional base station (BS) tracking method, our dynamic tracking could reduce tracking error by 35.11% during the 115s tracking process.

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Sequence Elimination Algorithm for Multi-Server Multi-User Mobile Edge Computing Offloading 7

Sequence Elimination Algorithm for Multi-Server Multi-User M...

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7th IEEE International Conference on network Intelligence and Digital Content, IC-NIDC 2021

作者： Chu, Yi Li, Ruixiang Wang, Fang Ren, Junlong Qiao, Yuanyuan Yang, Jie Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Aisino Corporation The Technology Research Institute Beijing100195 China

ISBN: (纸本)9781665405829

Edge computing is a new paradigm of distributed computing, in which computing offloading attracts wide attention from scholars. Existing work mainly focuses on single-server and single-user, and generally only optimizes energy consumption or execution time respectively. However, optimizing one may lead to the decline in another. In order to remedy this defect, we propose sequence elimination algorithm based on genetic algorithm. The algorithm can optimize energy consumption or execution time respectively in the scenario of multi-user and multi-server. The simulation results show that the sequence elimination algorithm can effectively reduce the energy consumption and execution time in the edge computing offloading problem. © 2021 IEEE.

关键词： Genetic algorithms

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Image Segmentation Semantic Communication over Internet of Vehicles

arXiv

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arXiv 2022年

作者： Pan, Qiang Tong, Haonan Lv, Jie Luo, Tao Zhang, Zhilong Yin, Changchuan Li, Jianfeng Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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