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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Liu, Tao Chen, Yuxing Hong, Weijun Beijing University Of Posts And Telecommunications Beijing Key Laboratory Of Network System Architecture And Convergence Beijing Laboratory Of Advanced Information Network Beijing100876 China

ISBN: (纸本)9781665434379

In this paper, a low-cost, compact antenna system for wideband spectrum sensing and 360° beam scanning is designed. The system consists of an elliptical monopole Ultra-Wideband (UWB) antenna, and a cylindric antenna radome is composed of a PIN diode array and copper patch array. The passband/stopband reconfigurable Frequency selective surface(FSS) radome model is established by switching the state of the PIN diode array, and a 360° beam scanning switch is realized. The simulation results show that the operation band is 3.5GHz-11.8GHz, and the relative bandwidth is up to 108% in the wideband working mode. In the beam scanning mode, the -3dB main lobe width is about 35°-48°, achieving the 360° beam scanning with 10° as the step. © 2021 IEEE.

关键词： Frequency selective surfaces

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MK-SGN: A Spiking Graph Convolutional network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition

arXiv

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arXiv 2024年

作者： Zheng, Naichuan Xia, Hailun Liang, Zeyu Du, Yuchen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In recent years, multimodal Graph Convolutional networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. The reliance on high-energy-consuming continuous floating-point operations inherent in GCN-based methods poses significant challenges for deployment in energy-constrained, battery-powered edge devices. To address these limitations, MK-SGN, a Spiking Graph Convolutional network with Multimodal Fusion and Knowledge Distillation, is proposed to leverage the energy efficiency of Spiking Neural networks (SNNs) for skeleton-based action recognition for the first time. By integrating the energy-saving properties of SNNs with the graph representation capabilities of GCNs, MK-SGN achieves significant reductions in energy consumption while maintaining competitive recognition accuracy. Firstly, we formulate a Spiking Multimodal Fusion (SMF) module to effectively fuse multimodal skeleton data represented as spike-form features. Secondly, we propose the Self-Attention Spiking Graph Convolution (SA-SGC) module and the Spiking Temporal Convolution (STC) module, to capture spatial relationships and temporal dynamics of spike-form features. Finally, we propose an integrated knowledge distillation strategy to transfer information from the multimodal GCN to the SGN, incorporating both intermediate-layer distillation and soft-label distillation to enhance the performance of the SGN. MK-SGN exhibits substantial advantages, surpassing state-of-the-art GCN frameworks in energy efficiency and outperforming state-of-the-art SNN frameworks in recognition accuracy. The proposed method achieves a remarkable reduction in energy consumption, exceeding 98% compared to conventional GCN-based approaches. This research establishes a robust baseline for developing high-performance, energy-efficient SNN-based models for skeleton-based action recognition Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Joint Optimization of Base Station Sleeping, Functional Split, and Routing Selection in Virtualized Radio Access networks

Joint Optimization of Base Station Sleeping, Functional Spli...

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IEEE Conference on Wireless Communications and networking

作者： Yunqi Xu Hang Li Zhenghe Zhu Yawen Chen Luhan Wang Zhaoming Lu Xiangming Wen The Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

This paper investigates the Joint Optimization of Base station sleeping, Functional split, and Routing selection (JOBFR) in virtualized radio access network (vRAN) architectures to minimize the operator’s total cost, including operating cost and migration cost while satisfying user requirements. Specifically, we first use mathematical methods to describe the relationships between the base station (BS) sleeping, functional split (FS) and routing selection, and then model the goal as a joint optimization problem. Next, we propose a heuristic algorithm called Flexible Sleeping, Functional split, and Routing selection (FSFR) to decide BS sleeping, and FS option, and routing selection, simultaneously. Finally, we perform extensive simulations to evaluate our algorithm. The results demonstrate that our algorithm can significantly save the total cost of operators compared to the baseline approach and can also achieve near-optimal performance.

关键词： Base stations Costs Heuristic algorithms Routing Mathematical models Optimization Radio access networks

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Mobility-Aware AR Computation Tasks Offloading Based on Offline RL over Terahertz system

Mobility-Aware AR Computation Tasks Offloading Based on Offl...

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IEEE Globecom Workshops

作者： Shuyue Zhao Wenpeng Jing Xiangming Wer Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

Augmented reality (AR) technology is increasingly applied in the Internet of vehicles (IoV). As it poses significant challenges to the communication and computation capabilities of AR terminal devices, this paper investigates combining terahertz (THz) and mobile edge computing (MEC) integrated with reinforcement learning (RL) to optimize the performance of AR services for the IoV. Though the RL-based MEC offloading has been validated to have excellent performance, it has an intrinsic disadvantage where its training phase needs inappropriate offloading strategies to be tested frequently and poses risks to safe driving. Moreover, although THz communication can provide high transmission data rates, its characteristics that different transmission distances lead to different available bandwidth, pose a unique challenge for the communication resources allocation. Motivated by these, we propose a novel static data-driven AR tasks offloading framework to solve resources allocation problem for the IoV with THz communication. To deal with the challenge that different users have different available THz bandwidth, we propose a distance-aware fair THz bandwidth allocation algorithm. Moreover, the offline RL, instead of online RL, is utilized to train the AR task offloading decision agent, where interaction with the real world is not required, completely safeguarding the system's safety. To address the typical issue of out-of-distribution (ODD) actions encountered in offline RL, we adopt conservative Q-learning (CQL) to train and obtain the optimal AR computation task offloading strategy to enhance users' experience. Simulation results show that the proposed solution outperforms state-of-the-art algorithms in terms of algorithm stability and user reward.

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Super-Resolution Enhanced Video Streaming Based on Cloud-Edge Cooperation for Satellite Backhauls

Super-Resolution Enhanced Video Streaming Based on Cloud-Edg...

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IEEE Globecom Workshops

作者： Changhao Liu Wenpeng Jing Ziyuan 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

Although satellite backhauls facilitate expanding network coverage and providing capacity in unconnected areas, delivering high-quality videos via satellite backhauls is challenging due to high delay, bandwidth limitations and high expenses. Super-resolution (SR) technique has been proposed to integrate into streaming systems to improve Quality of Experience (QoE) in bandwidth-limited environments. However, existing SR-enhanced streaming systems face two key challenges: 1) compatibility problem that legacy video servers and user equipment (UEs) must be modified to integrate SR-related modules, and 2) quality consistency problem that enhanced video quality fluctuates across chunks. Motivated by this, we present SatOL, a novel SR-enhanced video streaming system for satellite-backhauled networks. Specifically, SatOL is designed on a cloud-edge cooperation framework, where a cloud server and an edge server are responsible for SR model training and SR-based video enhancement, respectively. This framework operates as an intermediate hop within end-to-end streaming sessions to perform SR-related processing, avoiding modifications to legacy video servers and UEs. Instead of training video-tailored SR models offline, the cloud server continually trains chunk-tailored SR models during video streaming, which are overfitted to corresponding chunks and provide consistent quality enhancement. To avoid rebuffering caused by time-consuming model training, an online fine-tuning method is proposed to significantly expedite SR model training. To further improve online training efficiency, we propose a decision mechanism that determines whether and how to train chunk-tailored SR models based on the chunks' 1-frame count. Evaluations show that SatOL outperforms baselines, improving QoE and enhanced video quality by 6-285% and 17-25%, respectively, while reducing bandwidth usage by 60-64%.

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Deep Learning Enabled Video Semantic Transmission Against Multi-Dimensional Noise

Deep Learning Enabled Video Semantic Transmission Against Mu...

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IEEE Globecom Workshops

作者： Haiwen Niu Luhan Wang Zhaoming Lu Keliang Du 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

As a novel communication paradigm to achieve efficient end-to-end communication over wireless channels, deep learning (DL) enabled semantic communication system has attracted increasing attention. However, semantic wireless communication systems are affected by multi-dimensional noise, including physical channel noise and semantic noise caused by unstable sources in the real environment. Unfortunately, the joint consideration of physical channel noise and visual semantic noise in video semantic transmission has not been studied yet in the current literature. In this paper, we propose a novel DL-enabled video semantic communication system called DVSC, which employs channel and spatial attention mechanisms to achieve self-adaptation for different channel conditions, mitigating the impacts of varying degrees of physical channel noise on system performance. Additionally, we introduce a feature refinement module based on an encoder-decoder architecture, and a semantic correction module based on a lightweight U-Net structure, which can address semantic ambiguity caused by visual semantic noise at the transceiver. We also design a loss function that considers both pixel and perceptual quality metrics to jointly optimize the DVSC in the pixel and semantic domains. Our extensive experimental results demonstrate that the DVSC can adapt to various channel conditions and outperform benchmark comparison schemes while spending less time on encoding and decoding. To reproduce our results, we make the source code publicly available at https://***/Coding-Cow/***.

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Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving

arXiv

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arXiv 2024年

作者： Lv, Jie Tong, Haonan Pan, Qiang Zhang, Zhilong He, Xinxin Luo, Tao Yin, Changchuan School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, detecting the key objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that of other objects to guarantee driving safety. Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy. First, to accurately extract image semantics, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy. Next, we propose a multi-scale semantic extraction scheme via assigning the number of Swin Transformer blocks for diverse resolution features, thus highlighting the important objects’ accuracy. Furthermore, the importance-aware loss is invoked to emphasize the important objects, and an online hard sample mining (OHEM) strategy is proposed to handle small sample issues in the dataset. Experimental results demonstrate that the proposed VIS-SemCom can achieve a coding gain of nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the transmitted data amount by up to 70% with a 60% mIoU, and improve the segmentation intersection over union (IoU) of important objects by 4%, compared to traditional transmission scheme. © 2024, CC BY-NC-SA.

关键词： Autonomous vehicles

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CPP-Based Cooperative Defense Against DoS Attacks in Future Non-terrestrial networks 15th

CPP-Based Cooperative Defense Against DoS Attacks in Future ...

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15th EAI International Conference on Communications and networking, ChinaCom 2020

作者： Cong, Zhaori Zhang, Zhilong Liu, Danpu Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783030677190

In future non-terrestrial networks, satellites with sufficient computing resources are expected to serve as base stations or access points, which changes the structure of traditional satellite networks into a more flexible environment. For such satellite nodes, denial of service (DoS) attacks may become a potential secure threat that should be prevented. Existing researches mainly focus on the defense against DoS attacks on ground nodes and have no consideration of the attacks on future satellites. Moreover, the problem of reducing the access delay when a satellite is under DoS attack has not been addressed. In this paper, we study the DoS attack defense strategy in non-terrestrial networks. By adopting client puzzle protocol (CPP) and load balancing, we propose a cooperative defense strategy where multiple auxiliary nodes are used to help the attacked node to process the intensive attack requests. An access delay minimization problem to optimize the selection of auxiliary nodes, puzzle difficulty as well as traffic offload ratios is then formulated based on queuing theory and solved. Simulation results show that the proposed scheme not only improve the network’s anti-attack capability, but also achieves desirable performance in average access delay. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Denial-of-service attack

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A Satellite-Terrestrial Integrated network architecture Based on Stateless Core network

A Satellite-Terrestrial Integrated Network Architecture Base...

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International Conference on Computer and Communications (ICCC)

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

Comprehensive coverage is a natural advantage of satellites, and loading the functions of base station on satellites can expand the service range of Satellite-Terrestrial Integrated network(STIN), providing users with extensive and efficient access services. However, the management and control capabilities of satellite networks are inadequate, and this aspect has not been well investigated. The signalling from the control plane needs to be routed over the backhaul links to the Terrestrial Core network(TCN) for processing which increases the service latency. In this paper, we propose a novel integrated core network architecture where satellites simultaneously have access and control capabilities by provisioning next-generation NodeB(gNB) and some core network functions. Considering the constrained satellite resources, we design a lightweight Stateless Satellite Core network (SSCN) to enhance the flexibility and stability of the satellite network control. Meanwhile, based on SSCN, we redesign the registration, session establishment, and handover procedures to support mobility management and further reduce service latency. Simulation results show that the proposed integrated core network architecture improves the stability of mobility management and optimizes the network latency compared to the Non-Terrestrial network(NTN) architecture in that satellites act as radio access networks only.

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Deep Joint Source-Channel Coding Based on Semantics of Pixels for Wireless Image Transmission

Deep Joint Source-Channel Coding Based on Semantics of Pixel...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Qizheng Sun Caili Guo Yang Yang Rui Tang Chuanhong Liu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

Current image coding methods for semantic communication typically concentrate on intelligent tasks or image reconstruction separately, and seldom consider both aspects simultaneously. To balance these two aspects during wireless image transmission, we propose a joint source-channel coding method based on the semantics of pixels (SP), which can retain both pixel information for reconstruction and semantic information for intelligent tasks. Specifically, we first design a gradient-based mechanism to quantify the semantic importance of downstream intelligent tasks on pixels. Then, we design the SP-based loss function to train the deep joint source-channel coding network. Experiment results demonstrate that the proposed method maintains reconstruction performance and improves the task performance by 1.61% and 4.06%, respectively, compared to the state-of-the-art deep joint source-channel coding method and traditional separate source-channel coding method at the same transmission rate and signal-to-noise ratio.

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