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IEEE International Conference on Communications in China (ICCC)

作者： Lianwen Zhang Zhiqun Hu Zhaoming Lu Yujing Zhang Beijing Key Laboratory of Network System Architecture and Convergence China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications China

Unmanned aerial vehicle (UAV) used as the aerial base station, can provide flexible and reliable data transmission service for designated areas, owing to its advantages of mobility, easy deployment and cost-effectiveness. However, the limited endurance of UAV, caused by its small size and payload limitation, significantly affects the quality of UAV base station (BS) network. In this study, we employ multiple intelligent reflecting surfaces (IRSs) to enhance the performance of multi-UAV BS network through collaborative adjustment of the phase shift matrix of the IRS, where UAVs can benefit from all IRSs in the region, leading to a significant increase in the network capacity. We formulate an optimization problem which aims to maximize the downlink sum-rate of the network by optimizing UAV-user association, transmit beamforming at UAVs, phase shifts of IRSs and UAVs’ locations. Considering the non-convexity of the original problem, we use Lagrangian dual transform and quadratic transform (QT) techniques to simplify the problem. Then we proposed an algorithm, named SLOTC, which combines successive refinement method, Lagrange multiplier method, oblique manifold method and Taylor-conjugate gradient method, to solve the simplified problem. Our proposed algorithm significantly outperforms existing benchmark schemes, as demonstrated by numerical results.

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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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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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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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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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Converged Service-based architecture for Next-Generation Mobile Communication networks

Converged Service-based Architecture for Next-Generation Mob...

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IEEE Conference on Wireless Communications and Networking

作者： Keliang Du Luhan Wang Zishen Zhu Yunan Yan Xiangming Wen Beijing Laboratory of Advanced Information Networks BUPT Beijing China Beijing Key Laboratory of Network System Architecture and Convergence BUPT Beijing China

Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies have driven mobile communication networks to evolve toward a Service-Based architecture (SBA) with great flexibility to meet the highly-dynamic requirements of vertical applications. The SBA has been applied into 5 th Generation (5G) core network and is evolving toward service-based end-to-end networks including virtualized Radio Access networks (vRANs). This paper proposes a converged SBA (cSBA) to decouple access, control, and data planes. Specifically, the access plane with Distributed Units (DUs) functionalities is responsible for accessing User Equipments (UEs); the control plane consists of newly-defined converged services by integrating functionalities of Central Unit Control Planes (CU-CPs) and 5G core network for signaling processing; the data plane comprises Central Unit User Planes (CP-UPs) and User Plane Functions (UPFs), which are responsible for service data transmission. Optional converged services definition, service-based interface implementation, and cognitive service framework are proposed to enable an efficient cSBA. Finally, we evaluate the performance of the cSBA against the monolithic architecture.

关键词： Protocols 5G mobile communication Process control Computer architecture Media Access Protocol Network function virtualization Synchronization

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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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Autonomous Driving via Brain-Inspired Causality-Aware Contrastive Learning with Time-Frequency Prediction

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IEEE Internet of Things Journal 2025年

作者： Wang, Chengyu Wang, Luhan Lu, Zhaoming Wu, Celimuge Shou, Guochu Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China Meta-Networking Research Center The University of Electro-Communications Tokyo182-8585 Japan

Developing trustworthy reinforcement learning (RL) agents for safety-critical control tasks, such as end-to-end autonomous driving, has been a longstanding challenge due to low sample efficiency. Prior works have attempted to address this challenge by performing self-supervised auxiliary tasks like self-reconstruction or predicting long-term future states. However, there still remain unexplored sequential features and causality relationships inherent in sequential state, action, and reward signals in the frequency domain. To fully exploit the temporal and frequential features, we propose a contrastive RL framework called BRain-Inspired causalitY-Aware coNTrastive learning (BRYANT) to achieve efficient representation learning and human-like autonomous driving. Different from existing temporal predictive methods, we transform the sequential latent representations, reward, and action signals into the frequency domain, followed by the symmetric temporal prediction pattern for real and imaginary parts of the frequential signals. To capture the temporal causality for the latent representations, we introduce a brain-inspired network structure called Closed-form Continuous-time (CfC) network to parameterize the derivative of the latent representations and establish the neural dynamic model. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of BRYANT in efficient representation learning, enabling agents to concentrate on potential risks and decrease the collision rate compared to several state-of-the-art RL methods. Furthermore, through the visualization of the latent representation prediction process, we reveal the causal relationships between the critic Q values and the latent representation vectors in the frequency domain, and demonstrate the effectiveness of the frequency domain prediction. © 2014 IEEE.

关键词： Reinforcement learning

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Signal-SGN: A Spiking Graph Convolutional Network for Skeleton Action Recognition via Learning Temporal-Frequency Dynamics

arXiv

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arXiv 2024年

作者： Zheng, Naichuan Du, Yuchen Xia, Hailun Liu, Dapeng 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

For skeleton-based action recognition, Graph Convolutional networks (GCNs) are effective models. Still, their reliance on floating-point computations leads to high energy consumption, limiting their applicability in battery-powered devices. While energy-efficient, Spiking Neural networks (SNNs) struggle to model skeleton dynamics, leading to suboptimal solutions. We propose Signal-SGN (Spiking Graph Convolutional Network), which utilizes the temporal dimension of skeleton sequences as the spike time steps and represents features as multi-dimensional discrete stochastic signals for temporal-frequency domain feature extraction. It combines the 1D Spiking Graph Convolution (1D-SGC) module and the Frequency Spiking Convolution (FSC) module to extract features from the skeleton represented as spiking form. Additionally, the Multi-Scale Wavelet Transform Feature Fusion (MWTF) module is proposed to extract dynamic spiking features and capture frequency-specific characteristics, enhancing classification performance. Experiments across three large-scale datasets reveal Signal-SGN exceeding state-of-the-art SNN-based methods in accuracy and computational efficiency while attaining comparable performance with GCN methods and significantly reducing theoretical energy consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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