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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Zhang, Xuanrui Xiang, Wan Chen, Yawen Zheng, Wei Lu, Zhaoming Wen, Xiangming 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: (纸本)9798350390643

This paper studies a multi-user integrated sensing and communication (ISAC) system with delay alignment modulation (DAM) in the near-field. We aim to optimize multi-path hybrid beamforming so as to maximize the signal-to-noise ratio (SNR) of the target echo while ensuring communication users' signal-to-interference plus-noise ratio (SINR) requirements and transmit power budget. To this end, we propose an alternating optimization algorithm using successive convex approximation (SCA) technology, penalty convex-concave procedure (PCCP) and second-order cone programming (SOCP). Simulation results demonstrate the effectiveness of the proposed algorithm and show the impact of channel model mismatch and the distance dimension introduced by the near-field model on the near-field DAM-ISAC system. © 2024 IEEE.

关键词： Signal to noise ratio

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Joint antenna position and transmit signal optimization for ISAC system with movable antenna array 35

Joint antenna position and transmit signal optimization for ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Xiang, Wan Chen, Yawen Zhang, Xuanrui Lu, Zhaoming Wen, Xiangming 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: (纸本)9798350362244

This paper studies an integrated sensing and communication (ISAC) system with movable antennas (MAs), where the position of antennas can be flexibly adjusted to reshape the wireless channel. We aim at jointly optimizing the positions of MAs, beamforming of the communication signal, and the covariance of the sensing signal to maximize the overall transmit beampattern gain while ensuring the quality of communication service requirement. Considering the non-convexity of this optimization problem, we propose an efficient algorithm to obtain a high-quality solution by using alternating optimization, semi-definite relaxation (SDR) and Taylors theorem. The simulation results show that the proposed algorithm significantly improves the beampattern gain. © 2024 IEEE.

关键词： Beamforming

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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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2023 IEEE Globecom Workshops, GC Wkshps 2023

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

ISBN: (纸本)9798350370218

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. © 2023 IEEE.

关键词： augmented reality Mobile edge computing offloading mobility awareness offline reinforcement learning terahertz communication

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Multi-IRS Assisted Multi-UAV Wireless Resource Allocation and Deployment Optimization

Multi-IRS Assisted Multi-UAV Wireless Resource Allocation an...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

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

ISBN: (纸本)9798350345384

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. © 2023 IEEE.

关键词： Cost effectiveness

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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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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Liu, Changhao Jing, Wenpeng Zheng, Ziyuan Lu, Zhaoming 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

ISBN: (纸本)9798350370218

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%. © 2023 IEEE.

关键词： Cloud-Edge cooperation On-line learning Satellite backhaul Super-Resolution Video streaming

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Keypoint-Restored Inverse Kinematic Solver 9

Keypoint-Restored Inverse Kinematic Solver

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ni, Yongkang Wu, Muqing Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350317251

Model-based 3D human body reconstruction provides a remarkable perspective for generating realistic full-body mesh in mesh-based 3D pose estimation. However, different strategies for capturing the nonlinear attributes of human model have varying effects on the efficiency and accuracy of recovering 3D human body. In this paper, our objective is to develop a simple and credible 3D human body parameter solver for skinned multi-person linear model (SMPL). To achieve this, we propose a keypoint-restored inverse kinematic solver (KRIKS), composed of inverse kinematics (IK) and iterative optimization, which transforms 3D keypoints to actual human shape and pose with mesh form. Additionally, considering the limitation of IK calculation and the relationship across pose frames, we extend our approach from single-frame analysis to video sequence in seq2frame manner. This integration leverages the temporal coherence of continuous actions, further enhancing the reconstruction performance. We demonstrate quantitative KRIKS baselines on the 3DPW and AMASS datasets, achieving impressive results with 0mm MPBE, 0.18mm PMPJPE, and 11.28mm MPVE on the 3DPW dataset. © 2023 IEEE.

关键词： Inverse kinematics

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A Satellite-Terrestrial Integrated Network architecture Based on Stateless Core Network 9

A Satellite-Terrestrial Integrated Network Architecture Base...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Huang, Shaoxin Wang, Luhan Wen, Xiangming Liu, Yu Niu, Haiwen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing100876 China

ISBN: (纸本)9798350317251

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. © 2023 IEEE.

关键词： Network architecture

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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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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Niu, Haiwen Wang, Luhan Lu, Zhaoming Du, Keliang Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (纸本)9798350370218

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/***. © 2023 IEEE.

关键词： attention deep learning (DL) semantic communication semantic correction U-net

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A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network

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Digital Communications and networks 2025年第1期11卷 92-105页

作者： Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu Beijing Laboratory of Advanced Information Networks Beijing Univ.of Posts&TelecomBeijing 100876China Beijing Key Lab of Network System Architecture and Convergence Beijing Univ.of Posts&TelecomBeijing 100876China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading ***,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy *** order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy ***,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov ***,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission ***,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real *** results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.

关键词： Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading Pipelining Delay-aware

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Optimizing Community Detection in Complex networks by Regrouping Gateways 9

Optimizing Community Detection in Complex Networks by Regrou...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Luo, Hongli Wu, Muqing Zhao, Min 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

ISBN: (纸本)9798350317251

Community detection that can extract potential hidden structures and key information of large-scale complex networks has been studied so many years. However, there exists a possibility of grouping the gateway incorrectly which is the central node communicates with the outside, thus resulting in a large loss of modularity. Here we propose a regrouping gateways (RG) algorithm for community detection to optimize the existing community partitions which have been divided by various algorithms. We design a heuristic optimization algorithm based on a breadth-first search strategy with less complexity than Leiden to improve modularity. Experiments carried out on multiple real networks show that RG exhibits good generality in effectively increasing the modularity and reducing the randomness of community detection algorithms. © 2023 IEEE.

关键词： Optimization

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