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Performance Analysis of Integrated Sensing and Communication Networks With Blockage Effects

IEEE TRANSACTIONS ON VEHICULAR technology 2024年第11期73卷 16876-16891页

作者： Sun, Zezhong Yan, Shi Jiang, Ning Zhou, Jiaen Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Communication-sensing integration represents an up-and-coming area of research, enabling wireless networks to simultaneously perform communication and sensing tasks. However, in urban cellular networks, the blockage of buildings results in a complex signal propagation environment, affecting the performance analysis of integrated sensing and communication (ISAC) networks. To overcome this obstacle, this paper constructs a comprehensive framework considering building blockage and employs a distance-correlated blockage model to analyze interference from line of sight (LoS), non-line of sight (NLoS), and target reflection cascading (TRC) links. Using stochastic geometric theory, expressions for signal-to-interference-plus-noise ratio (SINR) and coverage probability for communication and sensing in the presence of blockage are derived, allowing for a comprehensive comparison under the same parameters. The research findings indicate that blockage can positively impact coverage, especially in enhancing communication performance. The analysis also suggests that there exists an optimal base station (BS) density when blockage is of the same order of magnitude as the BS density, maximizing communication or sensing coverage probability.

关键词： Sensors Interference Radar Wireless sensor networks Analytical models Wireless networks Stochastic processes Blockage effect integrated sensing and communication (ISAC) performance analysis stochastic geometry

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RIS-Enabled Multi-Target Sensing: Performance Analysis and Space-Time Beamforming Design

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IEEE Transactions on Wireless Communications 2024年第10期23卷 13889-13903页

作者： Wang, Zhen Hu, Xiaoling Liu, Chenxi Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

Recently, reconfigurable intelligent surface (RIS) has gained growing research interests in sensing fields. While extensive efforts have been devoted to designing RIS space beamforming for improving sensing accuracy, the potential of RIS in improving sensing resolution has so far not been fully dug. In this paper, we investigate the fundamental performance of RIS-enabled multi-target sensing, and exploit the potential of RIS in simultaneously boosting resolution and accuracy. Specifically, based on the identification that sensing is to obtain target state information from the received echo signals, we adopt the sensing mutual information as the performance metric, thus enabling comprehensive evaluation of both sensing resolution and accuracy. Then, we derive the analytical expression of the sensing mutual information in our considered systems, revealing that in addition to providing beamforming gains to enhance sensing accuracy in the space domain, RIS can also flexibly vary its beamforming in the time domain to improve sensing resolution. Based on this result, we propose a novel space-time beamforming, in which space-domain and time-domain beamforming gains are utilized for enhancing sensing accuracy and resolution, respectively. Numerical results demonstrate the significant advantages of the proposed space-time beamforming scheme over the traditional space-only beamforming scheme in terms of sensing resolution. © 2024 IEEE.

关键词： Signal to noise ratio

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SMR: Spatial-Guided Model-Based Regression for 3D Hand Pose and Mesh Reconstruction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO technology 2024年第1期34卷 299-314页

作者： Sun, Haifeng Zheng, Xiaozheng Ren, Pengfei Wang, Jingyu Qi, Qi Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

3D hand reconstruction is an important technique for human-computer interaction. Interactive experience depends on the accuracy, efficiency, and robustness of the algorithm. Therefore, in this paper, we first propose a balanced framework called spatial-aware regression (SAR) to achieve precise and fast reconstruction. SAR can bridge convolutional networks and graph-structure networks more effectively than existing frameworks to fully exploit extracted spatial information using a novel spatial-aware initial graph building module. In addition, SAR uses adaptive-GCN to make keypoints interact efficiently and effectively;and regresses 2.5D belief maps to characterize uncertainty. SAR is highly flexible because it can predict an arbitrary number of keypoints and apply pose-guided refinement for coarse to fine regression. To produce more rational results for challenging cases and mitigate 3D label reliance, we also propose a more robust model-based framework called spatial-guided model-based regression (SMR) that is based on SAR. There are two critical designs of SMR: 1) it uses SAR to enhance the features with pose information to help the regression of hand model parameters;and 2) it regresses parameters in a spatially aware manner that is similar to SAR. Experiments demonstrate that the proposed frameworks surpass existing fully-supervised approaches on the FreiHAND, HO-3D, RHD, and STB datasets. Also, the performances of the proposed frameworks under weakly/self-supervised settings outperform other competitors. Meanwhile, the proposed frameworks are accurate and efficient.

关键词： Three-dimensional displays Feature extraction Decoding Predictive models Image reconstruction Computational modeling Solid modeling 3D hand pose estimation 3D hand mesh reconstruction

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Joint Network Function Placement and Routing Optimization in Dynamic Software-Defined Satellite-Terrestrial Integrated Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 5172-5186页

作者： Yuan, Shuo Sun, Yaohua Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Software-defined satellite-terrestrial integrated networks (SDSTNs) are seen as a promising paradigm for achieving high resource flexibility and global communication coverage. However, low latency service provisioning is still challenging due to the fast variation of network topology and limited onboard resource at low earth orbit satellites. To address this issue, we study service provisioning in SDSTNs via joint optimization of virtual network function (VNF) placement and routing planning with network dynamics characterized by a time-evolving graph. Aiming at minimizing average service latency, the corresponding problem is formulated as an integer nonlinear programming under resource, VNF deployment, and time-slotted flow constraints. Since exhaustive search is intractable, we transform the primary problem into an integer linear programming by involving auxiliary variables and then propose a Benders decomposition based branch-and-cut (BDBC) algorithm. Towards practical use, a time expansion-based decoupled greedy (TEDG) algorithm is further designed with rigorous complexity analysis. Extensive experiments demonstrate the optimality of BDBC algorithm and the low complexity of TEDG algorithm. Meanwhile, it is indicated that they can improve the number of completed services within a configuration period by up to 58% and reduce the average service latency by up to 17% compared to baseline schemes.

关键词： Routing Satellites Planning Dynamic scheduling Heuristic algorithms Network topology Topology Satellite-terrestrial integrated networks virtual network function placement routing planning end-to-end latency

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Reliability-Aware Task Replication for Mobile Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 24846-24857页

作者： Yang, Lipei Zhou, Ao Ma, Xiao Zhang, Yiran Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

As infrastructure deployment continues to expand worldwide, the development of the Internet of Vehicles has become increasingly feasible. With widespread cellular connectivity and powerful roadside computing capabilities, advanced driving assistance systems can now rely on roadside decision models in addition to vehicle-side ones, overcoming the limitations of a single vehicle's perception range. This shift has led to improvements in manufacturing efficiency, cruising range, and battery life of intelligent vehicles. However, maintaining the ultralow latency and high-reliability requirements of on-vehicle services is still a challenge due to air interface fluctuations and edge server computing loads, which could potentially jeopardize driving safety. To tackle this issue, we conducted real-world measurements of edge server access delay in LTE and 5G cellular networks. Our analysis identified key factors affecting delay distribution, leading to the development of an approximate fitting function for the delay probability density function. We also proposed a reliability-aware task replication algorithm that leverages delay samples and edge server status information to make real-time task replication and offloading decisions, minimizing replication while ensuring service reliability. Simulations based on real-world data sets indicate our approach reduces task completion delay by up to 42.11% and limits the maximum task replication redundancy peak value to 63.37%, effectively ensuring the reliability of on-vehicle services during driving.

关键词： Reliability Task analysis Delays Servers 5G mobile communication Real-time systems Wireless communication Mobile edge computing (MEC) service reliability task replication

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Online Offloading for Energy-Efficient and Delay-Aware MEC Systems With Cellular-Connected UAVs

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IEEE INTERNET OF THINGS JOURNAL 2024年第12期11卷 22321-22336页

作者： Liu, Binghong Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this article, an unmanned aerial vehicle (UAV)-mobile edge computing (MEC) network is considered, where cellular-connected UAVs can either handle the computing tasks locally or offload to base stations. Considering the emerging computation-intensive and delay-sensitive applications, how to strike a balance between energy and delay, is one of the key design issues in UAV-MEC networks. Against this backdrop, we establish a double-queue model innovatively, in which the virtual queue is introduced to sense the backlog status of the actual queue. As such, the delay guarantee of computing tasks can be turned into the stable control of virtual queues. The network quality and server heterogeneity are considered to schedule the workloads rationally. Based on the Lyapunov optimization method, we formulate the deterministic problem to achieve a tradeoff between the long-term energy consumption and the time-average traffic delay, by jointly optimizing the offloading decision, resource allocation and trajectory planning, subject to the constraints of queue stability, resource budgets and flying kinematics. To solve this mixed-integer nonlinear programming problem, we propose an energy-efficient and delay-aware online algorithm, in which the problem is first split into equivalent resource allocation and trajectory planning subproblems, and the closed-form solutions of power, slot and computing resource allocation can be derived. Then, based on the Lagrange dual and successive convex approximation methods, these subproblems are solved iteratively to explore the optimality. Extensive simulations validate the superiority of our proposed algorithm over various benchmark schemes, showing its effectiveness in minimizing the energy consumption while simultaneously maintaining the low latency.

关键词： Autonomous aerial vehicles Task analysis Resource management Energy consumption Delays Optimization Servers Lyapunov optimization mobile edge computing (MEC) resource allocation unmanned aerial vehicle (UAV) trajectory planning UAV

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A Relay System for Semantic Image Transmission Based on Shared Feature Extraction and Hyperprior Entropy Compression

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16158-16170页

作者： An, Wannian Bao, Zhicheng Liang, Haotai Dong, Chen Xu, Xiaodong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, the a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out, respectively, at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, compared with the element-distance-based scheme, the proposed shared feature extraction method based on Pearson correlation not only exhibits an approximate 0.2 advantage in multiscale structural similarity (MS-SSIM) but also efficiently eliminates the burden caused by the transmission of shared feature indexes.

关键词： Hyperprior entropy compression (HEC) relay system semantic communication shared feature extraction

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Hierarchical Multiple Split Federated Learning for Low-Carbon Resource-Constrained User Equipment

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 39127-39141页

作者： Guo, Chengwei Zhou, Fanqin Feng, Lei Li, Wenjing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Split federated learning (SFL) allows clients with limited resources to engage in distributed machine learning, yet it grapples with issues related to energy usage and the efficiency of training. We propose low-carbon hierarchical multiple SFL (HMSFL) to address these issues and advance sustainable computing. HMSFL partitions the client model into several segments, enabling local aggregation among clients. This process amplifies energy efficiency and diminishes the carbon footprint. We formulate the training cost minimization problem and solve it using a generalized task allocation algorithm. Evaluation across real-world tasks demonstrates that HMSFL achieves a 36% reduction in training time and a 33% decrease in energy consumption compared to baseline methods, showcasing its potential for sustainable distributed machine learning.

关键词： Training Computational modeling Servers Energy consumption Federated learning Costs Green computing Resource management Heuristic algorithms Collaboration Federated learning (FL) low carbon split learning (SL) sustainable computing training cost minimization (TCM)

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HDR-DANet: single HDR image reconstruction via dual attention

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-12页

作者： Ma, Jindong Zhang, Haitao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Key Lab Intelligent Telecomm Software & Mu Beijing Peoples R China

Restoring high dynamic range images with a single low dynamic range image is a challenging task due to the limited brightness range captured by most consumer camera sensors, leading to missing details in overexposed or underexposed areas of the image. The current methods suffer from weak reconstruction ability for inconspicuous complex features in the extreme exposure area, resulting in artifacts still appearing in the extreme exposure area after reconstruction. This paper proposes an end-to-end network based on dual attention to solve this problem. Specifically, this paper designs a pixel-level spatial attention mechanism with luminance mask to refine the spatial attention to the pixel level, and can focus on inconspicuous feature areas in the overexposed and underexposed regions. This paper also designs a concentrated channel attention mechanism to maximize the use of image context information, accurately assigning the channel weight and effectively extracting the complex features in the image. Qualitative and quantitative evaluations show the superiority of our approach over state-of-the-art methods.

关键词： HDR reconstruction Attention mechanism Convolutional neural network

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BUPTCMCC-6G-CMG+: a GBSM-based ISAC standard channel model generator

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Science China(Information Sciences) 2025年第5期68卷 235-249页

作者： Changsheng ZHAO Jianhua ZHANG Yuxiang ZHANG Lei TIAN Heng WANG Hanyuan JIANG Yameng LIU Wenjun CHEN Tao JIANG Guangyi LIU State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Future Research Laboratory China Mobile Research Institute

Integrated sensing and communication(ISAC) has been recognized as the key technology in the vision of the sixth generation(6G) era. With the emergence of new concepts in mobile communications, the channel model is the prerequisite for system design and performance evaluation. Currently, the 3rd generation partnership project(3GPP) Release 19 is advancing the standardization of ISAC channel models. Nevertheless, a unified modeling framework has yet to be established. This paper provides a simulation diagram of ISAC channel modeling extended based on the geometry-based stochastic model(GBSM),compatible with existing 5G channel models and the latest progress in the 3GPP Rel-19 standardization. We first introduce the progress of the ISAC channel model standardization in general. Then, a concatenated channel modeling method is presented considering the team's standardization proposals, which are implemented on the BUPTCMCC-6G-CMG+ channel model generator. We validated the model in cumulative probability density function in statistical extension of angle and delay, and radar cross section(RCS). Simulation results show that the proposed model can realistically characterize the feature of channel concatenation and RCS within the ISAC channel.

关键词： channel model generator channel concatenation ISAC channel radar cross section (RCS) 3GPP standardization

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