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Scalable and Efficient URLLC Resource Allocation Based on Offline Graph Reinforcement Learning

Scalable and Efficient URLLC Resource Allocation Based on Of...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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

Deep reinforcement learning (RL) has emerged as a transformative technology for addressing intricate radio resource allocation problems in both 5G and 6G networks. Nevertheless, during the training phases of RL-based resource allocation algorithms, online RL methods introduce potential risks as they necessitate continuous interaction with the environment. Motivated by the fact that the historical data of the resource allocation process in the base station (BS) reveals the operation rules of the mobile network dynamics, this paper proposes a novel data-driven framework for resource allocation optimization, termed Offline Graph Reinforcement Learning (OGRL). Different from existing intelligent resource allocation schemes, which acquire policies by interacting either with wireless network simulators or real-world networks, the proposed OGRL framework exclusively leverages historical data from BS to train a high-performance resource allocation policy. Furthermore, by harnessing the capabilities of the graph neural networks (GNNs) at processing intricate data structures, OGRL can adeptly manage dynamic network environments characterized by continuously changing active users. Extensive experimental results substantiate that, following training with historical data, the proposed OGRL attains Quality of Service (QoS) and packet loss rates comparable to those of the best online algorithms. Furthermore, OGRL exhibits excellent scalability and generalization capabilities.

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Video-Based Multi-Stage 3D Human Mesh Reconstruction

Video-Based Multi-Stage 3D Human Mesh Reconstruction

引用

International Conference on Computer and Communications (ICCC)

作者： Zhiwen Qiu Muqing Wu Qihui Su Chengzhang Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Existing Model-based 3D human reconstruction provides a new perspective for generating realistic full-body meshes for 3D human pose estimation. However, many existing methods for reconstructing human meshes typically rely on neural networks to directly estimate the parameters of the SMPL model, subsequently utilizing the SMPL model to recover the human mesh. Despite significant progress in recent research, these methods still encounter several issues, including shape estimation overfitting, cumulative rotation errors, and rotational semantic ambiguities. Furthermore, these methods have not leveraged recent advancements in 2D and 3D pose estimation. To address these challenges, we propose a framework that decomposes the task of reconstructing 3D human meshes into three components: extracting 2D keypoints, a 2D-to-3D lifting network, and fitting the rotations of the keypoints. We decompose the rotation of the 3D keypoints into twist and swing components. In the 2D-to-3D network, we regress the 3D keypoint locations, shape parameters, and twist of the keypoints, while also introducing an optimization method for fitting the rotations. We conducted experiments on the Human3.6M dataset, achieving significant performance improvements.

关键词： Solid modeling Three-dimensional displays Shape Pose estimation Pipelines Neural networks Fitting Semantics Bones Overfitting

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Human Pose Estimation Based on Multi-branch Convolutional Neural Network 2

Human Pose Estimation Based on Multi-branch Convolutional Ne...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Zhang, Haochen Xia, Hailun Zhang, Rui Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781665442763

With the development of the convolutional neural network, significant progress has been made in human pose estimation. Recent human pose estimation approaches adopt a single branch network, which can only provide limited surveillance information. Aiming at the problem, we propose to adopt a multi-branch network for both 2D human pose estimation and 3D human pose estimation. Leading results on multiple datasets verify the effectiveness of our multi-branch network. © 2022 IEEE.

关键词： Convolutional neural networks

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A UCA-Based Orbital Angular Momentum Solution for Integrated Sensing and Communication systems

A UCA-Based Orbital Angular Momentum Solution for Integrated...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zheng, Yihong Wang, Shumin Chu, Xinghe Zheng, Wei Hu, Zhiqun Lu, Zhaoming 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)9798350368369

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods. © 2025 IEEE.

关键词： Target drones

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Sparsely Deployed Bluetooth and Magnetic Fusion Positioning for Large Underground Parking Lots

Sparsely Deployed Bluetooth and Magnetic Fusion Positioning ...

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IEEE Conference on Wireless Communications and Networking

作者： Ziwen Luo Yihong Zheng Shuya Zhou Shumin Wang Xinghe Chu Luhan Wang Zhaoming Lu 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: (数字)9798350368369

ISBN: (纸本)9798350368376

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m 2 ), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices.

关键词： Global navigation satellite system Bluetooth Accuracy Costs Heuristic algorithms Magnetic fields Received signal strength indicator Vehicle dynamics Low latency communication Smart phones

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Sparsely Deployed Bluetooth and Magnetic Fusion Positioning for Large Underground Parking Lots

Sparsely Deployed Bluetooth and Magnetic Fusion Positioning ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Luo, Ziwen Zheng, Yihong Zhou, Shuya Wang, Shumin Chu, Xinghe Wang, Luhan Lu, Zhaoming 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)9798350368369

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m2), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices. © 2025 IEEE.

关键词： Global positioning system

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Dynamic Acceleration-Aware Risk Potential Field Car-Following Model for Heterogeneous Individuals

Dynamic Acceleration-Aware Risk Potential Field Car-Followin...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Yuchen Wang, Chengyu Shi, Zhengrui Wang, Luhan Chen, Yawen Li, Sirui 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)9798350368369

In the developing phase of connected autonomous driving, roads are populated with heterogeneous individuals, i.e., connected and unconnected vehicles, autonomous driving and human driving, resulting in intricate interactions and significant uncertainty in their motion trajectories. At present, the research of modeling under mixed traffic scenarios has the following challenges: 1) Traditional path planning ignores dynamic risks from the third-order vehicle state, i.e., acceleration;2) Existing microscopic models lack the descriptions for the diverse decision-making patterns among heterogeneous individuals, which increases the uncertainty and complexity of the scene. In this paper, we propose a novel Risk Potential Field car-Following Model (RPFFM) tailored for heterogeneous individuals. It employs differentiated parameter adjustments, integrating forward propulsion with potential field repulsion to calculate vehicle acceleration. RPFFM achieves proactive and safe car-following by perceiving the change of dynamic obstacle acceleration through dynamic factors and converting it into the variation of the dynamic potential field. Experimental results based on the Next Generation Simulation (NGSIM) dataset demonstrate that the RPFFM surpasses both the Intelligent Driver Model (IDM) and Optimal Velocity Model (OVM) in terms of effectiveness, safety, and providing enhanced car-following comfort. © 2025 IEEE.

关键词： Automobile driver simulators

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One-Dimensional Bilateration Localization Algorithm Based on Indoor Distributed Antenna systems

One-Dimensional Bilateration Localization Algorithm Based on...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Hu, Ruoqian Chu, Xinghe Lu, Zhaoming Wang, Luhan 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)9798350368369

Indoor spaces have already been accounted for a significant proportion of people's lives, and with the advent of the fifth-generation mobile communication technology (5G) era and the rise of the Internet of Things (IoT), the demand for indoor localization is further increasing. Current solutions for indoor positioning, such as Bluetooth, Wi-Fi, ultra-wideband (UWB), and pseudo-satellite technologies, have faced issues in terms of cost, accuracy, and scalability during the promotion and application process. In this regard, distributed antenna systems (DAS) have an absolute advantage in terms of indoor coverage for communication, which shows enormous potential for fusion with current indoor positioning technologies. This paper proposed a universal one-dimensional localization algorithm fusing UWB technology with the indoor DAS system, aimed at narrow indoor environments such as corridors and cable tunnels, which can achieve high-precision localization as evaluated in a real scenario of an indoor corridor in the Beijing University of Posts and Telecommunications (BUPT). Additionally, our algorithm requires only two anchors, effectively reducing positioning costs while improving accuracy. The results indicate that this localization technique effectively meets the requirements defined for indoor scenarios in 3GPP 38.855, with an error of decimeter level. © 2025 IEEE.

关键词： Ultra-wideband (UWB)

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One-Dimensional Bilateration Localization Algorithm Based on Indoor Distributed Antenna systems

One-Dimensional Bilateration Localization Algorithm Based on...

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IEEE Conference on Wireless Communications and Networking

作者： Ruoqian Hu Xinghe Chu Zhaoming Lu Luhan Wang 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: (数字)9798350368369

ISBN: (纸本)9798350368376

关键词： Location awareness Costs Accuracy Scalability Ultra wideband antennas Telecommunications Internet of Things 3GPP Wireless fidelity Antenna arrays

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Mask guided attention for fetal standard plane recognition

Mask guided attention for fetal standard plane recognition

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2022 International Conference on Computer Application and Information Security, ICCAIS 2022

作者： Li, Ruijie Guo, Caili Xu, Hong Li, Zheng 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 Beijing China Department of Ultrasound Diagnosis First Medical Center PLA General Hospital Beijing China

ISBN: (纸本)9781510663459

At present, ultrasound is the most widely used in prenatal diagnosis. The accurate acquisition of fetal standard plane is very important in prenatal diagnosis. However, the recognition of fetal standard planes (standard versus non-standard) is a challenging task, mainly because of the need to simultaneously recognize several key anatomical structures. In this paper, we propose a novel method termed mask guided attention (MGA) to integrate global and diagnostic region cues to recognize the fetal standard plane, imitating the sonographer's diagnostic mode. In addition, to further mine key anatomical structures, an innovative attention mining (AM) module is designed to improve the MGA. Utilizing the clinical dataset, we demonstrate that MGA significantly improve the performance of fetal standard plane recognition tasks. © 2023 SPIE.

关键词： Computer aided diagnosis

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