检索结果-内蒙古大学图书馆

Swin-Loc: Transformer-Based CSI Fingerprinting Indoor Localization With MIMO ISAC System

IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2024年第8期73卷 11664-11679页

作者： Xu, Xiaodong Zhu, Fangzhou Han, Shujun Yu, Zhongyao Zhao, Hangyu Wang, Bizhu Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

With multiple-input multiple-output (MIMO) technologies widely employed in mobile communication systems, wireless signals will have higher resolution in both the time and angle domains. It makes high-precision localization gain increasing attention in MIMO integrated sensing and communication (ISAC) systems. However, the decimeter-level precise indoor localization is full of challenges due to the multi-path fading and additional noise in indoor propagations. Excessive resource overhead and channel state information (CSI) fingerprint distortion in complex channel environments are the main factors that hinder indoor high-precision localization. To solve the CSI fingerprint distortion while reducing resource consumption, we propose a Swin Transformer-based CSI data-driven indoor localization framework called Swin-Loc. In the proposed Swin-Loc, a channel fingerprint extraction scheme is formulated to enhance the CSI features. Moreover, an improved Swin Transformer-based CSI network (SwinCSINet) model is proposed to improve localization precision. Experiments are conducted on the real-world CSI dataset given by the KU Leuven lab and the simulation CSI dataset generated by the DeepMIMO platform. Simulation results demonstrate that the localization precision on all datasets in the metric of root mean squared error (RMSE) is within 0.3 m, which outperforms current deep neural networks (DNNs) based schemeand attention-based scheme. Furthermore, the storage overhead of the improved SwinCSINet model is reduced to about 33$\%$ of the DNN regression model, and the real-time performance is optimized.

关键词： Location awareness Fingerprint recognition Feature extraction Transformers MIMO communication Data mining Wireless communication CSI fingerprint data-driven localization ISAC MIMO swin transformer

来源：评论

学校读者我要写书评

暂无评论

Enhancing NOMA Networks via Reconfigurable Multi-Functional Surface

引用

IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1195-1199页

作者： Zheng, Ailing Ni, Wanli Wang, Wen Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

By flexibly manipulating the radio propagation environment, reconfigurable intelligent surface (RIS) is a promising technique for future wireless communications. However, the single-side coverage and double-fading attenuation faced by conventional RISs largely restrict their applications. To address this issue, we propose a novel concept of multi-functional RIS (MF-RIS), which provides reflection, transmission, and amplification simultaneously for the incident signal. With the aim of enhancing the performance of a non-orthogonal multiple-access (NOMA) downlink multiuser network, we deploy an MF-RIS to maximize the sum rate by jointly optimizing the active beamforming and MF-RIS coefficients. Then, an alternating optimization algorithm is proposed to solve the formulated non-convex problem by exploiting successive convex approximation and penalty-based method. Numerical results show that the proposed MF-RIS outperforms conventional RISs under different settings.

关键词： NOMA Array signal processing Quality of service Decoding Radio propagation Power supplies Nickel Multi-functional reconfigurable intelligent surface non-orthogonal multiple access rate maximization

来源：评论

学校读者我要写书评

暂无评论

Flexible Cyclic Queuing and Forwarding for Time-Sensitive Software-Defined Networks

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 533-546页

作者： Huang, Yudong Wang, Shuo Zhang, Xinyuan Huang, Tao Liu, Yunjie Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

Time-Sensitive networking (TSN) is emerging to support critical real-time applications in Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in TSN. However, the CQF is not flexible enough in two aspects. First, it cannot achieve zero jitter. The Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows require zero jitter. Second, it may require setting the maximum queue length to a fixed value in advance and scheduling the flows offline, which is challenging for dynamic traffic scheduling. In this paper, we firstly present a time-aware cyclic-queuing (TACQ) mechanism to enable zero jitter for CQF. TACQ consists of a novel no-wait shaper (NWS) and a cyclic-queuing shaper (CQS). The NWS handles isochronous flows by strictly limiting the transmission time of flows that do not overlap on each output port and each period. The CQS is extended from CQF to schedule cyclic flows. Then, we propose a variable time slot mechanism and a novel incremental routing and scheduling (IRAS) algorithm based on software-defined networking (SDN) to online schedule dynamic flows. Simulation results show that TACQ significantly reduces the delay of isochronous flows and achieves zero jitter compared with CQF. And the IRAS algorithm approaches 96.1% of the optimal solution in scheduling 2000 flows with a feasible per-flow computational time.

关键词： Jitter Delays Job shop scheduling Processor scheduling Schedules Logic gates Synchronization Time-sensitive networks cyclic queuing and forwarding zero-jitter incremental routing and scheduling

来源：评论

学校读者我要写书评

暂无评论

Flight Trajectory Control With Network-Oriented Hierarchical Reinforcement Learning for UAVs-Assisted Data Time-Sensitive IoT

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6332-6345页

作者： Wang, Jingyu Li, Songyang Chen, Dezhi Fu, Qianlong Qi, Qi Sun, Haifeng He, Bo Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Pengcheng Lab Shenzhen 518000 Peoples R China

Within the Internet of Things (IoT) for traffic monitoring, the employment of autonomous aerial vehicles (AAVs) as relays for collecting and transmitting real-time data from traffic sensors to base stations has proven a promising approach. In UAV-assisted Data Time-Sensitive IoT (DTIoT), the Age of Information is a crucial metric assessing data freshness, measuring the elapsed time from traffic sensors to the base station. Optimizing flight trajectories of multiple UAVs to minimize AoI while adhering to energy constraints poses a significant challenge. Current research often employs deep reinforcement learning for UAV trajectory control. Nevertheless, managing multi-agent continuous trajectories in intricate DTIoT network environments faces obstacles due to sparse rewards, thus impeding the training of deep neural network-based control policies using traditional DRL techniques. In this paper, we propose a network-oriented hierarchical reinforcement learning (NO-HRL) to control the UAVs' flight trajectory in DTIoT networks for minimizing the AoI. We devise a control policy leveraging a two-tier hierarchical DRL framework, with the upper tier determining the target and the lower tier executing it. We also introduce a decoupled sequential training approach to efficiently train the mutually dependent two-tier DRL network of NO-HRL. Experimental results demonstrate that our method excels in optimizing AoI for DTIoT compared to other baselines.

关键词： Trajectory Autonomous aerial vehicles Sensors Training Internet of Things Aerospace electronics Monitoring Data collection Base stations Information age Autonomous aerial vehicle (AAV) sensor data collection age of information hierarchical reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Double-Lead Content Search And Producer Location Prediction Scheme For Producer Mobility In Named Data networking

引用

COMPUTER JOURNAL 2023年第11期66卷 2825-2843页

作者： Rui, Lanlan Dai, Shiyue Gao, Zhipeng Qiu, Xuesong Chen, Xingyu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol 10 Xitucheng Rd Beijing Peoples R China

In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer's movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.

关键词： Named Data networking Producer Mobility Double-Lead Producer Location Prediction

来源：评论

学校读者我要写书评

暂无评论

Exploiting Structured Sparsity in Near Field: From the Perspective of Decomposition

引用

IEEE COMMUNICATIONS MAGAZINE 2025年第1期63卷 37-43页

作者： Guo, Xufeng Chen, Yuanbin Wang, Ying Yuen, Chau Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Singapore

The structured sparsity can be leveraged in traditional far-field channels, greatly facilitating efficient sparse channel recovery by compressing the complexity of overheads to the level of the scatterer number. However, when experiencing a fundamental shift from planar-wave-based far-field modeling to spherical-wave-based near-field modeling, whether these benefits persist in the near-field regime remains an open issue. To answer this question, this article delves into structured sparsity in the near-field realm, examining its peculiarities and challenges. In particular, we present the key features of near-field structured sparsity in contrast to the far-field counterpart, drawing from both physical and mathematical perspectives. Upon unmasking the theoretical bottlenecks, we resort to bypassing them by decoupling the geometric parameters of the scatterers, termed the triple parametric decomposition (TPD) framework. It is demonstrated that our novel TPD framework can achieve robust recovery of near-field sparse channels by applying the potential structured sparsity and avoiding the curse of complexity and overhead.

关键词： Three-dimensional displays Complexity theory Near field communication Telecommunication network management Channel estimation Wireless sensor networks Linear antenna arrays 6G mobile communication switching circuits Spectral efficiency Space division multiplexing MIMO

来源：评论

学校读者我要写书评

暂无评论

MSTGAN: A Multi-Slot Conditional Generative Adversarial Network Based on Swin Transformer for Channel Estimation

引用

IEEE COMMUNICATIONS LETTERS 2023年第7期27卷 1799-1803页

作者： Cheng, Lujie Zhang, Zhi Dong, Chen Liu, Sirui Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Accurate channel estimation is the key to enhance the transmission performance of communication systems. In this letter, we propose a multi-slot conditional generative adversarial network (cGAN) based on Swin Transformer called MSTGAN for channel estimation in a single-input single-output (SISO) scenario. Specifically, the proposed MSTGAN could learn the temporal correlation feature from continuous channel slots by the 3D convolution and extract the deep feature well by the Swin Transformer to improve the accuracy of channel estimation. The model is trained with data augmentation to reduce the computational cost for offline training. The simulation results demonstrate that the proposed method outperforms the linear minimum mean square error (LMMSE) method and other deep learning methods. Furthermore, extension schemes of the MSTGAN to the multiple-input multiple-output (MIMO) case are also provided.

关键词： Channel estimation deep learning conditional generative adversarial network swin transformer multi-slot

来源：评论

学校读者我要写书评

暂无评论

Network Management Service Composition Migration Method Based on Anomaly Detection 29

Network Management Service Composition Migration Method Base...

引用

29th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Qu, Zhenying Yang, Yang Sun, Yating Gao, Zhipeng Rui, Lanlan Xu, Siya Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350354249;9798350354232

The complexity and dynamism of modern networks pose significant challenges to network management services. Existing technologies often exhibit latency in migrating services after encountering problems. However, adopting a proactive approach through anomaly detection before migration enables the early reservation of resources, thereby ensuring overall performance and stability. This paper proposes a Network Management Service Composition (NMSC) migration method leveraging anomaly detection. The method includes an anomaly detection approach using a Transformer model with a time decay mechanism and a multi-agent reinforcement learning algorithm enhanced by a graph attention autoencoder. First, a time decay mechanism is designed to enhance the self-attention mechanism, allowing the model to capture long-term dependencies while maintaining high sensitivity to recent events. Second, a critic network, enhanced by a graph attention autoencoder, enables the multi-agent algorithm to comprehend the interaction dynamics among agents during computation. Experimental results demonstrate that the proposed anomaly detection algorithm significantly outperforms existing algorithms. Furthermore, the service composition migration algorithm exhibits superior performance in terms of reward value and migration delay, thus proving its effectiveness and feasibility.

关键词： service migration reinforcement learning anomaly detection transformer

来源：评论

学校读者我要写书评

暂无评论

FANT: Flexible Attention-Shifting Network Telemetry

引用

APPLIED SCIENCES-BASEL 2025年第2期15卷 892-892页

作者： Cui, Mingwei Peng, Yufan Yang, Fan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

As data center networks grow in scale and complexity, the active inband network telemetry (AINT) system collects a broader range of network status metrics to provide comprehensive visibility for AINT-related network applications, but it also leads to higher measurement costs. To address this issue, we introduce the Flexible Attention-shifting Network Telemetry (FANT), which dynamically focuses on critical links in each measurement cycle. Specifically, FANT employs a metric categorization strategy that divides all measurement metrics into two categories: basic measurements, which are lightweight but cover fewer metrics, and detailed measurements, which are comprehensive but incur higher overhead. Based on the analysis of the previous cycle's measurements, FANT identifies which links are suspicious and then activates certain probe traces through an attention-shifting mechanism to collect detailed measurements of these links in the current cycle. To further save bandwidth, we model the attention-shifting process and apply heuristic algorithms to solve it. Our experiments show that FANT effectively supports the operation of ANT network applications. In a fat-tree topology with 30 pods, FANT significantly reduces bandwidth usage to 42.6% of the state-of-the-art solution. For scenarios requiring rapid computation, FANT can accelerate algorithm execution 105x by setting acceleration factors, with only a 6.4% performance loss.

关键词： active inband network telemetry monitoring programmable data plane

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Shooting and Bouncing Rays Method for Ray-Tracing Channel Modeling Assisted by Environmental Prior Information for 6G 35

An Adaptive Shooting and Bouncing Rays Method for Ray-Tracin...

引用

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

作者： Luo, Yubin Yu, Li Miao, Yinghe Zhang, Yuxiang Zhang, Jianhua Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology China

ISBN: (纸本)9798350362244

Due to the fact that the scale and construction of 6G communication systems are progressing toward unprecedentedly large and complex, accuracy and robustness of channel modeling method are vital to ensure that communication systems can work efficiently. Ray-Tracing (RT), as a precise deterministic modeling method, can meet most of these requirements. Shooting and Bouncing Rays (SBR) is a forward RT algorithm with high efficiency and wide usage. Traditional SBR utilizes environmental prior information, like distribution of scatters, only in path-finding. Channel parameters like quantity of rays are determined artificially, which affects SBR's performance especially in 6G systems. In order to enable SBR to conduct 6G modeling stably and efficiently, we propose an advanced SBR algorithm that utilizes environmental prior information globally for both path-finding progress and parameter-determination. Space-division, face-classification are some environmental-information-assisted processes of the proposed algorithm, which help to calculate channel parameters quantitatively. Additionally, our new algorithm accelerates path-finding progress by decreasing unnecessary intersections through space-division. Simulation reveals our new algorithm reduces computational complexity by 34.53% in comparison of traditional SBR. Furthermore, with Image Method (IM) as a standard, new algorithm has smaller Normalized Mean Squared Error (NMSE) of 78.09% less in LOS-path ignoring power-receiving, compared to traditional SBR. Other metrics such as Power-Delay Profile (PDP) are also demonstrated in our work. © 2024 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：