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A robust TDOA localization method for researching upper bound on NLOS ranging error

SIGNAL PROCESSING 2025年 235卷

作者： Shui, Wenqi Xiong, Mingjie Mai, Wen Qin, Shuang Key Lab Wireless Sensor Network Univ Coll Phys & Elect Engn Chengdu 610101 Sichuan Peoples R China

In time-difference-of-arrival (TDOA) localization, the accuracy of the robust convex optimization algorithm is significantly affected by the upper bound of non-line-of-sight (NLOS) ranging error. Some algorithms estimate NLOS ranging error and source coordinates together, which makes the NLOS upper bound seem unnecessary, but this often leads to lower localization accuracy. Therefore, it is crucial to establish a reasonable upper bound for the NLOS ranging error. However, existing robust optimization algorithms suffer a significant drawback when introducing upper bounds on NLOS ranging error: The assignment of the upper bound of the NLOS ranging error does not match the actual environment, leading to poor algorithm accuracy. To solve the rationality problem of the NLOS ranging error upper bound assignment, this paper innovatively proposes the NLOS error upper bound as an uncertain variable arising from NLOS variability. We then develop a robust optimization formulation using matrix transformations to optimize the upper bound. By applying techniques such as linearization and perturbation decomposition, we derive an optimal solution that adjusts the NLOS error upper bound. Simulations and experiments show that the proposed approach outperforms existing algorithms in terms of localization accuracy in NLOS environments.

关键词： Time-difference-of-arrival (TDOA) Non-line-of-sight (NLOS) wireless localization Robust location Convex optimization

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Combining the Inherent Distance Geometry Constraints of CMD and Triangulation Constraints to Improve Positioning Accuracy

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IEEE sensorS JOURNAL 2025年第5期25卷 8528-8538页

作者： Long, Yu Xiong, Mingjie Sun, Hui Zhao, Qi Shui, Wenqi Qin, Shuang Sichuan Normal Univ Coll Phys & Elect Engn Key Lab Wireless Sensor Network Univ Sichuan Prov Chengdu 610101 Peoples R China

In wireless sensor networks (WSNs), sources are usually localized with the help of pre-deployed base stations (BSs). Due to the unknown environment, the main challenge in source localization is the non-line-of-sight (NLOS) error. This article focuses on two approaches, such as triangulation constraints based on the relationships between each BS, and distance geometry constraints of Cayley-Menger determinants (CMDs). However, there is no literature using them jointly. Therefore, this study aims to combine triangulation constraints and distance geometry constraints of CMD to improve localization performance. First, we obtain the weight coefficients for all combinations of BSs by solving the Karush-Kuhn-Tucker (KKT) conditions for the triangular constraints and the distance geometry constraints of CMD. Since various combinations of BSs have different weighting coefficients, this leads to uncertainty. Then, based on the uncertainty of the weight coefficients, we formulate the source localization problem as a robust least squares (RLS) problem. Finally, we eliminate the uncertainty by continuously relaxing the constraints through some mathematical methods, such as Cauchy-Schwarz inequality and norm compatibility. In addition, excessively relaxing the constraints may lead to low estimation accuracy, so we add some inherent constraints. Simulations and experiments demonstrate that the proposed method can perform well in different environments.

关键词： wireless sensor networks Location awareness Accuracy Geometry Upper bound Optimization sensors Mathematical models wireless communication Vectors Cayley-Menger determinant (CMD) Karush-Kuhn-Tucker (KKT) conditions non-line-of-sight (NLOS) robust optimization wireless localization

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A teacher-student based attention network for fine-grainedimage recognition

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Digital communications and networks 2025年第1期11卷 52-59页

作者： Ang Li Xueyi Zhang Peilin Li Bin Kang Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing210003China School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China

Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car *** order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature ***,these works pay less attention on global context *** neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view ***,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of *** framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention *** this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region *** fine-grained global information,we design a graph convolution based Global Attention network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among *** last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and *** experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.

关键词： Fine-grained image recognition Collaborative teacher-student strategy Multi-attention Global attention

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Equivalent Bandwidth Concept and Its Usage in the network Selection

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China communications 2025年第2期22卷 213-225页

作者： Ma Anhua Pan Su Zhou Weiwei Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and TelecommunicationsNanjing 210003China

Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in different networks cannot be compared *** paper proposes a network selection algorithm for heterogeneous ***,the concept of equivalent bandwidth is proposed,through which the actual resources occupied by users with certain QoS requirements in different networks can be compared *** the concept of network applicability is defined to express the abilities of networks to support different *** proposed network selection algorithm first evaluates whether the network has enough equivalent bandwidth required by the user and then prioritizes network with poor applicability to avoid the situation that there are still residual resources in entire network,but advanced services can not be *** simulation results show that the proposed algorithm obtained better performance than the baselines in terms of reducing call blocking probability and improving network resource utilization efficiency.

关键词： call blocking probability equivalent bandwidth heterogeneous network network applicability

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Joint optimization of energy consumption and latency for task offloading in cloud-edge collaboration system

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Jiang, Xue Dou, Haie Wang, Lei Xia, Zhijie Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Nanjing Peoples R China

Ina cloud-edge collaboration system, ensuring consistent wireless connectivity that meets the quality of service (QoS) for users with varying requirements is challenging. Therefore, the approach to task offloading for all mobile devices (MDs) should be carefully designed to enhance performance based on different requirements. This paper introduces a novel multi-objective genetic algorithm (MOGA) to address the task offloading problem, considering minimal energy consumption and latency simultaneously. Experimental results demonstrate that the proposed multi-objective genetic algorithm achieves rapid convergence and optimizes delay and energy consumption.

关键词： Cloud-edge collaborative Task offloading Multi-objective genetic algorithm

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Semantic ghost imaging based on semantic coding

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OPTICS AND LASER TECHNOLOGY 2025年 181卷

作者： Zhao, Shengmei He, Zheng Wang, Le Nanjing Univ Posts & Telecommun Inst Signal Proc & Transmiss Nanjing 210003 Peoples R China Sensor Network Technol Key Lab Broadband Wireless Commun Minist Educ Nanjing 210003 Peoples R China Nanjing Univ Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China

To address the challenge of reconstructing high-quality images under low sampling during the Ghost Imaging (GI) procedure, we propose a semantic GI method based on semantic encoding. Through training, the obtained continuous weights of the convolution kernels are served as the speckle patterns. A processing module and a Recurrent Neural network (RNN) are then employed to decode and reconstruct the images. Both the speckle patterns and their corresponding bucket values are optimized, so that the semantic information of the target object can be better extracted. With different gated recurrent units (GRU) layers for the target objects in different datasets, the feasibility of the proposed GI method is validated by the numerical simulations and experiments on the simple target objects (the handwriting digits "0"to "9") and more complex target objects ("NUPT"). The results show that the target objects (the handwriting digits) can be reconstructed with higher quality even at a lower sampling rate of 1.28%. Additionally, the proposed method has applicability for more complex objects (such as "NUPT") in real applications. In comparison with those results by using traditional ghost imaging (TGI), deep convolution auto-encoder network (DCAN), and RNN-based GI (GI-RNN), the proposed GI method shows a better performance in terms of the quality of reconstructed images and the training time, that is, the proposed method can have both good reconstructed image quality and less training time. By introducing the concept of semantic communication into GI, the proposed method provides a new idea for the model-based GI.

关键词： Ghost imaging Speckles optimization Deep learning Recurrent Neural network

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DOGS: Dynamic Task Offloading in Space-AirGround Integrated networks With Game-Theoretic Stochastic Learning

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1655-1672页

作者： Zhang, Jing Zhang, Jiaxuan Shen, Fei Yan, Feng Bu, Zhiyong Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

The space-air-ground integrated network (SAGIN) integrates satellites, unmanned aerial vehicles (UAVs), and terrestrial remote clouds to provide seamless network access and high-volume computing services for remote Internet of Things (IoT) devices, thus alleviating geographic and resource constraints. Existing methods typically focus on the network dynamics while overlooking the comprehensive consideration of device dynamics, namely, the time-varying task performance weights, task sizes, and task processing demands. Moreover, the centralized learning-based offloading schemes often lead to substantial signaling overhead. To bridge these gaps, this article proposes a distributed dynamic task offloading mechanism with game-theoretic multiagent stochastic learning (MASL). Technically, a stochastic game is formulated with each device as a player minimizing its weighted sum cost of latency and energy. We prove the existence of Nash equilibrium (NE) for our proposed game and propose a multiagent entropy-enhanced stochastic learning (MESL) algorithm in a fully distributed manner with no information exchange among IoT devices. By introducing the entropy of decision probability for each device, MESL increases decision dimensions, accelerates convergence, and facilitates optimal strategy achievement. Experimental results show that the MESL algorithm significantly reduces the overall cost and greatly enhances the convergence speed in dynamic SAGIN environments compared to existing algorithms.

关键词： Internet of Things Heuristic algorithms Games Dynamic scheduling Satellites Space-air-ground integrated networks Performance evaluation Distributed computing offloading multiagent stochastic learning (MASL) Nash equilibrium (NE) potential game space-air-ground integrated network (SAGIN)

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Precision flatness measurement based on orbital angular momentum

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CHINESE PHYSICS B 2025年第5期34卷 054204-054204页

作者： Han, Feifei Wang, Zhiwan Wang, Le Zhao, Shengmei Nanjing Univ Posts & Telecommun Inst Signal Proc & Transmiss Nanjing 210003 Peoples R China Minist Educ Key Lab Broadband Wireless Commun & Sensor Network Nanjing 210003 Peoples R China Nanjing Univ Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China

We propose a method to measure the flatness of an object with a petal-like pattern generated by the interference of the measured orbital angular momentum (OAM) beam and the reference OAM beam which carries the opposite OAM state. By calculating the difference between the petal rotation angle without/with the object, the thickness information of the object, and then the flatness information, can be evaluated. Furthermore, the direction of the object's flatness can be determined by the petal's clockwise/counterclockwise rotation. We theoretically analyze the relationship between the object's thickness and petal rotation angle, and verify the proposed method by experiment. The experimental results show that the proposed method is a high precision flatness measurement and can obtain the convex/concave property of the flatness. For the 1.02 mm glass sample, the mean deviation of the flatness is 1.357 x 10-8 and the variance is 0.242 x 10-16. For the 0.50 mm glass sample, the mean deviation of the flatness is 1.931 x 10-8 and the variance is 2.405 x 10-16. Two different topological charges are adopted for the 2.00 mm glass sample, and their flatness deviations are 0.239 x 10-8 (& ell;= 1) and 0.246 x 10-8 (& ell;= 2), where their variances are 0.799 x 10-18 (& ell;= 1) and 0.775 x 10-18 (& ell;= 2), respectively. It is shown that the flatness measured by the proposed method is the same for the same sample when different topological charges are used. All results indicate that the proposed method may provide a high flatness measurement, and will be a promising way to measure the flatness.

关键词： orbital angular momentum flatness measurement interference petal rotation angle 42.50.Tx 42.25.Hz 43.20.Ye

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Re-pairing strategy for mode-pairing quantum key distribution

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QUANTUM INFORMATION PROCESSING 2025年第3期24卷

作者： Zhou, Xing-Yu Hu, Jia-Rui Zhang, Chun-Hui Wang, Qin Nanjing Univ Posts & Telecommun Inst Quantum Informat & Technol Nanjing 210003 Peoples R China NUPT Broadband Wireless Commun & Sensor Network Technol Key Lab Minist Educ Nanjing 210003 Peoples R China NUPT Telecommun & Networks Natl Engn Res Ctr Nanjing 210003 Peoples R China

Mode-pairing quantum key distribution (MP-QKD) leverages post-selection pairing strategy to achieve a balance between ease of implementation and high performance, establishing itself as a highly promising protocol in future QKD networks. However, the reliance on pairing strategies causes a considerable number of pulses to be excluded from parameter estimation and key generation, introducing inefficiencies and diminishing overall performance. In this study, we extend the original protocol by incorporating an active pairing strategy. This involves actively pairing clicking events that fall within the same phase slice, thereby generating new successful pairs and enabling the reutilization of previously discarded events. To validate the efficiency of our scheme, we conducted simulations of the three-intensity decoy-state MP-QKD protocol with the double-scanning method. The simulation results demonstrate significant advantages in both parameter estimation and final key generation. Therefore, this study provides valuable insights for advancing future MP-QKD experiments and applications.

关键词： Quantum key distribution MP-QKD Re-pairing strategy Quantum cryptography

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An improved grey wolf algorithm and its localization research in complex indoor environments

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Li, Bing Hao, Yanxin Cui, Yiyang Chai, Xingshao Zhou, Jingmei Liu, Chungang Hebei Normal Univ Coll Combust Engn Shijiazhuang 050024 Peoples R China Hebei Prov Key Lab Informat Fus & Intelligent Cont Shijiazhuang 050024 Peoples R China Hebei Prov Innovat Ctr Wireless Sensor Network Dat Shijiazhuang 050024 Peoples R China

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse

关键词： Ultra-wideband technology 3D indoor localization Swarm algorithm Grey wolf optimization algorithm Localization accuracy

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