检索结果-内蒙古大学图书馆

AWB⁺-Tree: A Novel Width-Based Index Structure Supporting Hybrid Matching for Large-Scale Content-Based Pub/Sub Systems

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第6期36卷 1268-1281页

作者： Liao, Zhengyu Qian, Shiyou Zheng, Zhonglong Cao, Jian Xue, Guangtao Li, Minglu Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321017 Zhejiang Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Event matching is a key component in a large-scale content-based publish/subscribe system. The performance of most existing algorithms is easily affected by the subscription matching probability. In this article, we propose a new data structure, named AWB(+)-Tree, which is based on the width of the predicates, to efficiently index the subscriptions. The most notable feature of AWB(+)-Tree is its ability to combine the advantages of different matching methods, thus achieving high and robust performance in dynamic environments. First, we implement both a forward matching method (AFM) and a backward matching method (ABM) based on AWB(+)-Tree. Then, we introduce a hybrid matching method (AHM) that combines AFM and ABM. Moreover, we extend AWB(+)-Tree in three aspects: approximate matching, string type matching, and fine-grained parallelization. We conducted extensive experiments to evaluate the performance of the proposed matching algorithms on synthetic and real-world datasets. The experiment results reveal that AHM achieves a reduction in matching time by up to 53.8% compared to the state-of-the-art method. Additionally, AHM exhibits improved performance robustness, with up to a 76.9% reduction in terms of the standard deviation of matching time. Particularly in dynamic scenarios, AHM is at least 2.3 times faster and 41.3% more stable than its counterparts. Furthermore, by implementing parallelization, the matching speed of 8 threads can be accelerated by 4.16 times compared to the single-thread matching speed.

关键词： Data structures Heuristic algorithms Classification algorithms approximation algorithms Training Standards Semantics Robustness Reviews Real-time systems Pub/sub system event matching performance robustness parallelization

来源：评论

学校读者我要写书评

暂无评论

Robust Transmission Scheduling Mechanism for Millimeter Wave Train-to-Train System With Priority Weighting

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6035-6047页

作者： Wei, Yao Ma, Yunhan Niu, Yong Han, Zhu Zhao, Xu Lu, Bin Dong, Mingyang Guan, Ke Ao, Shaoyou Res Inst China Telecom Co Ltd Guangzhou 510630 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea Beijing Jiaotong Univ Beijing Engn Res Ctr High sSpeed Railway Broadband Beijing 100044 Peoples R China

Currently, millimeter wave technology is used to carry a large number of 5G-R wireless services, such as train control information and passenger demand. This will make the train-to-train (T2T) communication system face the challenge of higher data capacity. In this article, we consider the communication between two trains running forward and backward on the same track. However, the 5G-R network has corresponding priority management according to the business application attributes, and different data flows between trains have different scheduling priorities. Therefore, our aim is to maximize the number of completed flows in the system with scheduling priority weighting. We deployed mobile relays on the roof of the train to provide relay assistance for the T2T communication link, and establish a mixed integer nonlinear programming optimization problem with a priority weighted system to maximize the number of flows. In order to reduce the computational complexity of the system, we propose an efficient and robust transmission scheduling optimization algorithm based on graph theory. This optimization algorithm consists of a relay node selection algorithm and a transmission scheduling algorithm. Finally, as shown in system simulation, the proposed algorithm can approximate system performance while reducing system computational complexity. In comparison with the baseline scheme in the literature, the proposed algorithm has significant performance improvement.

关键词： Millimeter wave communication Communication systems Relays Rail transportation Millimeter wave technology Reliability Optimization approximation algorithms Dynamic scheduling Doppler effect High-speed railway millimeter wave communication transmission scheduling

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Control of Second-Order Multiagent Systems With Input Delay via Data-Driven Forward Reward Q -Learning Framework

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 1858-1869页

作者： Rao, Kai Yan, Huaicheng Liu, Qiwei Dang, Qingmei Shi, Kaibo East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Weifang Univ Coll Machinery & Automat Weifang 261061 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China

In this article, an optimal tracking control algorithm is derived for second-order discrete-time multiagent systems (MASs) with unknown system dynamics and input delay. First, the optimal tracking problem of MASs with input delay is constructed by the tracking error and a local performance index function. By designing a new variable, the original model is converted into a model without delay while guaranteeing the equivalence of performance index and control law of each agent. Subsequently, the transformed model and reinforcement learning (RL) theory are integrated to obtain a novel data-driven distributed learning framework. This framework enables online learning of the optimal control law and ensures tracking consensus of all followers' position and velocity states. Compared to the traditional actor-critic framework, an additional neural network (NN) is utilized to approximate the forward reward information (FRI) to improve the information learning capability of the MASs. Furthermore, the convergence analysis of system states and three NNs structures are conducted by Lyapunov theory. Finally, the proposed framework is verified to have better convergence and require fewer iteration steps than classical actor-critic framework by numerical simulation comparison experiments.

关键词： Delays Heuristic algorithms Performance analysis Convergence Q-learning Optimal control Artificial neural networks approximation algorithms Delay effects System dynamics Data-driven input delays optimal tracking control reinforcement learning (RL) second-order multiagent systems (MASs)

来源：评论

学校读者我要写书评

暂无评论

Exploring Fairness for FAS-Assisted Communication Systems: From NOMA to OMA

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3433-3449页

作者： Yao, Junteng Zhou, Liaoshi Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China UCL Dept Elect & Elect Engn London WC1E 6BT England Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Yonsei Univ Sch Integrated Technol Seoul 03722 South Korea

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Besides, we address a more general scenario involving interference and channel estimation overheads, deriving exact users' outage probabilities and employing a combination of bisection, one-dimensional (1D) search, and SCA algorithms to efficiently and effectively solve max-min optimization problems in both NOMA and OMA systems, significantly enhancing system fairness and computational efficiency. Our numerical results demonstrate that the proposed schemes significantly enhance outage performance over conventional OMA and NOMA benchmarks, even in the presence of interference, confirming their effectiveness in realistic scenarios. The performance of our closed-form and SCA algorithm-based solutions in FAS-assisted NOMA and OMA systems closely approaches that of the optimal solutions, further validated by the effective approximation of users' outage probabilities in simulations.

关键词： NOMA Probability Power system reliability Antennas Resource management Fluids Quality of service Optimization Closed-form solutions approximation algorithms Fairness fluid antenna system non-orthogonal multiple access orthogonal multiple access

来源：评论

学校读者我要写书评

暂无评论

A Distributed Symmetric Game Optimization to 3-Path Vertex Cover of Networks

引用

IEEE TRANSACTIONS ON NETWORKING 2025年第2期33卷 913-928页

作者： Chen, Jie Ding, Yong Zhou, Rongpei Qiu, Zhifeng Gui, Weihua Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330036 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

As a typical combinatorial optimization problem, the 3-path vertex cover problem has wide applications in practice. To solve the 3-path vertex cover problem from the perspective of distributed optimization, we treat each vertex as an agent (i.e., player) with computation, and decision-making capabilities. First, we establish a 3-player symmetric game model to describe the 3-path vertex cover problem, and design the corresponding cost function for each player. Then, we prove that under the established game model, strict Nash equilibriums (SNEs) act as the basis of the connection between 3-path vertex cover states and minimum 3-path vertex cover states. Next, we propose a novel memory-based synchronous learning (MSL) algorithm, where the initial profile strategy generation of players relies on the designed degree preference rule, and each player has a memory length for recording strategies and independently update their strategies concurrently based on the accessed local information. After that, we prove that our proposed MSL algorithm can guarantee that any strategy profile converges to an SNE, and provide a theoretical analysis of the algorithm's complexity. Finally, we present numerous numerical simulations to demonstrate the performance of our proposed algorithm on various networks. Moreover, we find that increasing the memory length and adopting the degree preference initialization can yield a better SNE.

关键词： Games Optimization approximation algorithms Nash equilibrium Monitoring Symmetric matrices NP-hard problem Wireless sensor networks Training Standards 3-path vertex cover symmetric game distributed optimization strict Nash equilibrium algorithm complexity

来源：评论

学校读者我要写书评

暂无评论

Distributed Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework

引用

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2025年 11卷 1-10页

作者： Zhang, Jiayi Wei, Guoliang Ding, Derui Ju, Yamei Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

In this paper, the distributed sequential state estimation problem is addressed for a class of discrete time-varying systems with inaccurate process noise covariance over binary sensor networks. First, with the purpose of reducing communication costs, a special class of sensors called binary sensors, which output only one bit of data, is adopted. The Gaussian tail function is then used to describe the likelihood of the binary measurements. Subsequently, the process noise covariance matrix is modeled as a inverse Wishart distribution. By employing a variational Bayesian approach combined with diffusion filtering strategies, the parameters (i.e., mean and variance) of the prior and posterior probability density functions are formalized for the sequential estimator and the sequential predictor. Then, the fixed-point iteration is utilized to receive the approximate optimal distributions of both system states and estimated covariance matrices. Finally, a simulation example of target tracking demonstrates that our algorithm performs effectively using binary measurement outputs.

关键词： Target tracking Noise Tail approximation algorithms Vectors Bayes methods State-space methods State estimation Covariance matrices Time-varying systems Binary sensors sequential state estimation variational Bayesian approaches inverse Wishart distribution diffusion strategies

来源：评论

学校读者我要写书评

暂无评论

Integrated Scheduling of Batch Production and Intermediate Delivery with Batch-Position-Based Learning Effect

引用

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2025年

作者： Cheng, Bayi Wang, Yuqi Zhou, Mi Zhu, Xiaoxi Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China

In this paper, we consider a three-stage integrated scheduling problem with learning effect motivated by the applications in semiconductor manufacturing. In the first stage, the jobs are assigned into batches to process on a batching machine, where the processing times are affected by the learning effect. In the second stage, the processed jobs are delivered by a single transporter for further processing. In the third stage, the jobs are individually processed on a single machine. Our objective is to minimize the makespan. We first propose an optimal algorithm with time complexity of O(nlog n) for the case where jobs have identical sizes. Second, for the case where jobs have identical processing time on batch machine, we propose an approximation algorithm. The absolute and asymptotic worst-case ratios are (5)/(3) and (11)/(9), respectively. Finally, for the general case where jobs have arbitrary sizes and processing times, an approximation algorithm with absolute worst-case ratio of (7)/3 and asymptotic worst-case ratio of 2 is proposed.

关键词： Batch processing batch delivery learning effects approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

3D UAV Trajectory Planning With Obstacle Avoidance for UAV-Enabled Time-Constrained Data Collection Systems

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1460-1474页

作者： Zheng, Jun Liu, Kai Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper investigates the three-dimensional (3D) unmanned aerial vehicle (UAV) trajectory-planning problem in a UAV-enabled data collection (DC) system, in which a UAV executes a mission to collect time-constrained data in a geographical area with spatial obstacles. For such a UAV trajectory-planning problem, how to timely collect data, avoid collisions with spatial obstacles, and complete DC with limited UAV onboard energy is a big challenge. To address this challenge, the investigated problem is formulated as an optimization problem aiming to minimize the UAV's total mission completion time subject to the time constraint of collected data, the avoidance of spatial obstacles, and the energy constraint of the UAV. To solve the formulated problem, the problem is decomposed into a visiting sequence optimization sub-problem, and a hovering position and flying path optimization sub-problem. Correspondingly, a genetic algorithm (GA) based algorithm, and a successive convex approximation (SCA) based algorithm are proposed to solve the sub-problems, respectively. Based on the two algorithms, a 3D trajectory optimization algorithm is further proposed to solve the main problem. Simulation results show that the UAV's trajectory planned with the proposed algorithm can effectively reduce the UAV's total mission completion time.

关键词： Autonomous aerial vehicles Three-dimensional displays Trajectory Sensors approximation algorithms Planning Optimization UAV data collection obstacle avoidance 3D trajectory planning

来源：评论

学校读者我要写书评

暂无评论

Online IoT Indoor Localization Evolution: Enhanced Sparse Random Fourier Features With Multikernel Strategy

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10070-10086页

作者： Du, Hongkun Li, Xifeng Bi, Dongjie Peng, Libiao Xie, Yongle Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

Indoor positioning within the Internet of Things (IoT) has become critically important, with accuracy and robustness being essential for its practical applications. However, the complexity of real-world disturbances, including both Gaussian and non-Gaussian noise, poses significant challenges to existing methods. Kernel adaptive filtering (KAF) has proven effective in addressing these challenges, yet traditional KAF approaches face issues with growing structural complexity and increasing memory demands. To address these drawbacks, this study introduces an enhanced sparsification technique within the random Fourier features (RFFs) framework. The improvement lies in adopting the generalized Gaussian distribution (GGD) to be better suited for real-world noise, leading to the development of the ESCGKAF algorithm-a robust solution for high-precision indoor positioning in online scenarios. The algorithm leverages the half-quadratic (HQ) optimization on the kernel risk-sensitive loss (KRSL) cost function, which is further refined through the conjugate gradient (CG) method, to achieve a superior performance in noisy environments. The proposed approach permits a more generalized representation of random features, surpassing traditional Gaussian-based RFF methods in terms of adaptability and robustness. To further enhance performance in complex scenarios, MESCGKAF algorithm is proposed by introducing a multikernel strategy. The effectiveness of the proposed algorithms is validated in two real-world scenarios, showing marked performance improvement.

关键词： Kernel approximation algorithms Accuracy Cost function Internet of Things Prediction algorithms Noise Complexity theory Fingerprint recognition Adaptive filters Generalized Gaussian distribution (GGD) indoor positioning Internet of Things (IoT) kernel adaptive filter (KAF) multikernel adaptive filter (MKAF) random fourier features (RFFs)

来源：评论

学校读者我要写书评

暂无评论

A General Constrained Adaptive Filtering Algorithm for SISO Channel Estimation and Beamforming

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2770-2782页

作者： Li, Yingsong Sun, Dazhi Li, Liping Zakharov, Yuriy Miao, Yongchun Huang, Zhixiang Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230093 Peoples R China Univ York Sch Phys Engn & Technol York YO10 5DD England

In wireless communications and vehicle communications, it is useful to use adaptive filtering techniques for channel estimation, beamforming and echo cancellation. In this paper, we propose a general constrained adaptive filtering (GCAF) algorithm for single channel estimation and beamforming, which is obtained by integrating a general and adaptive loss function into the constrained adaptive filtering (CAF) framework. By selecting the parameter in the GCAF, it can approximate to several popular CAF algorithms. Then, the convergence, stability boundary and the stability analysis of the mean squared-deviation have been analyzed and presented in detail. Additionally, The complexity of the GCAF is presented and compared with the existing algorithms. The proposed GCAF is used for single-input and single-output (SISO) channel estimation and beamforming under different noises, and the tracking performance of the GCAF is also analyzed. The simulation results demonstrate that the GCAF algorithm outperforms the typically adaptive filtering algorithms and can effectively approxiamte the similar algorithms under heavy-tailed noises, which makes the proposed GCAF more robust and general.

关键词： approximation algorithms Channel estimation Signal processing algorithms Array signal processing Vectors Convergence Complexity theory Adaptive filtering beamforming channel estimation general constrained adaptive filtering algorithm

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：