检索结果-内蒙古大学图书馆

Deep-Unfolding Beamforming for Intelligent Reflecting Surface Assisted Full-Duplex Systems

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第7期21卷 4784-4800页

作者： Liu, Yanzhen Hu, Qiyu Cai, Yunlong Yu, Guanding Li, Geoffrey Ye Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.

关键词： Array signal processing approximation algorithms Artificial neural networks Wireless communication Optimization Full-duplex system Computational complexity Intelligent reflecting surface full-duplex deep-unfolding neural network mixed-timescale beamforming machine learning

来源：评论

学校读者我要写书评

暂无评论

Towards Revenue-Driven Multi-User Online Task Offloading in Edge Computing

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第5期33卷 1185-1198页

作者： Ma, Zhi Zhang, Sheng Chen, Zhiqi Han, Tao Qian, Zhuzhong Xiao, Mingjun Chen, Ning Wu, Jie Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Univ New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Univ Sci & Technol China Suzhou Inst Adv Study Sch Comp Sci & Technol Hefei 230052 Anhui Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of 2(1+xi)ln(d+1) with a considerable probability, where d is the maximum number of process slots of an edge node and xi is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms.

关键词： Task analysis Cloud computing approximation algorithms Wireless communication Software Resource management Optimization Mobile edge computing primal-dual technique online computation offloading revenue-optimal

来源：评论

学校读者我要写书评

暂无评论

Image Reconstruction of Targets Undergoing Complex Motion Based on Optical Phased Array Compressed-Sensing Ghost Imaging Technology

引用

JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第24期40卷 7746-7756页

作者： Wang, Zihao Sun, Yanling Liao, Jiali Cao, Rui Wang, Chen Jin, Li Feng, Junbo Cao, Changqing Xidian Univ Sch Phys & Optoelect Engn Xian 710126 Peoples R China United Microelect Ctr CUMEC Xiyong Microelect Ind Pk Chongqing 400000 Peoples R China

Existing optical phased array (OPA) dynamic imaging techniques can only be used to perform image reconstruction of targets undergoing simple motion. This article proposes an imaging technique for targets undergoing complex motion based on recent research in the OPA-LiDAR field. To this end, the proposed technique combines two sub-algorithms: an OPA scanning algorithm and a compressed-sensing ghost imaging (CS-GI) technique. The reliability of the proposed technique is verified based on theoretical analysis, numerical calculations, and experimental validation. The first sub-algorithm is an innovative adaptive, single-target, motion state extraction algorithm, utilizing the inertia-free scanning characteristics of OPA. From this, the trajectory of the target can be reshaped accurately, and the target motion state can be calculated with an error of less than 3%. The second sub-algorithm is a CS-GI algorithm that requires a small number of samples to achieve a high-resolution OPA-based GI. The sampling ratio employed in the CS-GI algorithm corresponds to the root mean square error (RMSE) convergence of the reconstructed image is 1. The proposed technique for moving target imaging can be effectively used to perform motion trajectory reshaping and image reconstruction of targets undergoing fast and complex motion. We believe that this technique can be effectively used in the fields of vehicle-mounted LiDAR, unmanned systems, and medical imaging.

关键词： Image reconstruction Nanophotonics Phased arrays approximation algorithms Optical imaging Adaptive optics Compressed scanning ghost imaging imaging systems optical phased array silicon nanophononics

来源：评论

学校读者我要写书评

暂无评论

Symplectic algorithms for Stable Manifolds in Control Theory

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第6期67卷 3105-3111页

作者： Chen, Guoyuan Zhu, Gaosheng Zhejiang Univ Finance & Econ Sch Data Sci Hangzhou 310018 Peoples R China Harbin Inst Technol Sch Math Harbin 150001 Peoples R China

In this article, we propose a symplectic algorithm for the stable manifolds of the Hamilton-Jacobi equations combined with an iterative procedure in [N. Sakamoto and A. J. van der Schaft, "Analytical approximation methods for the stabilizing solution of the Hamilton-Jacobi equation," IEEE Trans. Autom. Control, 2008]. Our algorithm includes two key aspects. The first one is to prove a precise estimate for radius of convergence and the errors of local approximate stable manifolds. The second one is to extend the local approximate stable manifolds to larger ones by symplectic algorithms, which have better long-time behaviors than general-purpose schemes. Our approach avoids the case of divergence of the iterative sequence of approximate stable manifolds and reduces the computation cost. We illustrate the effectiveness of the algorithm by an optimal control problem with exponential nonlinearity.

关键词： Manifolds approximation algorithms Convergence Optimal control Iterative algorithms Feedback control Eigenvalues and eigenfunctions Hamiltonian systems Hamilton-Jacobi (HJ) equations optimal control stable manifolds symplectic algorithms

来源：评论

学校读者我要写书评

暂无评论

Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: From Time-Driven to Event-Driven

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 4139-4144页

作者： Zhao, Qingtao Si, Jennie Sun, Jian Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85287 USA

In this work, time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate dynamic programming (ADP) and reinforcement learning (RL) algorithms, the direct heuristic dynamic programming (dHDP) has been shown an effective tool as demonstrated in solving several complex learning control problems. It continuously updates the control policy and the critic as system states continuously evolve. It is therefore desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. Toward this goal, we propose a new event-driven dHDP. By constructing a Lyapunov function candidate, we prove the uniformly ultimately boundedness (UUB) of the system states and the weights in the critic and the control policy networks. Consequently, we show the approximate control and cost-to-go function approaching Bellman optimality within a finite bound. We also illustrate how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP.

关键词： Neural networks Task analysis Convergence Stability criteria Heuristic algorithms approximation algorithms Learning systems Direct heuristic dynamic programming (dHDP) event-driven time-driven dHDP reinforcement learning (RL)

来源：评论

学校读者我要写书评

暂无评论

Optimal Weighted Load Balancing in TCAMs

引用

IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第3期30卷 985-998页

作者： Sadeh, Yaniv Rottenstreich, Ori Kaplan, Haim Tel Aviv Univ Dept Comp Sci IL-69978 Tel Aviv Israel Technion Israel Inst Technol IL-3200003 Haifa Israel

Traffic splitting is a required functionality in networks, for example for load balancing over multiple paths or among different servers. The capacities of the servers determine the partition by which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Previous work showed how to compute the smallest prefix-matching TCAM necessary to implement a given partition exactly. In this paper we solve the more practical case, where at most n prefix-matching TCAM rules are available, restricting the ability to implement exactly the desired partition. We give simple and efficient algorithms to find n rules that generate a partition closest in $L_{infinity}$ to the desired one. We do the same for a one-sided version of $L_{infinity}$ which equals to the maximum overload on a server and for a relative version of it. We use our algorithms to evaluate how the expected error changes as a function of the number of rules, the number of servers, and the width of the TCAM.

关键词： Servers Partitioning algorithms approximation algorithms Task analysis Complexity theory Load management Memory management Computer network management packet switching data structures algorithms

来源：评论

学校读者我要写书评

暂无评论

An Evaluation-Focused Framework for Visualization Recommendation algorithms

引用

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 346-356页

作者： Zeng, Zehua Moh, Phoebe Du, Fan Hoffswell, Jane Lee, Tak Yeon Malik, Sana Koh, Eunyee Battle, Leilani Univ Maryland College Pk MD 20742 USA Adobe Res San Jose CA USA Korea Adv Inst Sci & Technol Seoul South Korea Univ Washington Seattle WA 98195 USA

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.

关键词： Data visualization Visualization Machine learning algorithms approximation algorithms Task analysis Encoding Clustering algorithms Visualization Tools Visualization Recommendation algorithms

来源：评论

学校读者我要写书评

暂无评论

Triple-D: Denoising Distant Supervision for High-Quality Data Creation 40

Triple-D: Denoising Distant Supervision for High-Quality Dat...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhu, Xinyi Zhang, Yongqi Chen, Lei Chen, Kai Guangzhou China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Distant supervision is a technique that aims to create large amounts of training data at a low cost. This approach benefits various downstream systems, particularly in natural language processing and relation extraction tasks. However, due to its strong assumption that any sentence containing entities expresses the specific relation between them found in existing knowledge bases (KBs), distant supervision introduces considerable noise. Existing works attempt to denoise distant supervision data by either using the original text or replacing entities in the text with patterns representing the entity types as inputs. However, replacing a frequently repeating pattern will result in loss of context due to the excessively general semantics of the pattern. Furthermore, due to the lack of ground truth, denoising module often relies on parametric models that still learn distribution from noisy data, which further limits model performance. In this paper, we propose Triple-d, a technique for high-quality data creation through adaptive pattern replacement and a scalable non-parametric model. Specifically, we formulate the adaptive pattern replacement task as a maximum-profit bipartite graph problem and propose an approximation algorithm as a solution. Additionally, we design a non-parametric model with scalable instance normalization to efficiently estimate and eliminate the influence of each dimension in neighbors. Extensive experiments in the denoising task and a downstream relation extraction task on real-world datasets demonstrate the superior effectiveness and efficiency of Triple-d, highlighting its potential to improve the performance for high-quality data creation. © 2024 IEEE.

关键词： approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

A Formal Characterization of Black-Box System Safety Performance With Scenario Sampling

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第1期7卷 199-206页

作者： Weng, Bowen Capito, Linda Ozguner, Umit Redmill, Keith Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this letter, we first present a novel safety evaluation criterion that seeks to characterize the actual operational domain within which the test subject would remain safe indefinitely with high probability. By formulating the black-box testing scenario as a dynamic system, we show that the presented problem is equivalent to finding a certain almost robustly forward invariant set for the given system. Second, for an arbitrary scenario testing strategy, we propose a scenario sampling algorithm that is provably asymptotically optimal in obtaining the safe invariant set with arbitrarily high accuracy. Moreover, as one considers different testing strategies (e.g., biased sampling of safety-critical cases), we show that the proposed algorithm still converges to the unbiased approximation of the safety characterization outcome if the scenario testing satisfies a certain condition. Finally, the effectiveness of the presented scenario sampling algorithms and various theoretical properties are demonstrated in a case study of the safety evaluation of a control barrier function-based mobile robot collision avoidance system.

关键词： Safety Uncertainty System testing Monte Carlo methods Dynamics approximation algorithms Unmanned aerial vehicles Robot safety probability and statistical methods black-box system scenario sampling invariant set

来源：评论

学校读者我要写书评

暂无评论

Twin-Width Meets Feedback Edges and Vertex Integrity 19

Twin-Width Meets Feedback Edges and Vertex Integrity

引用

19th International Symposium on Parameterized and Exact Computation, IPEC 2024

作者： Balabán, Jakub Ganian, Robert Rocton, Mathis Faculty of Informatics Masaryk University Brno Czech Republic Algorithms and Complexity Group TU Wien Vienna Austria

ISBN: (纸本)9783959773539

The approximate computation of twin-width has attracted significant attention already since the moment the parameter was introduced. A recently proposed approach (STACS 2024) towards obtaining a better understanding of this question is to consider the approximability of twin-width via fixed-parameter algorithms whose running time depends not on twin-width itself, but rather on parameters which impose stronger restrictions on the input graph. The first step that article made in this direction is to establish the fixed-parameter approximability of twin-width (with an additive error of 1) when the runtime parameter is the feedback edge number. Here, we make several new steps in this research direction and obtain: An asymptotically tight bound between twin-width and the feedback edge number;A significantly improved fixed-parameter approximation algorithm for twin-width under the same runtime parameter (i.e., the feedback edge number) which circumvents many of the technicalities of the original result and simultaneously avoids its formerly non-elementary runtime dependency;An entirely new fixed-parameter approximation algorithm for twin-width when the runtime parameter is the vertex integrity of the graph. © Jakub Balabán, Robert Ganian, and Mathis Rocton.

关键词： approximation algorithms

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：