检索结果-内蒙古大学图书馆

Joint Access Points-User Association and Caching Placement Strategy for Cell-Free Massive MIMO Systems Based on Soft Actor-Critic Algorithm

引用

IEEE COMMUNICATIONS LETTERS 2024年第2期28卷 347-351页

作者： Wang, Rui Shen, Min He, Yun Liu, Xiangyan Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Yuxi Normal Univ Sch Phys & Elect Engn Yuxi 653100 Yunnan Peoples R China

The joint optimization of APs-user association (AUA) and caching placement (CP) presents a promising method to mitigate the fronthaul overhead and reduce user-perceived latency in cell-free (CF) massive MIMO systems. However, previous studies have not considered the impact of deploying multiple access points (APs) and the user-centric nature of CF massive MIMO systems on joint schemes, while also lacking flexibility in managing caching and association resources. In this letter, we initially develop a latency model based on the collaborative caching and user-centric characteristic. We then formulate a long-term joint optimization problem for AUA and CP to minimize content delivery latency. A two-timescale soft actor-critic (SAC) deep reinforcement learning based algorithm is proposed to solve this NP-hard problem. To enhance the convergence of the SAC algorithm, we devise user clustering and cache partitioning schemes. Numerical results demonstrate that our proposed scheme exhibits reduced latency compared to the benchmark schemes, while efficiently utilizing fronthaul and caching resources.

关键词： Massive MIMO Collaboration Clustering algorithms Wireless communication partitioning algorithms Optimization Telecommunications APs-user association caching placement user clustering cache partitioning soft actor-critic algorithm

来源：评论

学校读者我要写书评

暂无评论

Hypergraph partitioning With Embeddings

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第6期34卷 2771-2782页

作者： Sybrandt, Justin Shaydulin, Ruslan Safro, Ilya Clemson Univ Sch Comp Clemson SC 29634 USA

Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein "hyperedges" may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively "coarsening" the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://***/2019/partition.

关键词： partitioning algorithms Bipartite graph Approximation algorithms Machine learning Message systems Interpolation Contracts

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning for Computation Rate Maximization in RIS-Enabled Mobile Edge Computing

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 10862-10866页

作者： Xu, Jianpeng Ai, Bo Wu, Lina Zhang, Yaoyuan Wang, Weirong Li, Huiya Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Peng Cheng Lab Res Ctr Networks & Commun Shenzhen 518055 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Beijing Informat Sci & Technol Univ Inst Appl Math Beijing 100192 Peoples R China

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the performance of mobile edge computing (MEC) network. Nevertheless, the RIS-enabled MEC network design is a non-trivial problem. This paper investigates the RIS-enabled MEC network, where the Internet of Things devices (IoTDs) with limited energy budgets can offload their partial computation tasks to the base station (BS). We first formulate a sum computation rate maximization problem by jointly designing the RIS phase shifts, and the IoTDs' energy partition strategies for local computing and offloading. Then, to handle the non-convex optimization problem, we propose a deep reinforcement learning (DRL)-based algorithm, in which the twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to optimize the RIS phase shifts and the IoTDs' energy partition strategies. Simulation results show that the proposed TD3 solution can reach a better sum computation rate than the benchmark algorithms.

关键词： Task analysis Servers Optimization Energy consumption Uplink partitioning algorithms Deep reinforcement learning Reconfigurable intelligent surface (RIS) mobile edge computing (MEC) deep reinforcement learning (DRL) computation rate maximization

来源：评论

学校读者我要写书评

暂无评论

Generalized Reduced-WMMSE Approach for Cell-Free Massive MIMO With Per-AP Power Constraints

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第10期13卷 2682-2686页

作者： Yoo, Wonsik Yu, Daesung Lee, Hoon Park, Seok-Hwan Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea Univ Surrey Inst Commun Syst Guildford GU2 7XH England Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan 44919 South Korea Ulsan Natl Inst Scienceand Technol Artificial Intelligence Grad Sch Ulsan 44919 South Korea

The optimization of cooperative beamforming vectors in cell-free massive MIMO (mMIMO) systems is presented where multi-antenna access points (APs) support downlink data transmission of multiple users. Albeit the successes of the weighted minimum mean squared error (WMMSE) algorithm and their variants, they lack careful investigations about computational complexity that scales with the number of antennas and APs. We propose a generalized and reduced WMMSE (G-R-WMMSE) approach whose complexity is significantly lower than conventional WMMSE. We partition the set of beamforming coefficients into subvectors, with each subvector corresponding to a specific AP. Such a partitioning approach decomposes the original WMMSE problem across individual APs. By leveraging the Lagrange duality analysis, a closed-form solution can be derived for each subproblem, which substantially reduces the computation burden. Additionally, we present a parallel execution of the proposed G-R-WMMSE with adaptive step sizes, aiming at further reducing the time complexity. Numerical results validate that the proposed G-R-WMMSE schemes achieve over 99% complexity savings compared to the conventional WMMSE scheme while maintaining almost the same performance.

关键词： Array signal processing Vectors Complexity theory Downlink partitioning algorithms Massive MIMO Interference Cell-free massive MIMO per-AP power constraints weighted MMSE parallel computation

来源：评论

学校读者我要写书评

暂无评论

Parallel Greedy algorithms for Steiner Forest

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第6期36卷 1311-1325页

作者： Ghalami, Laleh Grosu, Daniel Wayne State Univ Detroit MI 48202 USA

The Steiner Forest Problem is a fundamental combinatorial optimization problem in operations research and computer science. Given an undirected graph with non-negative weights for edges and a set of pairs of vertices called terminals, the Steiner Forest Problem is to find the minimum cost subgraph that connects each of the terminal pairs together. We design a family of parallel greedy algorithms based on a sequential heuristic greedy algorithm called Paired Greedy, which iteratively connects the terminal pairs that have the minimum distance. The family of parallel algorithms consists of a set of algorithms exhibiting various degrees of parallelism determined by the number of pairs that are connected in parallel in each iteration of the algorithms. We implement and run the algorithms on a multi-core system and perform an extensive experimental analysis. We analyzed the performance of the algorithms on a rich library of Steiner Forest instances with various underlying graph types. The results show that our proposed parallel algorithms achieve significant speedup with respect to the sequential Paired Greedy algorithm and provide solutions with costs that are very close to those of the solutions obtained by the sequential Paired Greedy algorithm. We provide recommendation on selecting the type of parallel algorithm and its parameters in order to achieve the most efficient results for each class of instances.

关键词： Forestry Greedy algorithms Costs Parallel algorithms Steiner trees Approximation algorithms Training partitioning algorithms Optimization Libraries Steiner forest parallel algorithms multi-core

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Intelligent Surfaces: How to Enhance Multiuser Channel Prediction?

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第2期13卷 446-450页

作者： Shehzad, M. Karam Assaad, Mohamad Rose, Luca Nokia Stand Radio Interface & Access Grp RIAG F-91300 Massy France Cent Supelec Lab Signals & Syst F-91192 Gif Sur Yvette France

Reconfigurable intelligent surface (RIS) is envisioned as the key enabler of a smart wireless environment, offering a nearly energy-neutral solution to control the dynamic propagation of electromagnetic signals. On the other hand, channel prediction can bring promising benefits in various aspects of wireless systems, e.g., reducing channel state information feedback overhead. Prediction of a highly dynamic channel is cumbersome. This letter focuses on enhancing the performance of multiuser channel prediction by introducing time correlation in the composite channel while exploiting quasi-RIS. We show that the intelligently configured RIS improves channel prediction performance while saving sufficient RIS elements for other applications and simplifies machine learning architecture.

关键词： Optimization Biological cells Wireless communication partitioning algorithms Sociology Prediction algorithms Correlation CSI prediction IRS machine learning reconfigurable intelligent surface time correlation

来源：评论

学校读者我要写书评

暂无评论

Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

引用

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2025年第1期6卷 438-448页

作者： Elnour, Ammar Yang, Wencheng Li, Yan Univ Southern Queensland Sch Math Phys & Comp Toowoomba Qld 4350 Australia

Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.

关键词： Uncertainty Clustering algorithms Machine learning Estimation Statistical learning Surveys Standards Noise measurement Testing partitioning algorithms Clustering validity testing randomness bootstrap likelihood significance testing statistical machine learning

来源：评论

学校读者我要写书评

暂无评论

Distributed Fusion of Labeled Multi-Bernoulli Filters Based on Arithmetic Average

引用

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 656-660页

作者： Wei, Jingxin Luo, Feng Qi, Jiawei Luo, Xianxian Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China

This letter considers the distributed fusion for Labeled Multi-Bernoulli (LMB) filters under the multi-sensor multi-target tracking scenario. In practice, a novel fusion method that combines the label-free strategy with the fusion method based on the Arithmetic Average (AA) is proposed. Firstly, the label-free version of the LMB posterior is obtained. Then, the corresponding Probability Hypothesis Density (PHD) is acquired and fused by the merging-based PHD-AA fusion method. Finally, the labels of the fused distributions are reassigned. The simulation shows that the distributed LMB filter using the proposed fusion method can achieve more accurate multi-target state estimates with relatively low computational burden under clutter and missed detection environment compared to other existing methods.

关键词： Radio frequency Merging Signal processing algorithms Target tracking partitioning algorithms Radar signal processing Mathematical models Arithmetic average fusion distributed multi-target tracking labeled multi-Bernoulli (LMB) filter random finite set

来源：评论

学校读者我要写书评

暂无评论

Fast Update Algorithm With Reorder Mechanism for SRAM-Based Longest Prefix Matching on FPGA

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 420-424页

作者： Song, Xiaoyong Guo, Zhichuan Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Longest prefix matching (LPM) is often used in network forwarding and it is usually implemented by ternary content addressable memory (TCAM) on field programmable gate array (FPGA). The traditional SRAM based TCAM only has one clock cycle in search delay, but it has a non-negligible update latency. Different from the general TCAM words, the ternary bits in LPM words are arranged continuously from low bits to high, and the prefix lengths of words have some distribution characteristics on actual network traffics. In this brief, a design is proposed to reduce the update latency of LPM by fast update algorithm with reorder mechanism. The key idea is reorder LPM prefix and partition don't care bits into each sub-prefix evenly in SRAM based LPM, so that the update latency in each sub-table would be minimized and the whole update latency would be reduced. The design of this brief is implemented on Xilinx Kintex-7 field-programmable gate array. Compared to the prior methods, the update latency of LPM is reduced by at least half or more in most cases with our method. As far as we know, this is the fastest update mechanism in SRAM based LPM, which consumes the least possible clock cycles.

关键词： Random access memory Field programmable gate arrays Clocks partitioning algorithms Engines Pattern matching Memory management Field programmable gate array (FPGA) SRAM-based TCAM longest prefix matching (LPM) update latency IP lookup

来源：评论

学校读者我要写书评

暂无评论

Accelerated Reeds-Shepp and Underspecified Reeds-Shepp algorithms for Mobile Robot Path Planning

引用

IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2691-2708页

作者： Ibrahim, Ibrahim Decre, Wilm Swevers, Jan Katholieke Univ Leuven Dept Mech Engn MECO Res Team B-3000 Leuven Belgium Katholieke Univ Leuven Flanders Make B-3000 Leuven Belgium

In this study, we present a simple and intuitive method for accelerating optimal Reeds-Shepp path computation. Our approach uses geometrical reasoning to analyze the behavior of optimal paths, resulting in a new partitioning of the state space and a further reduction in the minimal set of viable paths. We revisit and reimplement classic methodologies from literature, which lack contemporary open-source implementations, to serve as benchmarks for evaluating our method. In addition, we address the underspecified Reeds-Shepp planning problem where the final orientation is unspecified. We perform exhaustive experiments to validate our solutions. Compared to the modern C++ implementation of the original Reeds-Shepp solution in the Open Motion Planning Library, our method demonstrates a 15x speedup, while classic methods achieve a 5.79x speedup. Both approaches exhibit machine-precision differences in path lengths compared to the original solution. We release our proposed C++ implementations for both the accelerated and underspecified Reeds-Shepp problems as open-source code.

关键词： Robots Planning Path planning Optimal control Motion segmentation partitioning algorithms Turning Large Hadron Collider Kinematics Autonomous vehicles Computational geometry constrained motion planning motion and path planning nonholonomic motion planning

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：