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Hybrid Generalized Approximate Message Passing for Active User Detection and Channel Estimation With Correlated Group-Heterogeneous Activity

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第7期72卷 3919-3933页

作者： Chetot, Lelio Egan, Malcolm Gorce, Jean-Marie Univ Lyon Inria INSA Lyon CITIEA3720 F-69621 Villeurbanne France

The random access procedure is a bottleneck to the development of wireless networks supporting the use cases of massive machine-type communication and ultra reliable and low-latency communication. Such networks are densely and massively popupated and must meet stringent latency and reliability requirements. Due to these characteristics, grant-free random access is envisioned to alleviate the control overhead generated by the classical random access procedure. However, active user detection and channel estimation algorithms are required. Existing algorithms assume that the activity of each device is homogeneous and independent, which is not the case in many applications (e.g., due to sensors observing a common phenomenon). In order to address this problem, we introduce a new flexible model taking into account a group-heterogeneous activity, using the framework of copula theory. It is then leveraged by a hybrid generalized approximate message passing algorithm to solve the active user detection and channel estimation problem. Our numerical results show that the user detection and channel estimation are both improved with this new algorithm w.r.t. stateof- the-art Bayesian algorithms, with gains up to 10 times fewer detection errors and 10 dB less channel estimation error.

关键词： approximation algorithms Correlation Channel estimation Sensors Message passing Vectors Compressed sensing Active user detection channel estimation Bayesian compressed sensing approximate message passing correlated activity Internet of Things

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On λ-backbone coloring of cliques with tree backbones in linear time

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JOURNAL OF GRAPH THEORY 2024年第1期107卷 5-28页

作者： Michalik, Krzysztof Turowski, Krzysztof Jagiellonian Univ Theoret Comp Sci Dept PL-30348 Krakow Poland

A lambda-backbone coloring of a graph G with its subgraph (also called a backbone) H is a function c : V(G) -> {1, ..., k} ensuring that c is a proper coloring of G and for each {u, v} is an element of E(H) it holds that |c(u) - c(v)| >= lambda. In this paper we propose a way to color cliques with tree and forest backbones in linear time that the largest color does not exceed max{n , 2 lambda} + Delta(H)(2)[log n]. This result improves on the previously existing approximation algorithms as it is (Delta (H)(2)[log n])-absolutely approximate, that is, with an additive error over the optimum. We also present an infinite family of trees T with Delta(T) = 3 for which the coloring of cliques with backbones T requires at least max{n , 2 lambda} + Omega(log n) colors for lambda close to n/2. The construction draws on the theory of Fibonacci numbers, particularly on Zeckendorf representations.

关键词： approximation algorithms backbone coloring complete graphs Fibonacci numbers graph coloring

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K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11077-11090页

作者： Cheng, Dongdong Huang, Jinlong Zhang, Sulan Xia, Shuyin Wang, Guoyin Xie, Jiang Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing 408100 Peoples R China

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

关键词： Clustering algorithms Manifolds Kernel approximation algorithms Object recognition Resource management Partitioning algorithms Granular ball graph distance (GD) K-means natural density peaks (NDPs)

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Lightweight Distributed Gaussian Process Regression for Online Machine Learning

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第6期69卷 3928-3943页

作者： Yuan, Zhenyuan Zhu, Minghui Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

In this article, we study the problem where a group of agents aims to collaboratively learn a common static latent function through streaming data. We propose alight weight distributed Gaussian process regression (GPR)algorithm that is cognizant of agents' limited capabilities in communication, computation, and memory. Each agent independently runs agent-based GPR using local streaming data to predict test points of interest;then, the agents collaboratively execute distributed GPR to obtain global predictions over a common sparse set of test points;finally, each agent fuses results from distributed GPR with agent-based GPR to refine its predictions. By quantifying the transient and steady-state performances in predictive variance and error, we show that limited inter agent communication improves learning performances in the sense of Pareto. Monte Carlo simulation is conducted to evaluate the developed algorithm.

关键词： Gaussian processes Prediction algorithms Kernel Training data Training Servers approximation algorithms Distributed algorithms machine learning

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Reducing redundant information in search results employing approximation algorithms

Reducing redundant information in search results employing a...

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25th International Conference on Database and Expert Systems Applications, DEXA 2014

作者： Makris, Christos Plegas, Yannis Stamatiou, Yannis C. Stavropoulos, Elias C. Tsakalidis, Athanasios K. Computer Engineering and Informatics Department University of Patras Greece Business Administration Department University of Patras Greece Business Administration Department Technological Educational Institute of Patras Greece

ISBN: (纸本)9783319100845

It is widely accepted that there are many Web documents that contain identical or near-identical information. Modern search engines have developed duplicate detection algorithms to eliminate this problem in the search results, but difficulties still remain, mainly because the structure and the content of the results could not be changed. In this work we propose an effective methodology for removing redundant information from search results. Using previous methodologies, we extract from the search results a set of composite documents called SuperTexts and then, by applying novel approximation algorithms, we select the SuperTexts that better reduce the redundant information. The final results are next ranked according to their relevance to the initial query. We give some complexity results and experimentally evaluate the proposed algorithms. © 2014 Springer International Publishing Switzerland.

关键词： approximation algorithms

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Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

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IEEE ACCESS 2024年 12卷 56291-56308页

作者： Fang, Ning Cao, Qi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Longyan Tobacco Ind Co Ltd Longyan 364021 Peoples R China

This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm's performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed Algorithm, Multi-Verse Optimizer, Salp Swarm Algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic Algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

关键词： Vegetation Metaheuristics approximation algorithms Wind Heuristic algorithms Particle swarm optimization Mathematical models Metaheuristic algorithm optimization benchmark functions

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Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第9期46卷 6231-6246页

作者： Wu, Zhoutong Xiao, Mingqing Fang, Cong Lin, Zhouchen Peking Univ Acad Adv Interdisciplinary Studies Ctr Data Sci Beijing 100871 Peoples R China Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen AI Beijing 100871 Peoples R China Peking Univ Inst Artificial Intelligence Sch Intelligence Sci & Technol Natl Key Lab Gen AI Beijing 100871 Peoples R China Peng Cheng Lab Guangzhou 518066 Peoples R China

Universal approximation capability, also referred to as universality, is an important property of deep neural networks, endowing them with the potency to accurately represent the underlying target function in learning tasks. In practice, the architecture of deep neural networks largely influences the performance of the models. However, most existing methodologies for designing neural architectures, such as the heuristic manual design or neural architecture search, ignore the universal approximation property, thus losing a potential safeguard about the performance. In this paper, we propose a unified framework to design the architectures of deep neural networks with a universality guarantee based on first-order optimization algorithms, where the forward pass is interpreted as the updates of an optimization algorithm. The (explicit or implicit) network is designed by replacing each gradient term in the algorithm with a learnable module similar to a two-layer network or its derivatives. Specifically, we explore the realm of width-bounded neural networks, a common practical scenario, showcasing their universality. Moreover, adding operations of normalization, downsampling, and upsampling does not hurt the universality. To the best of our knowledge, this is the first work that width-bounded networks with universal approximation guarantee can be designed in a principled way. Our framework can inspire a variety of neural architectures including some renowned structures such as ResNet and DenseNet, as well as novel innovations. The experimental results on image classification problems demonstrate that the newly inspired networks are competitive and surpass the baselines of ResNet, DenseNet, as well as the advanced ConvNeXt and ViT, testifying to the effectiveness of our framework.

关键词： Optimization approximation algorithms Design methodology Manuals Reviews Knowledge engineering Image classification Neural network design universal approximation first-order optimization method skip connection width-bounded

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Risk-Based Dispatch of Power Systems Incorporating Spatiotemporal Correlation Based on the Robust Soft Actor-Critic Algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2478-2491页

作者： Feng, Jianbing Ren, Zhouyang Li, Wenyuan Chongqing Univ Natl Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

Based on safe deep reinforcement learning (SDRL), this paper presents a risk-based dispatch method that incorporates spatiotemporal correlation (SC-RD). In the SC-RD model, both the temporal correlation of violation risks and the spatial correlation of wind power uncertainties are considered. A novel robust soft actor-critic (R-SAC) algorithm based on SDRL is presented to efficiently solve the SC-RD model. This algorithm enables online decision-making in coping with the nonlinearity, nonconvexity, and integral form of the SC-RD model without any approximations and uncertain distribution assumptions. In the R-SAC, a robust constrained Markov decision process (R-CMDP) for the SC-RD is established to address the critical bottleneck of SDRL in handling constraints. In the R-CMDP, the violation risks are treated as the exploratory cost of the agent. The CVaR of the cost is used as a risk indicator for safe exploration in the feasible region of the SC-RD. A second-order central moment evaluation module is presented to efficiently estimate the CVaR. The accelerated primal-dual optimization approach is integrated into the R-SAC to efficiently drive the R-CMDP for maximum entropy adaptive learning. The effectiveness of the proposed model and solution method is validated using modified IEEE-39, IEEE-118 and South Carolina 500-bus test systems.

关键词： Correlation Costs Uncertainty Markov decision processes Wind power generation Deep reinforcement learning approximation algorithms Spatiotemporal phenomena Power systems Security Constrained Markov decision processes safe deep reinforcement learning (SDRL) risk-based dispatch spatiotemporal correlation

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A Recursive Soft-Input Soft-Output Decoding Algorithm

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第3期72卷 1290-1302页

作者： Karakchieva, Liudmila Trifonov, Peter ITMO Univ Dept Informat Technol & Programming St Petersburg 197101 Russia

A reduced complexity symbolwise maximum a posteriori probability (MAP) decoding algorithm for linear block codes is presented. This algorithm is based on the recursive trellises and performs two passes over the recursion tree. Probability-domain and Log-Max implementations are considered. Numeric results show that the proposed method has lower complexity compared to other known recursive algorithms and the classical BCJR algorithm. The complexity analysis for random codes is provided.

关键词： Complexity theory Maximum likelihood decoding Codes Iterative decoding approximation algorithms Symbols Generators SISO decoding recursive trellises optimal sectionalization Log-Max approximation

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Self-Organization of UAV Networks for Maximizing Minimum Throughput of Ground Users

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第8期73卷 11743-11755页

作者： Liu, Yaqun Xie, Jun Xing, Changyou Xie, Shengxu Luo, Xijian Army Engn Univ PLA Coll Command & Control Engn Nanjing 210007 Peoples R China

Unmanned aerial vehicles (UAVs) can act as aerial base stations (BSs) to provide communication service for ground users (GUs). Especially, many UAVs can form a UAV network in an ad hoc manner, so that they can provide communication service for GUs more effectively. When deploying a UAV network consisting of multiple UAV-BSs, the UAV network needs to cover as many GUs as possible and improve the communication quality of GUs while maintaining its own connectivity. To solve this problem, a UAV network construction algorithm based on virtual force is proposed, so that a connected UAV network can be deployed to cover as many GUs as possible. On this basis, the Air-to-Air connection establishment algorithm is proposed to establish connection relationships among UAV-BSs, and the Air-to-Ground connection establishment algorithm is proposed to determine the GUs served by each UAV-BS. The UAV-BS position adjustment algorithm is further proposed, so that each UAV adjusts its own position to improve the minimum throughput of covered GUs while maintaining the connectivity of the UAV network. The proposed algorithms can be executed in a distributed manner, i.e., the UAV network can be deployed under the constrains that the positions of GUs are prior unknown and there is no central controller. The simulation results show that the algorithms proposed in this paper are superior to other baselines.

关键词： Autonomous aerial vehicles approximation algorithms Throughput Heuristic algorithms Force Signal to noise ratio Atmospheric modeling Unmanned aerial vehicle (UAV) UAV network user coverage throughput optimization connectivity

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