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Fully Distributed Cell-Free MIMO Systems: Architecture, Algorithm, and Testbed Experiments

IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 7956-7973页

作者： Lee, Mi Hyun Yun, Chaewon Kim, Geun Ho Park, Soo Young Yu, Chang Wahn Choi, Kae Won Sungkyunkwan Univ Sch Informat & Commun Engn Dept Elect & Comp Engn Suwon 16419 South Korea Elect & Telecommun Res Inst Mobile Commun Res Div Daejeon 34129 South Korea

In this article, we propose a new fully distributed cell-free multiple-input and multiple-output (MIMO) architecture that enables independent deployment of access points (APs) without any infrastructure such as a central processing unit (CPU). In the proposed architecture, all baseband processing is distributively executed by homogeneous APs without any CPU, thanks to the fully distributed functional split structure. Since the proposed architecture only consists of APs, we expect that a 5G/6G-based private network with a cell-free MIMO capability can easily be built for Industrial Internet of Things (IIoT) applications without the help of mobile network operators (MNOs). We also propose a novel precoding algorithm for orthogonal frequency division multiplexing (OFDM)-based cell-free MIMO systems. In the proposed precoding algorithm, the APs in a user-centric cluster cooperatively maximize the capacity of the target user equipment (UE) while minimizing the interference to nearby UEs. The proposed precoding algorithm solves the OFDM power allocation problem to allocate power on a per-subcarrier basis under the per- AP power constraints, which has not been addressed in previous works. We have built a full-fledged and real-time cell-free MIMO testbed that implements the proposed cell-free MIMO architecture and precoding algorithm. The validity of the proposed architecture and precoding algorithm is verified by experiments and simulations.

关键词： MIMO communication Precoding Computer architecture Signal processing algorithms clustering algorithms Central Processing Unit OFDM Cell-free multiple-input and multiple-output (MIMO) functional split precoding private network

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Federated clustering for Electricity Consumption Pattern Extraction

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IEEE TRANSACTIONS ON SMART GRID 2022年第3期13卷 2425-2439页

作者： Wang, Yi Jia, Mengshuo Gao, Ning Von Krannichfeldt, Leandro Sun, Mingyang Hug, Gabriela Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Swiss Fed Inst Technol Power Syst Lab CH-8092 Zurich Switzerland Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.

关键词： Smart meters clustering algorithms Collaborative work Load modeling Data privacy Indexes Servers Federated clustering k-means smart meter data analytics pattern extraction data privacy

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Adaptive Contourlet Fusion clustering for SAR Image Change Detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 2295-2308页

作者： Zhang, Wenhua Jiao, Licheng Liu, Fang Yang, Shuyuan Liu, Jia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal synthetic aperture radar (SAR) images. A binary image indicating changed regions is generated by a novel fuzzy clustering algorithm from a Contourlet fused difference image. Contourlet fusion uses complementary information from different types of difference images. For unchanged regions, the details should be restrained while highlighted for changed regions. Different fusion rules are designed for low frequency band and high frequency directional bands of Contourlet coefficients. Then a fast non-local clustering algorithm (FNLC) is proposed to classify the fused image to generate changed and unchanged regions. In order to reduce the impact of noise while preserve details of changed regions, not only local but also non-local information are incorporated into the FNLC in a fuzzy way. Experiments on both small and large scale datasets demonstrate the state-of-the-art performance of the proposed method in real applications.

关键词： Speckle Radar polarimetry Wavelet transforms Neural networks clustering algorithms Synthetic aperture radar Image segmentation Synthetic aperture radar unsupervised change detection Contourlet fusion non-local clustering

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User clustering and Power Allocation for Massive MIMO With NOMA-Inspired Cognitive Radio

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第7期71卷 7656-7664页

作者： Shili, Maroua Hajjaj, Moufida Ammari, Mohamed Lassaad Univ Carthage Higher Sch Com municat Tunis InnovCOM Lab Ariana 2083 Tunisia

This paper proposes a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system based on cognitive radio (CR) networks. To reduce the users interference, we propose an efficient user clustering algorithm. Within a cluster, we consider the user with the weak channel gain as a primary user (PU) and the user with the strong channel gain as a secondary user (SU). Given the quality of service for both PUs and SUs, we propose a power allocation (PA) method that maximizes the sum rate. The PA optimization problem is discussed into three scenarios which depend on the available transmit power. Thereafter, we derive a robust solution for the PA issue under imperfect channel state information (CSI) assumptions. Simulation results demonstrate that our proposed NOMA-CR scheme can significantly improve the spectral efficiency (SE). In addition, the proposed robust PA scheme for the imperfect CSI case reduces the performance loss due to the channel uncertainty.

关键词： NOMA Radio frequency Quality of service Resource management Massive MIMO clustering algorithms Precoding NOMA cognitive radio massive MIMO power allocation user clustering

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A New Density Peak clustering Algorithm Based on Cluster Fusion Strategy

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IEEE ACCESS 2022年 10卷 98034-98047页

作者： Li, Fuxiang Zhou, Ming Li, Shu Yang, Tianhao Harbin Univ Sci & Technol Sch Sci Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Minist Educ Key Lab Engn Dielect & Applicat Harbin 150080 Peoples R China

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.

关键词： clustering algorithms Resource management Partitioning algorithms Classification algorithms Solid modeling Image segmentation Complexity theory Density measurement clustering density peaks candidate cluster center cluster fusion strategy

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Constrained Tensor Representation Learning for Multi-View Semi-Supervised Subspace clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 3920-3933页

作者： Tang, Yongqiang Xie, Yuan Zhang, Chenyang Zhang, Wensheng Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China

Multi-view subspace clustering is an effective method to partition data into their corresponding categories. Nevertheless, existing multi-view subspace clustering approaches generally operate in a purely unsupervised manner, while ignoring the valuable weakly supervised information that can be readily obtained in many practical applications. In this paper, we consider the weakly supervised form of sample pair constraints, and devote to promoting the performance of multi-view subspace clustering with the aid of such prior knowledge. To achieve this goal, inspired by the intrinsic block diagonal structure of ideal low-rank representation (LRR), we propose a novel regularization to integrate must-link, cannot-link and normalization constraints into a unified formulation. The proposed regularization can be regarded as a general description for sample pairwise constraints, and thus provides a flexible framework for multi-view semi-supervised subspace clustering task. Furthermore, we devise a contrained tensor representation learning (CTRL) model that takes advantage of our proposed regularization to facilitate the learning of the desired representation tensor. An efficient optimization algorithm based on alternating direction minimization strategy is carefully designed to solve the proposed CTRL model. Extensive experiments on eight challenging real-world datasets are conducted, and the results validate the effectiveness of our designed pairwise constraints regularization, as well as the superiority of the proposed CTRL model.

关键词： Representation learning Tensors Correlation clustering algorithms Minimization Task analysis Optimization Multi-view learning pairwise constraint semi-supervised clustering subspace learning tensor singular value decomposition (t-SVD)

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MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene Selection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第9期34卷 4371-4384页

作者： Cui, Laizhong Acharya, Sudipta Mishra, Sumit Pan, Yi Huang, Joshua Zhexue Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China IIIT Guwahati Dept Comp Sci & Engn Gauhati 781039 Assam India Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from the Curse of dimensionality. To overcome this problem, the dimensionality reduction through feature selection becomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a multi-objective optimization based multi-view Co-clustering algorithm (named MMCo-Clus). A popular evolutionary technique - Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo-Clus algorithm considering the constructed views to identify a set of "good" co-clustering solutions. Finally, based on a concept of consensus operation on the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed me

关键词： Feature extraction Germanium clustering algorithms Biology Optimization Data analysis Genetic algorithms Feature selection co-clustering multi-view learning multi-objective optimization sample classification

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Graph Representation Learning via Contrasting Cluster Assignments

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第3期16卷 912-922页

作者： Zhang, Chun-Yang Yao, Hong-Yu Chen, C. L. Philip Lin, Yue-Na Fuzhou Univ Coll Comp & Data Sci Fuzhou 350002 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

With the rise of contrastive learning, unsupervised graph representation learning (GRL) has shown strong competitiveness. However, existing graph contrastive models typically either focus on the local view of graphs or take simple considerations of both global and local views. This may cause these models to overemphasize the importance of individual nodes and their ego networks, or to result in poor learning of global knowledge and affect the learning of local views. Additionally, most GRL models pay attention to topological proximity, assuming that nodes that are closer in graph topology are more similar. However, in the real world, close nodes may be dissimilar, which makes the learned embeddings incorporate inappropriate messages and thus lack discrimination. To address these issues, we propose a novel unsupervised GRL model by contrasting cluster assignments, called graph representation learning model via contrasting cluster assignment (GRCCA). To comprehensively explore the global and local views, it combines multiview contrastive learning and clustering algorithms with an opposite augmentation strategy. It leverages clustering algorithms to capture fine-grained global information and explore potential relevance between nodes in different augmented perspectives while preserving high-quality global and local information through contrast between nodes and prototypes. The opposite augmentation strategy further enhances the contrast of both views, allowing the model to excavate more invariant features. Experimental results show that GRCCA has strong competitiveness compared to state-of-the-art models in different graph analysis tasks.

关键词： Prototypes clustering algorithms Representation learning Unsupervised learning Task analysis Topology Social networking (online) Contrastive learning graph data mining graph representation learning (GRL) unsupervised learning

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Multimodal Moore-Penrose Inverse-Based Recomputation Framework for Big Data Analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 6570-6582页

作者： Zhang, Wandong Yang, Yimin Wu, Q. M. Jonathan Wang, Tianlei Zhang, Hui Univ Western Ontario Dept Elect & Comp Engn London ON N6A 3K7 Canada Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Vector Inst Artificial Intelligence Toronto ON M5G 1M1 Canada Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310005 Peoples R China Hunan Univ Sch Robot Changsha 410082 Peoples R China

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://***/W1AE/Retraining.

关键词： Representation learning Training Encoding Nonhomogeneous media Feature extraction clustering algorithms Unsupervised learning Moore-Penrose inverse (MPI) multilayer fully connected neural network multimodal encoding representation learning

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Confidence-Based Simple Graph Convolutional Networks for Face clustering

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IEEE ACCESS 2022年 10卷 6459-6469页

作者： Sun, Dengdi Yang, Kang Ding, Zhuanlian Anhui Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Comp & Signal Proc ICSP Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

Face clustering is an effective method for taking advantage of unlabeled face data. Recent studies use graph convolutional networks (GCNs) to learn feature embeddings from the neighborhood information between face images. However, most of the face clustering methods require numerous overlapping subgraphs to characterize the local structure around the nodes, which causes significant redundancy. Moreover, the nonlinearity of the GCN itself increases the calculation complexity, which further reduces the model's training efficiency. In this study, we propose a lightweight clustering framework, the confidence-based simple graph convolutional network (CSGCN), for face clustering, which achieves more accurate clustering results and significantly improves the efficiency of GCN-based face clustering. Specifically, CSGCN does not construct any subgraphs but convolves the entire graph as a whole and also removes the nonlinearity of the convolution in the graph convolution module, which further reduces the computational complexity. Subsequently, an effective new confidence score is constructed to better characterize the embedded features and to ensure that the subsequent clustering still maintains a high accuracy rate under the aforementioned model simplification. In addition, while most of the existing GCN-based methods are actually supervised, we construct an unsupervised confidence to make it more suitable for clustering tasks. Extensive experiments with MS-Celeb-1M, YouTube-Faces and DeepFashion datasets show that our method not only improves the clustering accuracy but also significantly reduces the execution time, whether in supervised or unsupervised models.

关键词： Faces Task analysis Convolution clustering algorithms Computational modeling clustering methods Training Face clustering confidence score simple graph convolutional networks

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