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Leveraging Deep Neural Networks for Massive MIMO Data detection

IEEE WIRELESS COMMUNICATIONS 2023年第1期30卷 174-180页

作者： Nguyen, Ly V. Nguyen, Nhan T. Tran, Nghi H. Juntti, Markku Swindlehurst, A. Lee Nguyen, Duy H. N. San Diego State Univ San Diego CA 92182 USA Univ Oulu Oulu Finland Univ Akron Akron OH USA Univ Calif Irvine Irvine CA USA

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine article is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

关键词： Detectors Massive MIMO Symbols Signal processing algorithms detection algorithms Deep learning Antenna arrays

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A Fast Local Balanced Label Diffusion Algorithm for Community detection in Social Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第6期35卷 5472-5484页

作者： Roghani, Hamid Bouyer, Asgarali Azarbaijan Shahid Madani Univ Dept Informat Technol & Comp Engn Tabriz *** Iran

Community detection in large-scale networks is one of the main challenges in social networks analysis. Proposing a fast and accurate algorithm with low time complexity is vital for large-scale networks. In this paper, a fast community detection algorithm based on local balanced label diffusion (LBLD) is proposed. The LBLD algorithm starts with assigning node importance score to each node using a new local similarity measure. After that, top 5% important nodes are selected as initial rough cores to expand communities. In the first step, two neighbor nodes with highest similarity than others receive a same label. In the second step, based on the selected rough cores, the proposed algorithm diffuses labels in a balanced approach from both core and border nodes to expand communities. Next, a label selection step is performed to ensure that each node is surrendered by the most appropriate label. Finally, by utilizing a fast merge step, final communities are discovered. Besides, the proposed method not only has a fast convergence speed, but also provides stable and accurate results. Moreover, there is no randomness as well as adjustable parameter in the LBLD algorithm. Performed experiments on real-world and synthetic networks show the superiority of the LBLD method compared with examined algorithms.

关键词： Time complexity Social networking (online) detection algorithms Complexity theory Image edge detection Convergence Stability criteria Social networks local community detection Balanced label diffusion local similarity

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DeepTag: A General Framework for Fiducial Marker Design and detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第3期45卷 2931-2944页

作者： Zhang, Zhuming Hu, Yongtao Yu, Guoxing Dai, Jingwen Guangdong Virtual Real Technol Co Ltd X Lab Shenzhen 518000 Guangdong Peoples R China

A fiducial marker system usually consists of markers, a detection algorithm, and a coding system. The appearance of markers and the detection robustness are generally limited by the existing detection algorithms, which are hand-crafted with traditional low-level image processing techniques. Furthermore, a sophisticatedly designed coding system is required to overcome the shortcomings of both markers and detection algorithms. To improve the flexibility and robustness in various applications, we propose a general deep learning based framework, DeepTag, for fiducial marker design and detection. DeepTag not only supports detection of a wide variety of existing marker families, but also makes it possible to design new marker families with customized local patterns. Moreover, we propose an effective procedure to synthesize training data on the fly without manual annotations. Thus, DeepTag can easily adapt to existing and newly-designed marker families. To validate DeepTag and existing methods, beside existing datasets, we further collect a new large and challenging dataset where markers are placed in different view distances and angles. Experiments show that DeepTag well supports different marker families and greatly outperforms the existing methods in terms of both detection robustness and pose accuracy. Both code and dataset are available at https://***/research/publications/deep-tag/.

关键词： Symbols Image coding Convolutional neural networks Training data Robustness Pose estimation detection algorithms Fiducial marker deep learning object detection marker design monocular pose estimation

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Local Community detection Algorithm Based on Alternating Strategy of Strong Fusion and Weak Fusion

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 818-831页

作者： Shang, Ronghua Zhang, Weitong Zhang, Jingwen Jiao, Licheng Li, Yangyang Stolkin, Rustam Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

Existing fusion-based local community detection algorithms have achieved good results. However, when assigning a node to a community, similarity functions are sometimes used, which only use node information, while ignoring connection information within the community. These algorithms sometimes fail to find influential nodes, which eventually leads to the failure to find a complete local community. To address these problems, a new local community detection algorithm is proposed in this article. Two strategies, of strong fusion followed by weak fusion, are used alternately to fuse nodes. Compared with using two fusion strategies alone, the alternating loop method can improve the solution of the algorithm in each stage. In strong fusion, we propose a new membership function that considers both node information and connection information in the local community. This improves the quality of the fused node while preserving the structure of the current community. In weak fusion, we propose a parameter-based similarity measure, which can detect influential nodes for a local community. We also propose a local community evaluation metric, which does not require true division to determine the optimal local community under different parameters. Experiments, compared to six state-of-the-art algorithms, show that the proposed algorithm improves accuracy and stability, and also demonstrate the effectiveness of the new local community evaluation metrics in parameter selection.

关键词： detection algorithms Heuristic algorithms Optimization Indexes Complex networks Topology Social networking (online) Evaluation metrics local community detection membership function strong fusion weak fusion

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Multi-Source Features Fusion Single Stage 3D Object detection With Transformer

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第4期8卷 2062-2069页

作者： Tong, Guofeng Li, Zheng Peng, Hao Wang, Yaqi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

Due to the high efficiency in extracting context information, voxel-based method is widely used in 3D object detection from point cloud. However, the quantization loss of geometric information is inevitable in the process of voxelization for raw point cloud, which may have a certain impact on final detection performance. To alleviate this problem, we propose a novel single-stage 3D detection algorithm named MFT-SSD for accurate 3D bounding box prediction. Differ from most single-stage methods, our proposed framework combines point-based and voxel-based backbones, and extracts point, multi-scale voxel and BEV features as multi-source features, respectively. In order to enhance the correlation among different representation features, we propose a transformer feature fusion module with self-attention mechanism to fully integrate these multi-source features into richer point-wise features. Then, these fused point-wise features are sent to a candidate generation layer to generate a series of candidate points closer to instance centers. The final 3D bounding boxes are predicted on these generated candidate points. Relevant experiments on KITTI and nuScenes datasets verify that our proposed algorithm has achieved a competitive level compared with some state-of-the-art algorithms.

关键词： Feature extraction Three-dimensional displays Transformers Point cloud compression Convolution detection algorithms Correlation 3D object detection single-stage multi-source features transformer feature fusion point-wise features

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Community detection in Multilayer Networks Via Semi-Supervised Joint Symmetric Nonnegative Matrix Factorization

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1623-1635页

作者： Lv, Laishui Hu, Peng Bardou, Dalal Zheng, Zijun Zhang, Ting China Jiliang Univ Coll informat Engn Hangzhou 310018 Peoples R China Anhui Normal Univ Sch Comp & informat Wuhu 241002 Peoples R China Univ Abbes Laghrour Dept Comp Sci & Math Khenchela 4004 Algeria China Jiliang Univ Coll Sci Hangzhou 310018 Peoples R China Nanjing Normal Univ Sch Comp & Elect informat Nanjing 210023 Peoples R China

Community detection plays an important role in network analysis and has attracted considerable interest from researchers. In the past few decades, various community detection algorithms have been developed for single networks. However, in the real world, relationships between nodes are often of multiple natures, such as friendship, kinship and common interests among people in social networks. These relationships can be modeled by a multilayer network. Thus, identifying communities in multilayer networks has become a challenging problem. The existing algorithms for multilayer networks only utilize the topological structure and ignore the prior information, thereby resulting in low accuracy. In this article, by combining the graph regularization with the prior information, we propose a semi-supervised joint symmetric nonnegative matrix factorization(SSJSNMF) algorithm for community detection in multilayer networks. We use graph regularization term to penalize the latent space dissimilarity of some nodes when prior information shows that these nodes belong to same community. Then, by fusing graph regularization into a joint symmetric nonnegative matrix factorization(NMF) model, the proposed model can utilize the topological structure information and prior information simultaneously. Furthermore, we develop effective multiplicative updating rules to solve the proposed model. Finally, numerical experiments demonstrate that SSSNMF outperforms some existing algorithms.

关键词： Nonhomogeneous media detection algorithms Clustering algorithms Symmetric matrices Feature extraction Multiplexing Tensors Multilayer networks community detection nonnegative matrix factorization semi-supervised learning graph regularization

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Research on Obstacle detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 9198-9208页

作者： Cao, Xiang Ren, Lu Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

Due to the complexity of the ocean environment, an autonomous underwater vehicle (AUV) is disturbed by obstacles when performing tasks. Therefore, the research on underwater obstacle detection and avoidance is particularly important. Based on the images collected by a forward-looking sonar on an AUV, this article proposes an obstacle detection and avoidance algorithm. First, a deep learning-based obstacle candidate area detection algorithm is developed. This algorithm uses the You Only Look Once (YOLO) v3 network to determine obstacle candidate areas in a sonar image. Then, in the determined obstacle candidate areas, the obstacle detection algorithm based on the improved threshold segmentation algorithm is used to detect obstacles accurately. Finally, using the obstacle detection results obtained from the sonar images, an obstacle avoidance algorithm based on deep reinforcement learning (DRL) is developed to plan a reasonable obstacle avoidance path of an AUV. Experimental results show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment.

关键词： Sonar Sonar detection Collision avoidance Sonar navigation Feature extraction Image segmentation detection algorithms Autonomous underwater vehicle (AUV) forward-looking sonar obstacle detection and avoidance path planning

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Parallel Overlapping Community detection Algorithm on GPU

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IEEE TRANSACTIONS ON BIG DATA 2023年第2期9卷 677-687页

作者： Zheng, Zhigao Shi, Xuanhua Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big DataTechnol & Syst Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China

Community detection is one of the most representative graph mining applications, which is often assembled as a concurrent graph partition application to explore the maximum modularity (or gained modularity) of each community. However, many branch divergence operations create significant obstacles to unleashing GPU's high throughput and memory bandwidth, which are needed in community detection applications to divide the vertices into different communities. In this paper, we present Lugger, a GPU-based overlapping community detection algorithm that reduces GPU's branch divergence via the customer-designed cache-aware parallel searching technique. In Lugger, we first design a cache-aware parallel searching policy using the B-Tree structure. Then, we set the B-Tree node matches with the GPU cache line to meet the coalesced memory access manner and avoid the branch divergence in warps. Moreover, we design a positive node splitting scheme to reduce the lock operation and idle threads when building the B-Tree structure. In addition, we implement a warp-centric thread assignment strategy to make sure the workloads across threads are balanced. We implement the proposed algorithm on NVIDIA GPU and evaluate the performance on eight large graphs (up to 3 M vertices and 117 M edges) with ground-truth communities. The experimental results show that Lugger can outperform the state-of-the-art works on scalability and detection quality.

关键词： Graphics processing units Message systems detection algorithms Image edge detection Big Data Parallel processing Instruction sets Overlapping community detection B-Tree warp-centric thread assignment strategy GPGPU parallelism

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Anomaly detection With Representative Neighbors

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第6期34卷 2831-2841页

作者： Liu, Huawen Xu, Xiaodan Li, Enhui Zhang, Shichao Li, Xuelong Shaoxing Univ Dept Comp Sci Shaoxing 312000 Peoples R China Zhejiang Normal Univ Dept Comp Sci Jinhua 321004 Zhejiang Peoples R China Cent South Univ Sch Comp Sci Changsha 410083 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Identifying anomalies from data has attracted increasing attention in recent years due to its broad range of potential applications. Although many efforts have been made for anomaly detection, how to effectively handle high-dimensional data and how to exactly explore neighborhood information, a fundamental issue in anomaly detection, have not yet received sufficient concerns. To circumvent these challenges, in this article, we propose an effective anomaly detection method with representative neighbors for high-dimensional data. Specifically, it projects the high-dimensional data into a low-dimensional space via a sparse operation and explores representative neighbors with a self-representation learning technique. The neighborhood information is then transformed into similarity relations, making the data converge or disperse. Eventually, anomalies are discriminated by a tailored graph clustering technique, which can effectively reveal structural information of the data. Extensive experiments were conducted on ten public real-world datasets with 11 popular anomaly detection algorithms. The results show that the proposed method has encouraging and promising performance compared to the state-of-the-art anomaly detection algorithms.

关键词： Anomaly detection Dimensionality reduction Clustering algorithms Kernel Feature extraction detection algorithms Markov processes Anomaly detection high-dimensional data nearest neighbors sparse learning variable selection

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Optimal Low-Complexity Orthogonal Block Based detection of OTFS for Low-Dispersion Channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 5419-5423页

作者： Liu, Wei Ding, Yajie Li, Chuan Hanzo, Lajos Xidian Univ State Key Labs Integrated Serv Networks Xian 710071 Peoples R China Xian Univ Posts & Telecommun Sch Comp & Technol Xian 710061 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Orthogonal time frequency space (OTFS) modulation constitutes a promising technology for high-mobility scenarios. However, the detection of OTFS systems imposes substantial complexity. Hence, we propose a novel orthogonal block (OB) based detection scheme for significantly reducing the OTFS detection complexity without any performance loss with integer Doppler shifts. This is achieved by recognizing that the received signal can be partitioned into multiple parallel orthogonal blocks. Therefore, the detection of data symbols within an orthogonal block only depends on the signals received within this orthogonal block with reduced dimension. Explicitly, we propose a graph theory based orthogonal block identification algorithm, which models the relationship between the received signal and the original information symbols as a bipartite graph, where a depth first search (DFS) algorithm is invoked for partitioning the received signals into orthogonal blocks. For each orthogonal block, the existing detection algorithms can be used. Since the size of orthogonal blocks may be much lower than that of the original received signals, the detection complexity can be significantly reduced. For example, the complexity of the OB based MMSE detector is approximately a factor 4096 lower than that of the traditional MMSE detector for a channel having two paths.

关键词： Symbols Detectors Complexity theory detection algorithms Delays Partitioning algorithms Approximation algorithms Depth first search low complexity orthogonal block OTFS

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