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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Zhang, Jianyi Yao, Jiaqi Chu, Yanjie Yan, Jikun PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781665415989

Recently, speech content classification begins to be formulated as a multiple instance learning (MIL) problem by some researchers. Under the MIL framework, a speech signal is considered as a bag with labels, and speech fragments are regarded as instances without corresponding labels. On this basis, we propose a new algorithm, named MI-CNN-G, for speech content classification using Graph Convolutional Network (GCN) under the MIL framework. First, we only train the MIL classifier, whose architecture is based on Convolutional Neural Network, for bag classification. Second, we utilize GCN to handle graphs composed of instance features that are the outputs of the MIL classifier for correcting the predicted probabilities of the classifier and then updated the classifier and GCN together. Finally, we employ the MI-CNN-G model to predict the labels of unknown bags. The experimental results show that GCN is effective in improving the performance of the MIL classifier on speech content classification and the MI-CNN-G model outperforms the baseline approaches in the Average Precision metric. © 2021 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of OSM-OFDM Systems with Carrier Frequency Offset 20

Performance Analysis of OSM-OFDM Systems with Carrier Freque...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Jiang, Tingmin Xiao, Yue Dan, Lilin Ji, Xinrong Tang, Wenqi Zhao, Yan Uestc National Key Laboratory of Science and Technology on Communications Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728181417

Offset spatial modulation (OSM) is a novel modulation scheme with simplified radio-frequency-switching structure. In this paper, the concept of OSM is combined with orthogonal frequency division multiplexing (OFDM) transmission to take advantage of the benefits of these two techniques. We shed light on the effects of carrier frequency offset (CFO) on the OSM-OFDM systems over multipath Rayleigh fading channel, where the inter-carrier interference (ICI) induced by CFO degrades the system performance. We further derive the theoretical bit-error rate (BER) upper bound of OSM-OFDM impaired by CFO. Simulation results show that the theoretical BER is in good agreement with its simulated counterpart, which demonstrates the accuracy of the theoretical analysis. © 2020 IEEE.

关键词： Orthogonal frequency division multiplexing

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Reduced-complexity Transmit Antenna Selection for Spatial Modulation 20

Reduced-complexity Transmit Antenna Selection for Spatial Mo...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Zhu, Ruotong Chen, Hao Xiao, Yue Ji, Xinrong Tang, Wenqi Zhao, Yan Uestc National Key Laboratory of Science and Technology on Communications Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728181417

In this paper, a novel transmit antenna selection (TAS) scheme is conceived for spatial modulation multiple-input multiple-output (SM-MIMO) systems, in order to reduce the search complexity. Compared to the existing TAS schemes, the proposed scheme shows a similar performance to optimal one with lower complexity, by exploring an efficient pruning algorithm for tree search based on a combined criterion. Simulation result indicates that the proposed scheme is capable of balancing the transmission performance and search complexity. © 2020 IEEE.

关键词： MIMO systems

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A self-adaptive modified whale optimization algorithm for optimizing the linear antenna array 12

A self-adaptive modified whale optimization algorithm for op...

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Song, Kekang National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) is an effective algorithm for solving complex optimization problem. Its unique search mechanism results in better exploitation than exploration capability which means that it's easy to get trapped in the local optima. A self-adaptive modified whale optimization algorithm named of SAMWOA is proposed. A self-adaptive quasi-opposition method and novel population updating mechanism is integrated into the whale optimization algorithm to improve the solution accuracy. Finally, the proposed algorithm is utilized in optimizing the radiation pattern of a 16-element linear antenna array. The experimental results show that SAMWOA algorithm can reduce the sidelobe level obviously. © 2020 IEEE.

关键词： Optimization

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A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism 12

A modified multi-objective whale optimization algorithm with...

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Deng, Bing National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) has less control parameters and it is easier to implement. The multi-objective whale optimization algorithm (MOWOA) also shows good exploration and exploitation capability. A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism (MMOWOA-DLS) is proposed. First, the opposition-based learning (OBL) is employed to accelerate the convergence speed. Second, a dynamic leader selection mechanism is utilized to improve the solution accuracy. Third, a modified archive grid controller is proposed to delete redundant solutions in external archive. The simulation results show that the performance of MMOWOA-DLS outperforms other algorithms. © 2020 IEEE.

关键词： Dynamics

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A fast deep-learning signal detection architecture for the short-wave band 6

A fast deep-learning signal detection architecture for the s...

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6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Wang, Keren Li, Weihao National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Information Engineering University Zhengzhou China

Wideband signal detection plays an important role in wireless communication systems. In recent years, deep learning (DL) has been introduced, and many trial efforts have been posed. In this paper, a fast deep-learning signal detection architecture is proposed for the short-wave band, which consists of the GPU-based Down Digital Converter (DDC), the DL-based signal detector, and the signal data viewer & recorder. Wideband signal data are taken directly as the input, and detected signals are automatically recorded as the output of the architecture. Experimental results suggest that our architecture is capable of detecting signals in the whole short-wave band in quasi-real-time, and specifically, with single GPU device. © 2020 Journal of Physics: Conference Series.

关键词： Signal detection

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The Adjacency Matrix with Self-Loop and Adaptive Weights for Skeleton-Based Action Recognition

SSRN

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SSRN 2022年

作者： Fang, Zheng Zhang, Xiongwei Cao, Tieyong Zheng, Yunfei National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China Army Engineering University Nanjing210001 China The Army Artillery and Defense Academy of PLA Nanjing211131 China

Human skeleton data has received increasing attention in action recognition due to its background robustness and high efficiency. In skeleton-based action recognition, graph convolutional network (GCN) has become the mainstream method. This paper analyzes the fundamental factor for GCN-based models – the adjacency matrix. We notice that most GCN-based methods conduct their adjacency matrix based on the human natural skeleton structure. Based on experimental results and analysis, we propose that the human natural skeleton structure adjacency matrix is not proper for skeleton-based action recognition. We propose a new adjacency matrix that abandons all rigid neighbor connections but lets the model adaptively learn the relationships of joints. We conduct extensive experiments and analysis with a validation model on two skeleton-based action recognition datasets (NTURGBD60 and FineGYM). Comprehensive experimental results and analysis reveals that 1) the most widely used human natural skeleton structure adjacency matrix is unsuitable in skeleton-based action recognition;2) The proposed adjacency matrix is superior in model performance, noise robustness and transferability. c 2022 Elsevier Ltd. All rights reserved. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Network completion via deep metric learning

Network completion via deep metric learning

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2020 International Conference on Big Data Mining and Information processing, BDMIP 2020

作者： Wei, Qiang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small. © Published under licence by IOP Publishing Ltd.

关键词： Mapping

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

arXiv

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arXiv 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025, CC BY.

关键词： Tensors

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A Signal Synchronization and Pilot-Aided DOA Estimation Method in the presence of Interferences 6

A Signal Synchronization and Pilot-Aided DOA Estimation Meth...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Wei, Lanxin Wang, Siye Xu, Wenbo Li, Dong Yang, Kun Network and Wireless Communication Lab Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Shanghai China

ISBN: (纸本)9781728186351

Direction of arrival (DOA) estimation is a fundamental problem in signal processing. The super-resolution spatial spectrum estimation algorithm based on feature decomposition, such as multiple signal classification (MUSIC) and estimating signal parameter via rotational invariance techniques (ESPRIT), has a good performance in high signal-to-interference plus noise ratio (SINR) environment and single interference. However, strong interferences will make DOA estimation error. And the number of angles that can be estimated by these methods is limited by the freedom of the antenna. The existence of multiple interferences will affect accuracy of DOA estimation. To address this problem, in this paper, we propose a novel dual-channel synchronization architecture and a Piloted-Aided DOA estimation method. We improve the matching performance by dual channel synchronization. A new matched filter is designed by using the idea of filter truncation, which reduces the computational resource cost. And a new DOA estimation method is proposed, which can estimate the angle of target signal in the environment of low SINR and multiple interferences. Experiments were performed to verify the performance of the proposed design. Results show that this architecture can greatly reduce the computational complexity. And the method successfully gets the direction of target signal in low SINR environment. © 2020 IEEE.

关键词： Synchronization

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