Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9747354
N. Ouzir, J. Pesquet, F. Pascal
The multivariate power exponential (MEP) distribution can model a broad range of signals. In noisy scenarios, the robust estimation of the MEP parameters has been traditionally addressed by a fixed-point approach associated with a nonconvex optimization problem. Establishing convergence properties for this approach when the distribution mean is unknown is still an open problem. As an alternative, this paper presents a novel convex formulation for robustly estimating MEP parameters in the presence of multiplicative perturbations. The proposed approach is grounded on a re-parametrization of the original likelihood function in a way that ensures convexity. We also show that this property is preserved for several typical regularization functions. Compared with the robust Tyler’s estimator, the proposed method shows a more accurate precision matrix estimation, with similar mean and covariance estimation performance.
{"title":"A Convex Formulation for the Robust Estimation of Multivariate Exponential Power Models","authors":"N. Ouzir, J. Pesquet, F. Pascal","doi":"10.1109/icassp43922.2022.9747354","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747354","url":null,"abstract":"The multivariate power exponential (MEP) distribution can model a broad range of signals. In noisy scenarios, the robust estimation of the MEP parameters has been traditionally addressed by a fixed-point approach associated with a nonconvex optimization problem. Establishing convergence properties for this approach when the distribution mean is unknown is still an open problem. As an alternative, this paper presents a novel convex formulation for robustly estimating MEP parameters in the presence of multiplicative perturbations. The proposed approach is grounded on a re-parametrization of the original likelihood function in a way that ensures convexity. We also show that this property is preserved for several typical regularization functions. Compared with the robust Tyler’s estimator, the proposed method shows a more accurate precision matrix estimation, with similar mean and covariance estimation performance.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114967629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9746255
Zhenyu Tang, Long Ma, Xiaoke Shang, Xin Fan
To see clearly in low-light scenarios, a series of learning-based techniques have been developed to improve visual quality. However, due to the absence of semantic-level features, the existing methods are perhaps less effective on semantic-oriented visual analysis tasks (e.g., saliency detection). To break down the limitation, we propose a new classification-driven enhancement method with heterogeneous feature fusion. Specifically, we construct a new low-light image enhancement network by integrating features acquired from the pre-trained classification network. Then, to better exploit the semantic-level information, we establish a Heterogeneous Feature Fusion (HF2) operation with channel-and-spatial attention to strength the effects of cross-domain features. HF2 acts on not only the fusion between classification and encoded features but also the fusion between encoded and decoded features. Extensive experiments are conducted to indicate our superiority against other state-of-the-art methods. The application on saliency detection further reveals our effectiveness in settling the semantic-oriented visual tasks.
{"title":"Learning to Fuse Heterogeneous Features for Low-Light Image Enhancement","authors":"Zhenyu Tang, Long Ma, Xiaoke Shang, Xin Fan","doi":"10.1109/icassp43922.2022.9746255","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746255","url":null,"abstract":"To see clearly in low-light scenarios, a series of learning-based techniques have been developed to improve visual quality. However, due to the absence of semantic-level features, the existing methods are perhaps less effective on semantic-oriented visual analysis tasks (e.g., saliency detection). To break down the limitation, we propose a new classification-driven enhancement method with heterogeneous feature fusion. Specifically, we construct a new low-light image enhancement network by integrating features acquired from the pre-trained classification network. Then, to better exploit the semantic-level information, we establish a Heterogeneous Feature Fusion (HF2) operation with channel-and-spatial attention to strength the effects of cross-domain features. HF2 acts on not only the fusion between classification and encoded features but also the fusion between encoded and decoded features. Extensive experiments are conducted to indicate our superiority against other state-of-the-art methods. The application on saliency detection further reveals our effectiveness in settling the semantic-oriented visual tasks.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9746739
R. Liu, Zheng Lin, Peng Fu, Yuanxin Liu, Weiping Wang
Zero-shot and few-shot stance detection (ZFSD) aims to automatically identify the users’ stance toward a wide range of continuously emerging targets without or with limited labeled data. Previous works on in-target and cross-target stance detection typically focus on extremely limited targets, which is not applicable to the zero-shot and few-shot scenarios. Additionally, existing ZFSD models are not good at modeling the relationship between seen and unseen targets. In this paper, we propose a unified end-to-end framework with a discrete latent topic variable that implicitly establishes the connections between targets. Moreover, we apply supervised contrastive learning to enhance the generalization ability of the model. Comprehensive experiments on the ZFSD task verify the effectiveness and superiority of our proposed method.
{"title":"Connecting Targets via Latent Topics And Contrastive Learning: A Unified Framework For Robust Zero-Shot and Few-Shot Stance Detection","authors":"R. Liu, Zheng Lin, Peng Fu, Yuanxin Liu, Weiping Wang","doi":"10.1109/icassp43922.2022.9746739","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746739","url":null,"abstract":"Zero-shot and few-shot stance detection (ZFSD) aims to automatically identify the users’ stance toward a wide range of continuously emerging targets without or with limited labeled data. Previous works on in-target and cross-target stance detection typically focus on extremely limited targets, which is not applicable to the zero-shot and few-shot scenarios. Additionally, existing ZFSD models are not good at modeling the relationship between seen and unseen targets. In this paper, we propose a unified end-to-end framework with a discrete latent topic variable that implicitly establishes the connections between targets. Moreover, we apply supervised contrastive learning to enhance the generalization ability of the model. Comprehensive experiments on the ZFSD task verify the effectiveness and superiority of our proposed method.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115347432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9747256
Pol Grau Jurado, Xinyue Liang, S. Chatterjee
We propose to use deterministic transforms as weight matrices for several feedforward neural networks. The use of deterministic transforms helps to reduce the computational complexity in two ways: (1) matrix-vector product complexity in forward pass, helping real time complexity, and (2) fully avoiding backpropagation in the training stage. For each layer of a feedforward network, we pro-pose two unsupervised methods to choose the most appropriate deterministic transform from a set of transforms (a bag of well-known transforms). Experimental results show that the use of deterministic transforms is as good as traditional random matrices in the sense of providing similar classification performance.
{"title":"Deterministic Transform Based Weight Matrices for Neural Networks","authors":"Pol Grau Jurado, Xinyue Liang, S. Chatterjee","doi":"10.1109/icassp43922.2022.9747256","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747256","url":null,"abstract":"We propose to use deterministic transforms as weight matrices for several feedforward neural networks. The use of deterministic transforms helps to reduce the computational complexity in two ways: (1) matrix-vector product complexity in forward pass, helping real time complexity, and (2) fully avoiding backpropagation in the training stage. For each layer of a feedforward network, we pro-pose two unsupervised methods to choose the most appropriate deterministic transform from a set of transforms (a bag of well-known transforms). Experimental results show that the use of deterministic transforms is as good as traditional random matrices in the sense of providing similar classification performance.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115405950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/ICASSP43922.2022.9747789
Sai Srinadhu Katta, Kide Vuojärvi, S. Nandyala, Ulla-Maria Kovalainen, Lauren Baddeley
Detecting propeller damage in Unmanned Aerial Vehicles (UAV) is a crucial step in ensuring their operational resilience and safety. In this work, we present a novel real-world audio data set of propeller anomalies, and use several deep learning models to classify the damage. This data set consists of more than 5 hours of audio recordings, covering all configurations of intact and broken propellers in a UAV quadcopter. A microphone array was mounted onto a UAV, and numerous autonomous indoor missions were flown. Our on-board setup has provided clean audio recordings containing little background noise. We have developed classification models for this data set, using different deep learning architectures: Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformer Encoder (TrEnc). We conclude that the TrEnc outperforms other architectures, having 11k parameters, .57M Flops, 98.30% accuracy, .98 precision, and .98 recall. Finally, we make our data set publicly available here⊙.
无人机螺旋桨损伤检测是保证无人机运行弹性和安全性的关键环节。在这项工作中，我们提出了一种新的真实世界螺旋桨异常音频数据集，并使用几种深度学习模型对损伤进行分类。该数据集由超过5小时的录音组成，涵盖了无人机四轴飞行器中完整和损坏的螺旋桨的所有配置。一个麦克风阵列被安装在一架无人机上，并执行了许多自主室内任务。我们的车载设置提供了包含少量背景噪音的干净音频记录。我们为该数据集开发了分类模型，使用不同的深度学习架构:深度神经网络(dnn)、卷积神经网络(cnn)、长短期记忆(LSTM)和变压器编码器(TrEnc)。我们得出结论，TrEnc优于其他架构，具有11k参数，0.57 m Flops, 98.30%准确率，0.98精度和0.98召回率。最后，我们在这里公开了我们的数据集⊙。
{"title":"Real-World On-Board Uav Audio Data Set For Propeller Anomalies","authors":"Sai Srinadhu Katta, Kide Vuojärvi, S. Nandyala, Ulla-Maria Kovalainen, Lauren Baddeley","doi":"10.1109/ICASSP43922.2022.9747789","DOIUrl":"https://doi.org/10.1109/ICASSP43922.2022.9747789","url":null,"abstract":"Detecting propeller damage in Unmanned Aerial Vehicles (UAV) is a crucial step in ensuring their operational resilience and safety. In this work, we present a novel real-world audio data set of propeller anomalies, and use several deep learning models to classify the damage. This data set consists of more than 5 hours of audio recordings, covering all configurations of intact and broken propellers in a UAV quadcopter. A microphone array was mounted onto a UAV, and numerous autonomous indoor missions were flown. Our on-board setup has provided clean audio recordings containing little background noise. We have developed classification models for this data set, using different deep learning architectures: Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformer Encoder (TrEnc). We conclude that the TrEnc outperforms other architectures, having 11k parameters, .57M Flops, 98.30% accuracy, .98 precision, and .98 recall. Finally, we make our data set publicly available here⊙.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115447060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9747311
Jian Zhang, Runwei Ding, Miaoju Ban, Tianyu Guo
Surface defect detection is a common task for industrial quality control, which increasingly requires accuracy and real-time ability. However, the current segmentation networks are not effective in dealing with defect boundary details, local similarity of different defects and low contrast between defect and background. To this end, we propose a real-time surface defect segmentation network (FDSNet) based on two-branch architecture, in which two corresponding auxiliary tasks are introduced to encode more boundary details and semantic context. To handle the local similarity problem of different surface defects, we propose a Global Context Upsampling (GCU) module by capturing long-range context from multi-scales. Moreover, we present a representative Mobile phone screen Surface Defect (MSD) segmentation dataset to alleviate the lack of dataset in this field. Experiments on NEU-Seg, Magnetic-tile-defect-datasets and MSD dataset show that the proposed FDSNet achieves promising trade-off between accuracy and inference speed. The dataset and code are available at https://github.com/jianzhang96/fdsnet.
{"title":"FDSNeT: An Accurate Real-Time Surface Defect Segmentation Network","authors":"Jian Zhang, Runwei Ding, Miaoju Ban, Tianyu Guo","doi":"10.1109/icassp43922.2022.9747311","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747311","url":null,"abstract":"Surface defect detection is a common task for industrial quality control, which increasingly requires accuracy and real-time ability. However, the current segmentation networks are not effective in dealing with defect boundary details, local similarity of different defects and low contrast between defect and background. To this end, we propose a real-time surface defect segmentation network (FDSNet) based on two-branch architecture, in which two corresponding auxiliary tasks are introduced to encode more boundary details and semantic context. To handle the local similarity problem of different surface defects, we propose a Global Context Upsampling (GCU) module by capturing long-range context from multi-scales. Moreover, we present a representative Mobile phone screen Surface Defect (MSD) segmentation dataset to alleviate the lack of dataset in this field. Experiments on NEU-Seg, Magnetic-tile-defect-datasets and MSD dataset show that the proposed FDSNet achieves promising trade-off between accuracy and inference speed. The dataset and code are available at https://github.com/jianzhang96/fdsnet.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/ICASSP43922.2022.9747112
Ruixiang Geng, Qing Liu, Shuo Feng, Yixiong Liang
Fully automated cervical cancer grading on the level of Whole Slide Images (WSI) is a challenge task. As WSIs are in gigapixel resolution, it is impossible to train a deep classification neural network with the entire WSIs as inputs. To bypass this problem, we propose a two-stage learning framework. In detail, we propose to first learn patch-level deep pathological features for smear patches via a patch-level feature learning module, which is trained via leveraging the cell instance detection task. Then, we propose to learn WSI-level pathological features from patch-level features for cervical cancer grading. We conduct extensive experiments on our private dataset and make comparisons with rule-based cervical cancer grading methods. Experimental results demonstrate that our proposed deep feature-based WSI-level cervical cancer grading method achieves state-of-the-art performance.
{"title":"Learning Deep Pathological Features for WSI-Level Cervical Cancer Grading","authors":"Ruixiang Geng, Qing Liu, Shuo Feng, Yixiong Liang","doi":"10.1109/ICASSP43922.2022.9747112","DOIUrl":"https://doi.org/10.1109/ICASSP43922.2022.9747112","url":null,"abstract":"Fully automated cervical cancer grading on the level of Whole Slide Images (WSI) is a challenge task. As WSIs are in gigapixel resolution, it is impossible to train a deep classification neural network with the entire WSIs as inputs. To bypass this problem, we propose a two-stage learning framework. In detail, we propose to first learn patch-level deep pathological features for smear patches via a patch-level feature learning module, which is trained via leveraging the cell instance detection task. Then, we propose to learn WSI-level pathological features from patch-level features for cervical cancer grading. We conduct extensive experiments on our private dataset and make comparisons with rule-based cervical cancer grading methods. Experimental results demonstrate that our proposed deep feature-based WSI-level cervical cancer grading method achieves state-of-the-art performance.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123067676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unsupervised domain adaptation commonly uses cycle generative networks to produce synthesis data from source to target domains. Unfortunately, translated samples cannot effectively preserve semantic information from input sources, resulting in bad or low adaptability of the network to segment target data. This work proposes an advantageous domain translation mechanism to improve the perceptual ability of the network for accurate unlabeled target data segmentation. Our domain translation employs patchwise contrastive learning to improve the semantic content consistency between input and translated images. Our approach was applied to unsupervised domain adaptation based abdominal organ segmentation. The experimental results demonstrate the effectiveness of our framework that outperforms other methods.
{"title":"Contrastive Translation Learning For Medical Image Segmentation","authors":"Wankang Zeng, Wenkang Fan, Dongfang Shen, Yinran Chen, Xióngbiao Luó","doi":"10.1109/icassp43922.2022.9747097","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747097","url":null,"abstract":"Unsupervised domain adaptation commonly uses cycle generative networks to produce synthesis data from source to target domains. Unfortunately, translated samples cannot effectively preserve semantic information from input sources, resulting in bad or low adaptability of the network to segment target data. This work proposes an advantageous domain translation mechanism to improve the perceptual ability of the network for accurate unlabeled target data segmentation. Our domain translation employs patchwise contrastive learning to improve the semantic content consistency between input and translated images. Our approach was applied to unsupervised domain adaptation based abdominal organ segmentation. The experimental results demonstrate the effectiveness of our framework that outperforms other methods.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123178317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9746953
Miquel Ferriol Galmés, Xiangle Cheng, Xiang Shi, Shihan Xiao, P. Barlet-Ros, A. Cabellos-Aparicio
Network modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing them in real production networks. Recently, Graph Neural Networks (GNNs) have emerged as a practical solution to produce network models that can learn and extract complex patterns from real data without making any assumptions. However, state-of-the-art GNN-based network models only work with traffic matrices, this is a very coarse and simplified representation of network traffic. Although this assumption has shown to work well in certain use-cases, it is a limiting factor because, in practice, networks operate with flows. In this paper, we present FlowDT a new DL-based solution designed to model computer networks at the fine-grained flow level. In our evaluation, we show how FlowDT can accurately predict relevant per-flow performance metrics with an error of 3.5%, FlowDT’s performance is also benchmarked against vanilla DL models as well as with Queuing Theory.
{"title":"FlowDT: A Flow-Aware Digital Twin for Computer Networks","authors":"Miquel Ferriol Galmés, Xiangle Cheng, Xiang Shi, Shihan Xiao, P. Barlet-Ros, A. Cabellos-Aparicio","doi":"10.1109/icassp43922.2022.9746953","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746953","url":null,"abstract":"Network modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing them in real production networks. Recently, Graph Neural Networks (GNNs) have emerged as a practical solution to produce network models that can learn and extract complex patterns from real data without making any assumptions. However, state-of-the-art GNN-based network models only work with traffic matrices, this is a very coarse and simplified representation of network traffic. Although this assumption has shown to work well in certain use-cases, it is a limiting factor because, in practice, networks operate with flows. In this paper, we present FlowDT a new DL-based solution designed to model computer networks at the fine-grained flow level. In our evaluation, we show how FlowDT can accurately predict relevant per-flow performance metrics with an error of 3.5%, FlowDT’s performance is also benchmarked against vanilla DL models as well as with Queuing Theory.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1109/icassp43922.2022.9746094
Hyeonjin Chung, Sunwoo Kim
This paper proposes a two-stage beam training and a channel estimation based on fast alternating least squares (FALS) for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. To reduce the beam training overhead, only selected columns and rows of the channel matrix are observed by two-stage beam training. This beam training produces a partly observed channel matrix with low coherence, which enables the low rank matrix completion technique to recover unobserved entries. Unobserved entries are recovered by FALS, which alternatingly updates the left and the right singular vectors that comprise the channel. Simulation results and analysis show that the proposed algorithm is computationally efficient and has superior accuracy to existing algorithms.
{"title":"Efficient Two-Stage Beam Training and Channel Estimation for Ris-Aided Mmwave Systems Via Fast Alternating Least Squares","authors":"Hyeonjin Chung, Sunwoo Kim","doi":"10.1109/icassp43922.2022.9746094","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746094","url":null,"abstract":"This paper proposes a two-stage beam training and a channel estimation based on fast alternating least squares (FALS) for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. To reduce the beam training overhead, only selected columns and rows of the channel matrix are observed by two-stage beam training. This beam training produces a partly observed channel matrix with low coherence, which enables the low rank matrix completion technique to recover unobserved entries. Unobserved entries are recovered by FALS, which alternatingly updates the left and the right singular vectors that comprise the channel. Simulation results and analysis show that the proposed algorithm is computationally efficient and has superior accuracy to existing algorithms.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115828525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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