Pub Date : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553018
L. Spreeuwers, Maikel Schils, R. Veldhuis
Automated face recognition is increasingly used as a reliable means to establish the identity of persons for various purposes, ranging from automated passport checks at the border to transferring money and unlocking mobile phones. Face morphing is a technique to blend facial images of two or more subjects such that the result resembles both subjects. Face morphing attacks pose a serious risk for any face recognition system. Without automated morphing detection, state of the art face recognition systems are extremely vulnerable to morphing attacks. Morphing detection methods published in literature often only work for a few types of morphs or on a single dataset with morphed photographs. We create face morphing databases with varying characteristics and how for a LBP/SVM based morphing detection method that performs on par with the state of the art (around 2% EER), the performance collapses with an EER as high as if it is tested across databases with different characteristics. In addition we show that simple image manipulations like adding noise or rescaling can be used to obscure morphing artifacts and deteriorate the morphing detection performance.
{"title":"Towards Robust Evaluation of Face Morphing Detection","authors":"L. Spreeuwers, Maikel Schils, R. Veldhuis","doi":"10.23919/EUSIPCO.2018.8553018","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553018","url":null,"abstract":"Automated face recognition is increasingly used as a reliable means to establish the identity of persons for various purposes, ranging from automated passport checks at the border to transferring money and unlocking mobile phones. Face morphing is a technique to blend facial images of two or more subjects such that the result resembles both subjects. Face morphing attacks pose a serious risk for any face recognition system. Without automated morphing detection, state of the art face recognition systems are extremely vulnerable to morphing attacks. Morphing detection methods published in literature often only work for a few types of morphs or on a single dataset with morphed photographs. We create face morphing databases with varying characteristics and how for a LBP/SVM based morphing detection method that performs on par with the state of the art (around 2% EER), the performance collapses with an EER as high as if it is tested across databases with different characteristics. In addition we show that simple image manipulations like adding noise or rescaling can be used to obscure morphing artifacts and deteriorate the morphing detection performance.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116345414","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8552998
A. Cserkaszky, P. A. Kara, A. Barsi, M. Martini, T. Balogh
The ray structure and sampling properties of different light-field representations inherently determine their use-cases. Currently prevalent linear data structures do not allow for joint processing of light-fields captured from multiple sides of a scene. In this paper, we review and highlight the differences in capturing and reconstruction between light-fields captured with linear and circular camera arrays. We also examine and improve the processing of light-fields captured with circular camera arrays with a focus on their use in reconstructing dense light-fields, by proposing a new resampling technique for circular light-fields. The proposed circular epipolar light-field structure creates a simple sinusoidal relation between the objects of the scene and their curves in the epipolar image, opening the way of efficient reconstruction of circular light-fields.
{"title":"Light - fields of Circular Camera Arrays","authors":"A. Cserkaszky, P. A. Kara, A. Barsi, M. Martini, T. Balogh","doi":"10.23919/EUSIPCO.2018.8552998","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8552998","url":null,"abstract":"The ray structure and sampling properties of different light-field representations inherently determine their use-cases. Currently prevalent linear data structures do not allow for joint processing of light-fields captured from multiple sides of a scene. In this paper, we review and highlight the differences in capturing and reconstruction between light-fields captured with linear and circular camera arrays. We also examine and improve the processing of light-fields captured with circular camera arrays with a focus on their use in reconstructing dense light-fields, by proposing a new resampling technique for circular light-fields. The proposed circular epipolar light-field structure creates a simple sinusoidal relation between the objects of the scene and their curves in the epipolar image, opening the way of efficient reconstruction of circular light-fields.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739603","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553113
Panagiotis A. Traganitis, G. Giannakis
In recent years, advances in pattern recognition and data analytics have spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm exhibits different behavior for different types of data, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create such a high-performance meta-learner, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most current works presume that all classifiers are independent, this work introduces a scheme that can handle dependencies between classifiers. Preliminary tests on synthetic data showcase the potential of the proposed approach.
{"title":"Blind Multi-class Ensemble Learning with Dependent Classifiers","authors":"Panagiotis A. Traganitis, G. Giannakis","doi":"10.23919/EUSIPCO.2018.8553113","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553113","url":null,"abstract":"In recent years, advances in pattern recognition and data analytics have spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm exhibits different behavior for different types of data, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create such a high-performance meta-learner, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most current works presume that all classifiers are independent, this work introduces a scheme that can handle dependencies between classifiers. Preliminary tests on synthetic data showcase the potential of the proposed approach.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047637","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8552943
Yuming Huang, Ashkan Panahi, H. Krim, Liyi Dai
We develop a Belief Propagation algorithm for community detection problem in multiplex networks, which more accurately represents many real-world systems. Previous works have established that real world multiplex networks exhibit redundant structures/communities, and that community detection performance improves by aggregating (fusing) redundant layers which are generated from the same Stochastic Block Model (SBM). We introduce a probability model for generic multiplex networks, aiming to fuse community structure across layers, without assuming or seeking the same SBM generative model for different layers. Numerical experiment shows that our model finds out consistent communities between layers and yields a significant detectability improvement over the single layer architecture. Our model also achieves a comparable performance to a reference model where we assume consistent communities in prior. Finally we compare our method with multilayer modularity optimization in heterogeneous networks, and show that our method detects correct community labels more reliably.
{"title":"Fusion of Community Structures in Multiplex Networks by Label Constraints","authors":"Yuming Huang, Ashkan Panahi, H. Krim, Liyi Dai","doi":"10.23919/EUSIPCO.2018.8552943","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8552943","url":null,"abstract":"We develop a Belief Propagation algorithm for community detection problem in multiplex networks, which more accurately represents many real-world systems. Previous works have established that real world multiplex networks exhibit redundant structures/communities, and that community detection performance improves by aggregating (fusing) redundant layers which are generated from the same Stochastic Block Model (SBM). We introduce a probability model for generic multiplex networks, aiming to fuse community structure across layers, without assuming or seeking the same SBM generative model for different layers. Numerical experiment shows that our model finds out consistent communities between layers and yields a significant detectability improvement over the single layer architecture. Our model also achieves a comparable performance to a reference model where we assume consistent communities in prior. Finally we compare our method with multilayer modularity optimization in heterogeneous networks, and show that our method detects correct community labels more reliably.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127609486","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553311
A. Napolitano
A new cyclostationarity-based signal detector is proposed. It is based on (conjugate) cyclic autocorrelation measurements at pairs of cycle frequencies and lags for which the signal-of-interest exhibits cyclostationarity while the disturbance does not. No assumption is made on the noise distribution and/or its stationarity. A comparison is made with a previously proposed statistical test for presence of cyclostationarity. Monte Carlo simulations are carried out for performance analysis.
{"title":"On Cyclostationarity-Based Signal Detection","authors":"A. Napolitano","doi":"10.23919/EUSIPCO.2018.8553311","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553311","url":null,"abstract":"A new cyclostationarity-based signal detector is proposed. It is based on (conjugate) cyclic autocorrelation measurements at pairs of cycle frequencies and lags for which the signal-of-interest exhibits cyclostationarity while the disturbance does not. No assumption is made on the noise distribution and/or its stationarity. A comparison is made with a previously proposed statistical test for presence of cyclostationarity. Monte Carlo simulations are carried out for performance analysis.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620557","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553114
Michael Ulrich, Bin Yang
This paper examines the classification of walking, standing and mirrored persons based on radar micro-Doppler (m-D) measurements to resolve ambiguities in thermal infrared (TIR) mirror images in firefighting. If the walking or standing person is observed directly, its m-D is measured. In the case of a person mirrored on a reflecting object, only the m-D of the reflecting object is measured. Their spectrogram is differentiable which enables a classification. One difficulty is the random movement of the handheld radar which leads to short observation durations and Doppler blurring. A classification based on short spectrograms is proposed, where the influence of the short-time Fourier transform window length is investigated. Furthermore, a regularization is proposed to improve the classifier interpretability for this safety application.
{"title":"Short-Duration Doppler Spectrogram for Person Recognition with a Handheld Radar","authors":"Michael Ulrich, Bin Yang","doi":"10.23919/EUSIPCO.2018.8553114","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553114","url":null,"abstract":"This paper examines the classification of walking, standing and mirrored persons based on radar micro-Doppler (m-D) measurements to resolve ambiguities in thermal infrared (TIR) mirror images in firefighting. If the walking or standing person is observed directly, its m-D is measured. In the case of a person mirrored on a reflecting object, only the m-D of the reflecting object is measured. Their spectrogram is differentiable which enables a classification. One difficulty is the random movement of the handheld radar which leads to short observation durations and Doppler blurring. A classification based on short spectrograms is proposed, where the influence of the short-time Fourier transform window length is investigated. Furthermore, a regularization is proposed to improve the classifier interpretability for this safety application.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132073640","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553331
D. Giacobello
In this paper, we propose an expectation-maximization algorithm to perform online tracking of moving sources around multi-microphone devices. We are particularly targeting the application scenario of distant-talking control of a music playback device. The goal is to perform spatial tracking of the moving sources and to estimate the probability that each of these sources is active. In particular, we use the expectation-maximization algorithm to capture the statistical behavior of the feature space representing the ensemble of sources as a Gaussian mixture model, assigning each Gaussian component to an individual acoustic source. The features used exploit a wide range of information on the sources behavior making the system robust to noise, reverberation, and music playback. We then differentiate between desired and interfering sources. The spatial information and activity level is then determined for each desired source. Experimental evaluation of a real acoustic source tracking problem with and without music playback shows promising results for the proposed approach.
{"title":"An Online Expectation-Maximization Algorithm for Tracking Acoustic Sources in Multi-Microphone Devices During Music Playback","authors":"D. Giacobello","doi":"10.23919/EUSIPCO.2018.8553331","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553331","url":null,"abstract":"In this paper, we propose an expectation-maximization algorithm to perform online tracking of moving sources around multi-microphone devices. We are particularly targeting the application scenario of distant-talking control of a music playback device. The goal is to perform spatial tracking of the moving sources and to estimate the probability that each of these sources is active. In particular, we use the expectation-maximization algorithm to capture the statistical behavior of the feature space representing the ensemble of sources as a Gaussian mixture model, assigning each Gaussian component to an individual acoustic source. The features used exploit a wide range of information on the sources behavior making the system robust to noise, reverberation, and music playback. We then differentiate between desired and interfering sources. The spatial information and activity level is then determined for each desired source. Experimental evaluation of a real acoustic source tracking problem with and without music playback shows promising results for the proposed approach.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132414833","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553322
David Abou Chacra, J. Zelek
Road quality assessment is a key task in a city's duties as it allows a city to operate more efficiently. This assessment means a city's budget can be allocated appropriately to make sure the city makes the most of its usually limited budget. However, this assessment still relies largely on manual annotation to generate the Overall Condition Index (OCI) of a pavement stretch. Manual surveying can be inaccurate, while on the other side of the spectrum a large portion of automatic surveying techniques rely on expensive equipment (such as laser line scanners). To solve this problem, we propose an automated infrastructure assessment method that relies on street view images for its input and uses a spectrum of computer vision and pattern recognition methods to generate its assessments. We first segment the pavement surface in the natural image. After this, we operate under the assumption that only the road pavement remains, and utilize a sliding window approach using Fisher Vector encoding to detect the defects in that pavement; with labelled data, we would also be able to classify the defect type (longitudinal crack, transverse crack, alligator crack, pothole … etc.) at this stage. A weighed contour map within these distressed regions can be used to identify exact crack and defect locations. Combining this information allows us to determine severities and locations of individual defects in the image. We use a manually annotated dataset of Google Street View images in Hamilton, Ontario, Canada. We show promising results, achieving a 93% Fl-measure on crack region detection from perspective images.
{"title":"Municipal Infrastructure Anomaly and Defect Detection","authors":"David Abou Chacra, J. Zelek","doi":"10.23919/EUSIPCO.2018.8553322","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553322","url":null,"abstract":"Road quality assessment is a key task in a city's duties as it allows a city to operate more efficiently. This assessment means a city's budget can be allocated appropriately to make sure the city makes the most of its usually limited budget. However, this assessment still relies largely on manual annotation to generate the Overall Condition Index (OCI) of a pavement stretch. Manual surveying can be inaccurate, while on the other side of the spectrum a large portion of automatic surveying techniques rely on expensive equipment (such as laser line scanners). To solve this problem, we propose an automated infrastructure assessment method that relies on street view images for its input and uses a spectrum of computer vision and pattern recognition methods to generate its assessments. We first segment the pavement surface in the natural image. After this, we operate under the assumption that only the road pavement remains, and utilize a sliding window approach using Fisher Vector encoding to detect the defects in that pavement; with labelled data, we would also be able to classify the defect type (longitudinal crack, transverse crack, alligator crack, pothole … etc.) at this stage. A weighed contour map within these distressed regions can be used to identify exact crack and defect locations. Combining this information allows us to determine severities and locations of individual defects in the image. We use a manually annotated dataset of Google Street View images in Hamilton, Ontario, Canada. We show promising results, achieving a 93% Fl-measure on crack region detection from perspective images.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134009824","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553340
M. Saleh, R. Bouquin-Jeannès
According to the world health organization, millions of elderly suffer from falls every year. These falls are one of the major causes of death worldwide. As a rapid medical intervention would considerably decrease the serious consequences of such falls, automatic fall detection systems for elderly has become a necessity. In this paper, an efficient machine learning-based fall detection algorithm is proposed. Thanks to the proposed local binary features, this algorithm shows a high accuracy exceeding 99% when tested on a large dataset. In addition, it enjoys an attractive property that the computational cost of decision-making is independent from the complexity of the trained machine. Thus, the proposed algorithm overcomes a critical challenge of designing accurate yet low-cost solutions for wearable fall detectors. The aforementioned property enables implementing autonomous, low-power consumption wearable fall detectors.
{"title":"An Efficient Machine Learning-Based Fall Detection Algorithm using Local Binary Features","authors":"M. Saleh, R. Bouquin-Jeannès","doi":"10.23919/EUSIPCO.2018.8553340","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553340","url":null,"abstract":"According to the world health organization, millions of elderly suffer from falls every year. These falls are one of the major causes of death worldwide. As a rapid medical intervention would considerably decrease the serious consequences of such falls, automatic fall detection systems for elderly has become a necessity. In this paper, an efficient machine learning-based fall detection algorithm is proposed. Thanks to the proposed local binary features, this algorithm shows a high accuracy exceeding 99% when tested on a large dataset. In addition, it enjoys an attractive property that the computational cost of decision-making is independent from the complexity of the trained machine. Thus, the proposed algorithm overcomes a critical challenge of designing accurate yet low-cost solutions for wearable fall detectors. The aforementioned property enables implementing autonomous, low-power consumption wearable fall detectors.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134207544","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553104
Ozlem Tugfe Demir, T. E. Tuncer
In this paper, we consider beamformer design for multi-group multicasting where a common message is transmitted to the users in each group. We propose a novel effective alternating direction method of multipliers (ADMM) formulation in order to reduce the computational complexity of the existing state-of-the-art algorithm for multi-group multicast beamforming with per-antenna power constraints. The proposed approach is advantageous for the scenarios where the dimension of the channel matrix is less than the number of antennas at the base station. This case is always valid when the number of users is less than that of antennas, which is a practical situation in massive-MIMO systems. Simulation results show that the proposed method performs the same with significantly less computational time compared to the benchmark algorithm.
{"title":"Improved ADMM-Based Algorithm for Multi-Group Multicast Beamforming in Large-Scale Antenna Systems","authors":"Ozlem Tugfe Demir, T. E. Tuncer","doi":"10.23919/EUSIPCO.2018.8553104","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553104","url":null,"abstract":"In this paper, we consider beamformer design for multi-group multicasting where a common message is transmitted to the users in each group. We propose a novel effective alternating direction method of multipliers (ADMM) formulation in order to reduce the computational complexity of the existing state-of-the-art algorithm for multi-group multicast beamforming with per-antenna power constraints. The proposed approach is advantageous for the scenarios where the dimension of the channel matrix is less than the number of antennas at the base station. This case is always valid when the number of users is less than that of antennas, which is a practical situation in massive-MIMO systems. Simulation results show that the proposed method performs the same with significantly less computational time compared to the benchmark algorithm.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133961826","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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