Pub Date : 2023-07-02DOI: 10.1109/SSP53291.2023.10208047
Kretika Goel, M. Agrawal, Subrat Kar
The term fractal refers to the fractional dimensions that have recursive nature and when clubbed with the properties of sparse arrays leads to the generation of a novel array called a sparse fractal array. In this paper, we extend our research to the 2D domain by introducing planar sparse arrays which generate hole-free difference coarray and have OpN2q elements just like the OBA but here in the new closed box form, with the additional property of fractal arrays along with sparseness. To estimate azimuth and elevation angle we have designed planar sparse fractal arrays using nested arrays and coprime arrays as the fundamental basic generating array which helps in achieving a high degree of freedom which makes it useful for DOA estimation. Simulations show that the proposed planar arrays have the better estimation performance when compared with existing planar arrays like URA, OBA, and CPA.
{"title":"DOA Estimation using Planar Sparse Fractal Array","authors":"Kretika Goel, M. Agrawal, Subrat Kar","doi":"10.1109/SSP53291.2023.10208047","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208047","url":null,"abstract":"The term fractal refers to the fractional dimensions that have recursive nature and when clubbed with the properties of sparse arrays leads to the generation of a novel array called a sparse fractal array. In this paper, we extend our research to the 2D domain by introducing planar sparse arrays which generate hole-free difference coarray and have OpN2q elements just like the OBA but here in the new closed box form, with the additional property of fractal arrays along with sparseness. To estimate azimuth and elevation angle we have designed planar sparse fractal arrays using nested arrays and coprime arrays as the fundamental basic generating array which helps in achieving a high degree of freedom which makes it useful for DOA estimation. Simulations show that the proposed planar arrays have the better estimation performance when compared with existing planar arrays like URA, OBA, and CPA.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126694935","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208064
V. Leplat, A. Phan, A. Ang
This paper explores Higher-order Methods (HoM) for Matrix Factorization (MF) and Tensor Factorization (TF) models, which are powerful tools for high dimensional data analysis and feature extraction. Unlike First-order Methods (FoM), which use gradients, HoM use higher-order derivatives of the objective function, which makes them faster but more costly per iteration. We develop efficient and implementable higher-order proximal point methods within the BLUM framework for large-scale problems. We introduce the appropriate objective functions, the algorithm, and the experimental results that demonstrate the advantages of our HoM-based algorithms over FoM-based algorithms for MF and TF models. We show that our HoM-based algorithms have a lower number of iterations with respect to their per-iteration cost than FoM-based algorithms.
{"title":"Inexact higher-order proximal algorithms for tensor factorization","authors":"V. Leplat, A. Phan, A. Ang","doi":"10.1109/SSP53291.2023.10208064","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208064","url":null,"abstract":"This paper explores Higher-order Methods (HoM) for Matrix Factorization (MF) and Tensor Factorization (TF) models, which are powerful tools for high dimensional data analysis and feature extraction. Unlike First-order Methods (FoM), which use gradients, HoM use higher-order derivatives of the objective function, which makes them faster but more costly per iteration. We develop efficient and implementable higher-order proximal point methods within the BLUM framework for large-scale problems. We introduce the appropriate objective functions, the algorithm, and the experimental results that demonstrate the advantages of our HoM-based algorithms over FoM-based algorithms for MF and TF models. We show that our HoM-based algorithms have a lower number of iterations with respect to their per-iteration cost than FoM-based algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126739680","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208057
Natthanan Ruengchaijatuporn, Parin Kittipongdaja, Tagon Sompong, Pasit Jakkrawankul, P. Torvorapanit, N. Chantasrisawad, Wariya Chintanapakdee, Thanisa Tongbai, A. Petchlorlian, Wiroon Sriborrirux, C. Chunharas, O. Putcharoen, E. Chuangsuwanich, S. Sriswasdi
By minimizing human movement and contact, community isolation is an effective containment measure for the COVID-19 pandemic, especially against later strains that cause less severe symptoms. Nonetheless, a significant number of patients who enter community isolation with mild symptoms eventually develop severe pneumonias and require hospitalization. Therefore, the ability to foresee severe cases would be indispensable for managing limited medical resources. Here, we developed a proof-of-concept machine learning model, using daily vital signs data from 1,123 community isolation patients in Bangkok, Thailand, that can predict future hospitalization events up to 3 days in advance with an area under the precision-recall curve of 0.95. The model requires simple inputs, including body temperature, pulse rate, peripheral oxygen saturation, and shortness of breath, that the patients can self-perform and report. Hence, our approach can aid clinicians in providing remote, proactive healthcare service in broad settings
{"title":"AI-assisted monitoring of COVID-19 community isolation in Thailand","authors":"Natthanan Ruengchaijatuporn, Parin Kittipongdaja, Tagon Sompong, Pasit Jakkrawankul, P. Torvorapanit, N. Chantasrisawad, Wariya Chintanapakdee, Thanisa Tongbai, A. Petchlorlian, Wiroon Sriborrirux, C. Chunharas, O. Putcharoen, E. Chuangsuwanich, S. Sriswasdi","doi":"10.1109/SSP53291.2023.10208057","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208057","url":null,"abstract":"By minimizing human movement and contact, community isolation is an effective containment measure for the COVID-19 pandemic, especially against later strains that cause less severe symptoms. Nonetheless, a significant number of patients who enter community isolation with mild symptoms eventually develop severe pneumonias and require hospitalization. Therefore, the ability to foresee severe cases would be indispensable for managing limited medical resources. Here, we developed a proof-of-concept machine learning model, using daily vital signs data from 1,123 community isolation patients in Bangkok, Thailand, that can predict future hospitalization events up to 3 days in advance with an area under the precision-recall curve of 0.95. The model requires simple inputs, including body temperature, pulse rate, peripheral oxygen saturation, and shortness of breath, that the patients can self-perform and report. Hence, our approach can aid clinicians in providing remote, proactive healthcare service in broad settings","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126857972","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207950
Pasit Jakkrawankul, C. Chunharas, Wasan Akarathanawat, P. Vorasayan, Sedthapong Chunamchai, Ploy N. Pratanwanich, P. Punyabukkana, E. Chuangsuwanich
Cardioembolic stroke is a dangerous subtype of ischemic stroke. Patients with this subtype need special treatments to prevent recurrent events that might be fatal. Thus, identifying underlying stroke categories between cardioembolic and non-cardioembolic subtypes is of great importance. We propose a multimodal machine learning model that takes into account basic clinical information and non-contrast computed tomography (CT) images to predict the risk of cardioembolic stroke. The clinical information is not only used to provide additional information for the classification model but also to guide the attention module to extract better image features. Our model achieves a score of 0.840 using the area under the receiver operating characteristic curve (ROC-AUC) metric. Besides the capability to classify the stroke subtypes, the method can provide a heatmap for large infarct localization, which is crucial for stroke diagnosis.
{"title":"Risk Prediction of Cardioembolic Stroke using Clinical Data and Non-contrast CT","authors":"Pasit Jakkrawankul, C. Chunharas, Wasan Akarathanawat, P. Vorasayan, Sedthapong Chunamchai, Ploy N. Pratanwanich, P. Punyabukkana, E. Chuangsuwanich","doi":"10.1109/SSP53291.2023.10207950","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207950","url":null,"abstract":"Cardioembolic stroke is a dangerous subtype of ischemic stroke. Patients with this subtype need special treatments to prevent recurrent events that might be fatal. Thus, identifying underlying stroke categories between cardioembolic and non-cardioembolic subtypes is of great importance. We propose a multimodal machine learning model that takes into account basic clinical information and non-contrast computed tomography (CT) images to predict the risk of cardioembolic stroke. The clinical information is not only used to provide additional information for the classification model but also to guide the attention module to extract better image features. Our model achieves a score of 0.840 using the area under the receiver operating characteristic curve (ROC-AUC) metric. Besides the capability to classify the stroke subtypes, the method can provide a heatmap for large infarct localization, which is crucial for stroke diagnosis.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122411987","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207994
H. Khuong
Relaying communications (RC) in cognitive radio networks (CRNs) can ameliorate transmission coverage and spectrum utilization efficiency. Notwithstanding, the open nature of CRNs hardly assures security against eavesdropping. To overcome the security problem in CRNs, this paper proposes cognitive radios to transmit concurrently desired signal and artificial noise with appropriate power allocation. Apparently, such a proposal causes a trade-off between security and reliability. Moreover, to enhance energy efficiency, relaying operation should use available energy scavenged from radio frequency sources in CRNs. This paper evaluates a security-reliability trade-off of RC in CRNs with energy scavenging and artificial noise (RCiCRNwESaAN), which may benefit from improvement of transmission coverage, spectrum utilization efficiency, energy efficiency, and security capability. To do this, we recommend explicit intercept and outage probability formulas and then corroborate them by computer simulations. Eventually, multiple results are provided to have insights on RCiCRNwESaAN under these realistic operation conditions.
{"title":"Relaying Communications in Cognitive Radio Networks with Energy Scavenging and Artificial Noise: Reliability-Security Trade-off Analysis","authors":"H. Khuong","doi":"10.1109/SSP53291.2023.10207994","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207994","url":null,"abstract":"Relaying communications (RC) in cognitive radio networks (CRNs) can ameliorate transmission coverage and spectrum utilization efficiency. Notwithstanding, the open nature of CRNs hardly assures security against eavesdropping. To overcome the security problem in CRNs, this paper proposes cognitive radios to transmit concurrently desired signal and artificial noise with appropriate power allocation. Apparently, such a proposal causes a trade-off between security and reliability. Moreover, to enhance energy efficiency, relaying operation should use available energy scavenged from radio frequency sources in CRNs. This paper evaluates a security-reliability trade-off of RC in CRNs with energy scavenging and artificial noise (RCiCRNwESaAN), which may benefit from improvement of transmission coverage, spectrum utilization efficiency, energy efficiency, and security capability. To do this, we recommend explicit intercept and outage probability formulas and then corroborate them by computer simulations. Eventually, multiple results are provided to have insights on RCiCRNwESaAN under these realistic operation conditions.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367851","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207969
Omead Pooladzandi, Xilin Li, Yang Gao, L. Theverapperuma
In this paper, we study different Variational Autoencoders (VAEs) decoder distributions in the audio setting to see how to improve magnitude and phase reconstruction on speech resynthesis tasks. We first provide background on the existing decoder distributions, such as Complex Gaussian and Laplace, which are equivalent to a Gamma decoder under certain conditions. We then consider separately modeling speech’s magnitude and phase information to see if we can improve the quality of either component, yielding an improvement in speech resynthesis. Extensive experiments show the Gamma decoder significantly improves magnitude reconstruction and that the von Mises decoder can weakly learn phase information. The novel Gamma decoder outperforms previous approaches, achieving a near-perfect PESQ of 4.4, representing a 42% improvement upon the state-of-the-art IS-VAE and an 86% decrease in the FAD metric. Our results demonstrate the effectiveness of the novel approach, improving the quality of speech resynthesis and compression capacity of VAEs.
{"title":"Exploring the Potential of VAE Decoders for Enhanced Speech Re-Synthesis","authors":"Omead Pooladzandi, Xilin Li, Yang Gao, L. Theverapperuma","doi":"10.1109/SSP53291.2023.10207969","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207969","url":null,"abstract":"In this paper, we study different Variational Autoencoders (VAEs) decoder distributions in the audio setting to see how to improve magnitude and phase reconstruction on speech resynthesis tasks. We first provide background on the existing decoder distributions, such as Complex Gaussian and Laplace, which are equivalent to a Gamma decoder under certain conditions. We then consider separately modeling speech’s magnitude and phase information to see if we can improve the quality of either component, yielding an improvement in speech resynthesis. Extensive experiments show the Gamma decoder significantly improves magnitude reconstruction and that the von Mises decoder can weakly learn phase information. The novel Gamma decoder outperforms previous approaches, achieving a near-perfect PESQ of 4.4, representing a 42% improvement upon the state-of-the-art IS-VAE and an 86% decrease in the FAD metric. Our results demonstrate the effectiveness of the novel approach, improving the quality of speech resynthesis and compression capacity of VAEs.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116197095","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208036
Nguyen Thi Thu Trang, Nguyen Dai Tho, Kien Hoang Dang
The rapid increase in scale and complexity of malware attacks has made traditional signature-based defense approaches less effective due to the inability to detect new forms of malware. Therefore, there is a need for more advanced malware classification methods, which can identify both known and unknown malware efficiently enough, without using signatures. In this paper, we propose a new machine-learning technique for open-world malware classification, using hyperspheres for the succinct representation of different malware families. For each malware sample that needs to be classified, we calculate the probability for it to belong to each hypersphere, then assign the sample to the family having the hypersphere with the highest probability of containing the sample point. Results from experiments have demonstrated the effectiveness of our proposed method on malware datasets for personal computers.
{"title":"A New Method for Malware Classification Using Hyperspheres","authors":"Nguyen Thi Thu Trang, Nguyen Dai Tho, Kien Hoang Dang","doi":"10.1109/SSP53291.2023.10208036","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208036","url":null,"abstract":"The rapid increase in scale and complexity of malware attacks has made traditional signature-based defense approaches less effective due to the inability to detect new forms of malware. Therefore, there is a need for more advanced malware classification methods, which can identify both known and unknown malware efficiently enough, without using signatures. In this paper, we propose a new machine-learning technique for open-world malware classification, using hyperspheres for the succinct representation of different malware families. For each malware sample that needs to be classified, we calculate the probability for it to belong to each hypersphere, then assign the sample to the family having the hypersphere with the highest probability of containing the sample point. Results from experiments have demonstrated the effectiveness of our proposed method on malware datasets for personal computers.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114835344","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208063
F. Albu, L. Tran, S. Radhika, A. Chandrasekar
In this paper, a new algorithm is proposed for the acoustic feedback cancellation for hearing aids. It is based on the affine projection tanh algorithm, combined with a modified practical variable step size and frequency shifting. A modified soft clipping stability detector that controls both the variable step sizes and the frequency shifting is used. It is shown that the proposed variable step size approach that considers the tanh nonlinearities applied to both the preprocessed error signal with the pre-whitening filter and the error signal is beneficial for faster recovery from howling. Dichotomous coordinate descent iterations reduce the numerical complexity of the algorithm. Our experiments indicate that the proposed algorithm outperforms competing methods for incoming speech and music signals.
{"title":"Acoustic Feedback Cancellation using the Variable Step Size Affine Projection Tanh Algorithm","authors":"F. Albu, L. Tran, S. Radhika, A. Chandrasekar","doi":"10.1109/SSP53291.2023.10208063","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208063","url":null,"abstract":"In this paper, a new algorithm is proposed for the acoustic feedback cancellation for hearing aids. It is based on the affine projection tanh algorithm, combined with a modified practical variable step size and frequency shifting. A modified soft clipping stability detector that controls both the variable step sizes and the frequency shifting is used. It is shown that the proposed variable step size approach that considers the tanh nonlinearities applied to both the preprocessed error signal with the pre-whitening filter and the error signal is beneficial for faster recovery from howling. Dichotomous coordinate descent iterations reduce the numerical complexity of the algorithm. Our experiments indicate that the proposed algorithm outperforms competing methods for incoming speech and music signals.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114901928","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208015
L. T. That, S. Dao, T. T. M. Huynh, M. Le
Identifying smoking behavior holds a significant value for informing patients in the early stages. Due to the complexity of this process, the integration of machine learning can provide healthcare professionals with the necessary support to make accurate predictions regarding smoking behavior. To predict if a person smokes or not, the Lasso feature selection method is implemented to identify and select the most relevant features. Subsequently, a set of final subset features is utilized in conjunction with various machine learning classifiers, including LightGBM, XGBoost, Random Forest, and Multilayer Perceptron to perform the prediction task. This study aims to evaluate different classifiers and identify the one with the best performance. After conducting several tests, based on the results obtained, the Random Forest algorithm has outperformed the others, with an accuracy of 84.73%. Additionally, its training speed is significantly faster than other algorithms.
{"title":"A Feature Subset Selection Approach For Predicting Smoking Behaviours","authors":"L. T. That, S. Dao, T. T. M. Huynh, M. Le","doi":"10.1109/SSP53291.2023.10208015","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208015","url":null,"abstract":"Identifying smoking behavior holds a significant value for informing patients in the early stages. Due to the complexity of this process, the integration of machine learning can provide healthcare professionals with the necessary support to make accurate predictions regarding smoking behavior. To predict if a person smokes or not, the Lasso feature selection method is implemented to identify and select the most relevant features. Subsequently, a set of final subset features is utilized in conjunction with various machine learning classifiers, including LightGBM, XGBoost, Random Forest, and Multilayer Perceptron to perform the prediction task. This study aims to evaluate different classifiers and identify the one with the best performance. After conducting several tests, based on the results obtained, the Random Forest algorithm has outperformed the others, with an accuracy of 84.73%. Additionally, its training speed is significantly faster than other algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129622","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207987
Priyanka Maity, Sunaina Khatri, Suraj Srivastava, A. Jagannatham
This work conceives a sparse channel estimation (CE) scheme for multi-user (MU) intelligent reflecting surface (IRS)-aided Terahertz (THz) systems. The proposed framework also incorporates hardware impairments that arise due to manufacturing errors in practical THz systems, such as mutual coupling, irregular antenna spacing, and antenna gain/phase errors. A dictionary learning (DL) algorithm is proposed to learn the best sparsifying dictionary for an IRS-aided THz system in the presence of hardware impairments. The dictionary thus obtained is subsequently employed to leverage the sparsity inherent in the IRS-aided cascaded THz system toward channel estimation (CE). Simulation results corroborate our analytical findings and demonstrate the improved performance with respect to an agnostic scheme that ignores the non-idealities.
{"title":"Dictionary Learning (DL)-based Sparse Cascaded Channel Estimation in IRS-assisted Terahertz MU-SIMO Systems With Hardware Impairments","authors":"Priyanka Maity, Sunaina Khatri, Suraj Srivastava, A. Jagannatham","doi":"10.1109/SSP53291.2023.10207987","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207987","url":null,"abstract":"This work conceives a sparse channel estimation (CE) scheme for multi-user (MU) intelligent reflecting surface (IRS)-aided Terahertz (THz) systems. The proposed framework also incorporates hardware impairments that arise due to manufacturing errors in practical THz systems, such as mutual coupling, irregular antenna spacing, and antenna gain/phase errors. A dictionary learning (DL) algorithm is proposed to learn the best sparsifying dictionary for an IRS-aided THz system in the presence of hardware impairments. The dictionary thus obtained is subsequently employed to leverage the sparsity inherent in the IRS-aided cascaded THz system toward channel estimation (CE). Simulation results corroborate our analytical findings and demonstrate the improved performance with respect to an agnostic scheme that ignores the non-idealities.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125832758","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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