Pub Date : 2023-07-02DOI: 10.1109/SSP53291.2023.10207972
A. Falcon-Caro, M. Frîncu, S. Sanei
In this paper, for the first time a brain connectivity-enhanced diffusion adaptation is introduced and applied to an electroencephalogram (EEG) hyperscanning brain-computer interfacing scenario where the EEGs from two brains are recorded during the performance of a collaborative task. In the diffusion adaptation formulation for modeling, the link between one brain (under rehabilitation) which follows the other (healthy) brain, the combination weights are replaced by the connectivity estimates and the corresponding EEG channels of the healthy subject are used as the targets for the adaptation algorithm. The outcome can be used as a new rehabilitation platform where the state of the patient under rehabilitation depends on how well his/her brain signals can follow the target brain signals.
{"title":"A Diffusion Adaptation Approach to model Brain Responses in an EEG-based Hyperscanning Study","authors":"A. Falcon-Caro, M. Frîncu, S. Sanei","doi":"10.1109/SSP53291.2023.10207972","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207972","url":null,"abstract":"In this paper, for the first time a brain connectivity-enhanced diffusion adaptation is introduced and applied to an electroencephalogram (EEG) hyperscanning brain-computer interfacing scenario where the EEGs from two brains are recorded during the performance of a collaborative task. In the diffusion adaptation formulation for modeling, the link between one brain (under rehabilitation) which follows the other (healthy) brain, the combination weights are replaced by the connectivity estimates and the corresponding EEG channels of the healthy subject are used as the targets for the adaptation algorithm. The outcome can be used as a new rehabilitation platform where the state of the patient under rehabilitation depends on how well his/her brain signals can follow the target brain signals.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"51 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":"134158643","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/SSP53291.2023.10208067
Van-Tam Nguyen, Enzo Tartaglione, Tuan Dinh
Attention and working memory, which are two fundamental components of cognitive basis, can be improved through cognitive training. In addition, thanks to neuroplasticity, neurons are able to adapt quickly to the demands placed on them. By developing new neural networks and strengthening important connections, a cognitive training program can measurably and permanently improve brain activity. In this paper, we present a concept of AIoT based neural decoding and neurofeedback to accelerate cognitive training, the preliminary results and research directions. The proposed concept is to design adequate tiny machine learning to extract the relevant features and characteristics from physiological signals. A tiny ML performs classification or recognition of relevant patterns, based on which the neurofeedback system is appropriately designed for more effective cognitive training.
{"title":"AIoT-based Neural Decoding and Neurofeedback for Accelerated Cognitive Training: Vision, Directions and Preliminary Results","authors":"Van-Tam Nguyen, Enzo Tartaglione, Tuan Dinh","doi":"10.1109/SSP53291.2023.10208067","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208067","url":null,"abstract":"Attention and working memory, which are two fundamental components of cognitive basis, can be improved through cognitive training. In addition, thanks to neuroplasticity, neurons are able to adapt quickly to the demands placed on them. By developing new neural networks and strengthening important connections, a cognitive training program can measurably and permanently improve brain activity. In this paper, we present a concept of AIoT based neural decoding and neurofeedback to accelerate cognitive training, the preliminary results and research directions. The proposed concept is to design adequate tiny machine learning to extract the relevant features and characteristics from physiological signals. A tiny ML performs classification or recognition of relevant patterns, based on which the neurofeedback system is appropriately designed for more effective cognitive training.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"8 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":"133167996","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/SSP53291.2023.10208071
Madison Lee, O. Haddadin, T. Javidi
In this paper, we consider the problem of black-box function optimization. We propose an FFT-based algorithm that adaptively updates the parameters of a bandlimited Gaussian process surrogate model for the function. Our algorithm uses these parameters to construct approximate upper confidence bounds that determine its sampling behavior. We show that when the underlying function can be extended as a periodic function whose bandwidth is sufficiently small relative to the evaluation budget, our models converge to a perfect reconstruction, allowing our algorithm to recover the true optimizer. For periodic bandlimited function spaces, our algorithm has reduced complexity compared to traditional GP-UCB-based algorithms and demonstrates improved robustness.
{"title":"FFT-Based Approximations for Black-Box Optimization","authors":"Madison Lee, O. Haddadin, T. Javidi","doi":"10.1109/SSP53291.2023.10208071","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208071","url":null,"abstract":"In this paper, we consider the problem of black-box function optimization. We propose an FFT-based algorithm that adaptively updates the parameters of a bandlimited Gaussian process surrogate model for the function. Our algorithm uses these parameters to construct approximate upper confidence bounds that determine its sampling behavior. We show that when the underlying function can be extended as a periodic function whose bandwidth is sufficiently small relative to the evaluation budget, our models converge to a perfect reconstruction, allowing our algorithm to recover the true optimizer. For periodic bandlimited function spaces, our algorithm has reduced complexity compared to traditional GP-UCB-based algorithms and demonstrates improved robustness.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"20 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":"133913090","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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