Pub Date : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340146
D. Shehada, A. Gawanmeh, Claude Fachkha, H. A. Damis
Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.
{"title":"Performance Evaluation of a Lightweight IoT Authentication Protocol","authors":"D. Shehada, A. Gawanmeh, Claude Fachkha, H. A. Damis","doi":"10.1109/ICSPIS51252.2020.9340146","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340146","url":null,"abstract":"Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128472160","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340132
W. A. Nassan, Talal Bonny, Dr. Abdullatif Baba
Since 1993, chaos-based cryptosystems were widely discussed in the literature for the encryption of different types of row data. In recent years, as the encryption methods have been considerably improved due to the notable development of communication equipment; a large amount of secure communication system-based researches has been published. In this paper, we present a new chaos-based secure communication system relying on three levels of cryptography that combines a conventional encryption algorithm and chaos masking technique. Furthermore, to enhance the security level, three different types of attractors that were generated from a unified hyperchaotic oscillator are used in the proposed communication system. This new chaotic secure communication scheme is implemented to transmit a voice signal. Finally, to demonstrate the efficiency of the suggested communication system, several testing methods are employed to prove the unpredictability of the signal carrier, by the attackers in both domains, time, and frequency.
{"title":"A New Chaos-Based Cryptoystem for Voice Encryption","authors":"W. A. Nassan, Talal Bonny, Dr. Abdullatif Baba","doi":"10.1109/ICSPIS51252.2020.9340132","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340132","url":null,"abstract":"Since 1993, chaos-based cryptosystems were widely discussed in the literature for the encryption of different types of row data. In recent years, as the encryption methods have been considerably improved due to the notable development of communication equipment; a large amount of secure communication system-based researches has been published. In this paper, we present a new chaos-based secure communication system relying on three levels of cryptography that combines a conventional encryption algorithm and chaos masking technique. Furthermore, to enhance the security level, three different types of attractors that were generated from a unified hyperchaotic oscillator are used in the proposed communication system. This new chaotic secure communication scheme is implemented to transmit a voice signal. Finally, to demonstrate the efficiency of the suggested communication system, several testing methods are employed to prove the unpredictability of the signal carrier, by the attackers in both domains, time, and frequency.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"303 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104367","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340145
A. Panthakkan, M. AnzarS., S. Al-Mansoori, Hussain Al-Ahmad
Current research aims at the efficient prediction of COVID-19 (+) by employing advanced machine intelligence techniques by means of lung X-rays. In this paper, we have presented the promising VGG16 transfer learning model for the accurate and faster diagnosis of COVID-19 (+). The system provides a binary classification of the lung X-ray image into COVID-19 (+) and Normal. The effectiveness of the system being proposed is appraised by means of the performance metrics such as accuracy, precision, recall, and f1 score. Experiments were performed with 2000 X-ray specimens. For the two-class classification of the reported sample size, the proposed VGG16 model provides an outstanding recognition accuracy of 99.5%, which is loftier to all the contemporary methods provided in the literature. The suggested approach is extremely efficient and precise, for that reason, it can be used to aid and support radiologists and healthcare professionals to identify COVID-19 (+) utilizing the lung X-rays.
{"title":"Accurate Prediction of COVID-19 (+) Using AI Deep VGG16 Model","authors":"A. Panthakkan, M. AnzarS., S. Al-Mansoori, Hussain Al-Ahmad","doi":"10.1109/ICSPIS51252.2020.9340145","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340145","url":null,"abstract":"Current research aims at the efficient prediction of COVID-19 (+) by employing advanced machine intelligence techniques by means of lung X-rays. In this paper, we have presented the promising VGG16 transfer learning model for the accurate and faster diagnosis of COVID-19 (+). The system provides a binary classification of the lung X-ray image into COVID-19 (+) and Normal. The effectiveness of the system being proposed is appraised by means of the performance metrics such as accuracy, precision, recall, and f1 score. Experiments were performed with 2000 X-ray specimens. For the two-class classification of the reported sample size, the proposed VGG16 model provides an outstanding recognition accuracy of 99.5%, which is loftier to all the contemporary methods provided in the literature. The suggested approach is extremely efficient and precise, for that reason, it can be used to aid and support radiologists and healthcare professionals to identify COVID-19 (+) utilizing the lung X-rays.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133911785","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340139
A. Darya, S. Abdallah
In this work, we propose a low-overhead channel estimator for diffusive molecular communication systems. Since diffusive molecular communication systems rely on the random propagation of molecules as a means of communication, they may potentially suffer from inter-symbol-interference in addition to the noise introduced by the channel and external sources. Hence, acquiring an accurate estimate of the channel impulse response is essential for signal equalization and detection. The proposed low-overhead estimator utilizes a minimal number of pilot symbols, thus reducing the training overhead by up to 95%. The computational complexity of the proposed estimator is comparable to that of pilot-based least-squared estimators in related works. Consequently, the proposed low-overhead channel estimator is efficient in terms of throughput and complexity in a way that appeals to the majority of prospective diffusive molecular communication systems.
{"title":"Low-Overhead Channel Estimation for Diffusive Molecular Communication","authors":"A. Darya, S. Abdallah","doi":"10.1109/ICSPIS51252.2020.9340139","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340139","url":null,"abstract":"In this work, we propose a low-overhead channel estimator for diffusive molecular communication systems. Since diffusive molecular communication systems rely on the random propagation of molecules as a means of communication, they may potentially suffer from inter-symbol-interference in addition to the noise introduced by the channel and external sources. Hence, acquiring an accurate estimate of the channel impulse response is essential for signal equalization and detection. The proposed low-overhead estimator utilizes a minimal number of pilot symbols, thus reducing the training overhead by up to 95%. The computational complexity of the proposed estimator is comparable to that of pilot-based least-squared estimators in related works. Consequently, the proposed low-overhead channel estimator is efficient in terms of throughput and complexity in a way that appeals to the majority of prospective diffusive molecular communication systems.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977065","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340150
Shehanaz Amiroon, Claude Fachkha
The Internet of Things (IoT) has become an indispensable technology fragment diurnal that has consolidated all the discrete systems and data into a coherent and interconnected process. With the advent of cutting-edge technologies providing meticulous solutions, the digital world still lacks the existence of propitious capabilities for IoT digital forensics. As such, an exceptional increase in the number of electronic devices, enormous collection and consumption of data has necessitated the need for IoT forensics when it comes to cybercrimes. In this paper, we provide a summary of the difference between IoT and conventional digital forensic processes. In addition, we list three categories of IoT-related computer crimes. Furthermore, we group several contributions and case studies that are IoT-specific when it comes to digital investigations. Our results reveal the gap and pinpoint initiatives towards smart equipment forensics such as smartwatches, smart TVs, among others.
{"title":"Digital Forensics and Investigations of the Internet of Things: A Short Survey","authors":"Shehanaz Amiroon, Claude Fachkha","doi":"10.1109/ICSPIS51252.2020.9340150","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340150","url":null,"abstract":"The Internet of Things (IoT) has become an indispensable technology fragment diurnal that has consolidated all the discrete systems and data into a coherent and interconnected process. With the advent of cutting-edge technologies providing meticulous solutions, the digital world still lacks the existence of propitious capabilities for IoT digital forensics. As such, an exceptional increase in the number of electronic devices, enormous collection and consumption of data has necessitated the need for IoT forensics when it comes to cybercrimes. In this paper, we provide a summary of the difference between IoT and conventional digital forensic processes. In addition, we list three categories of IoT-related computer crimes. Furthermore, we group several contributions and case studies that are IoT-specific when it comes to digital investigations. Our results reveal the gap and pinpoint initiatives towards smart equipment forensics such as smartwatches, smart TVs, among others.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124974335","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340152
H. A. Damis, D. Shehada, Claude Fachkha, A. Gawanmeh, J. Al-Karaki
The use of Automatic Dependent Surveillance - Broadcast (ADS-B) for aircraft tracking and flight management operations is widely used today. However, ADS-B is prone to several cyber-security threats due to the lack of data authentication and encryption. Recently, Blockchain has emerged as new paradigm that can provide promising solutions in decentralized systems. Furthermore, software containers and Microservices facilitate the scaling of Blockchain implementations within cloud computing environment. When fused together, these technologies could help improve Air Traffic Control (ATC) processing of ADS-B data. In this paper, a Blockchain implementation within a Microservices framework for ADS-B data verification is proposed. The aim of this work is to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers. The proposed framework could mitigate ADS- B security issues of message spoofing and anomalous traffic data. and hence minimize the cost of ATC infrastructure by throughout third-party support.
{"title":"A Microservices Architecture for ADS-B Data Security Using Blockchain","authors":"H. A. Damis, D. Shehada, Claude Fachkha, A. Gawanmeh, J. Al-Karaki","doi":"10.1109/ICSPIS51252.2020.9340152","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340152","url":null,"abstract":"The use of Automatic Dependent Surveillance - Broadcast (ADS-B) for aircraft tracking and flight management operations is widely used today. However, ADS-B is prone to several cyber-security threats due to the lack of data authentication and encryption. Recently, Blockchain has emerged as new paradigm that can provide promising solutions in decentralized systems. Furthermore, software containers and Microservices facilitate the scaling of Blockchain implementations within cloud computing environment. When fused together, these technologies could help improve Air Traffic Control (ATC) processing of ADS-B data. In this paper, a Blockchain implementation within a Microservices framework for ADS-B data verification is proposed. The aim of this work is to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers. The proposed framework could mitigate ADS- B security issues of message spoofing and anomalous traffic data. and hence minimize the cost of ATC infrastructure by throughout third-party support.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122477843","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340133
Ria Gupta, Tehreem Fatima Zaidi, O. Farooq
Sleep Apnea is a major sleep disorder which leads to a partial or complete stopping in breathing for a short duration of time during sleep. The out-turn of this is extreme daytime drowsiness. Among different polysomnographic signals, Electroencephalogram (EEG) signal reflects the electrical activity of the brain by sensing and recording the brain's activities. Thus, EEG serves as a valuable information source for detecting the sleep apnea events. In this research, an efficient automatic method is proposed for differentiating apnea and non-apnea events of an apnea patient which is a very burdensome task if performed manually. The features energy, entropy, mean absolute deviation and kurtosis, extracted from five sub-bands, provide better accuracy, sensitivity and specificity as compared to other recently published studies. The performance evaluation of the proposed technique is carried out using the publicly available Physionet dataset. The highest classification accuracy of 95.10%, sensitivity of 93.20% and specificity of 96.80% is achieved by using Ensemble decision tree methods using the bagging technique. The novel method proposed in this research offers superior results in terms of accuracy, sensitivity and specificity as compared to the recently published work on the same database.
{"title":"Automatic Detection of Sleep Apnea Using Sub-Band Features from EEG Signals","authors":"Ria Gupta, Tehreem Fatima Zaidi, O. Farooq","doi":"10.1109/ICSPIS51252.2020.9340133","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340133","url":null,"abstract":"Sleep Apnea is a major sleep disorder which leads to a partial or complete stopping in breathing for a short duration of time during sleep. The out-turn of this is extreme daytime drowsiness. Among different polysomnographic signals, Electroencephalogram (EEG) signal reflects the electrical activity of the brain by sensing and recording the brain's activities. Thus, EEG serves as a valuable information source for detecting the sleep apnea events. In this research, an efficient automatic method is proposed for differentiating apnea and non-apnea events of an apnea patient which is a very burdensome task if performed manually. The features energy, entropy, mean absolute deviation and kurtosis, extracted from five sub-bands, provide better accuracy, sensitivity and specificity as compared to other recently published studies. The performance evaluation of the proposed technique is carried out using the publicly available Physionet dataset. The highest classification accuracy of 95.10%, sensitivity of 93.20% and specificity of 96.80% is achieved by using Ensemble decision tree methods using the bagging technique. The novel method proposed in this research offers superior results in terms of accuracy, sensitivity and specificity as compared to the recently published work on the same database.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122326119","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340143
N. Aburaed, A. Panthakkan, M. Al-Saad, S. Amin, W. Mansoor
Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against Machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer Learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss.
{"title":"Skin Cancer Classification Model Based on VGG 19 and Transfer Learning","authors":"N. Aburaed, A. Panthakkan, M. Al-Saad, S. Amin, W. Mansoor","doi":"10.1109/ICSPIS51252.2020.9340143","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340143","url":null,"abstract":"Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against Machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer Learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127428158","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 : 2020-11-25DOI: 10.1109/icspis51252.2020.9340147
Abhirup Khanna, Ananya Bansal, A. Agarwal, P. Maheshwari
In this day and age of social media, Twitter plays an important role when it comes to election campaigning. Twitter has been seemingly popular among leaders of the western world, but the advent of accessible and affordable internet connectivity had led to its inroads in the India electoral ecosystem. Major political parties have their twitter accounts with millions of active followers. Government departments and respective ministers are now using Twitter as a means of publicizing public interest schemes among their followers. Twitter is being used as a tool for addressing current followers and appealing to potential future voters. In this paper, we present a popularity analysis between two national parties, i.e. BJP and INC. The work discusses the extent of positive popularity each party enjoys in various cities in India. Twitter data is harvested for analysis from seventeen different cities spread across four different zones in India. We examine the growth of BJP and its prominence in certain social groups. The paper talks about the rise of Prime Minister Narendra Modi and his emergence as a nationwide leader. Finally, we present positive takeaways from our analysis that may interest any political pundit of Indian democracy.
{"title":"Reading Political Sentiment and Mood of the Electorate Through Twitter Data","authors":"Abhirup Khanna, Ananya Bansal, A. Agarwal, P. Maheshwari","doi":"10.1109/icspis51252.2020.9340147","DOIUrl":"https://doi.org/10.1109/icspis51252.2020.9340147","url":null,"abstract":"In this day and age of social media, Twitter plays an important role when it comes to election campaigning. Twitter has been seemingly popular among leaders of the western world, but the advent of accessible and affordable internet connectivity had led to its inroads in the India electoral ecosystem. Major political parties have their twitter accounts with millions of active followers. Government departments and respective ministers are now using Twitter as a means of publicizing public interest schemes among their followers. Twitter is being used as a tool for addressing current followers and appealing to potential future voters. In this paper, we present a popularity analysis between two national parties, i.e. BJP and INC. The work discusses the extent of positive popularity each party enjoys in various cities in India. Twitter data is harvested for analysis from seventeen different cities spread across four different zones in India. We examine the growth of BJP and its prominence in certain social groups. The paper talks about the rise of Prime Minister Narendra Modi and his emergence as a nationwide leader. Finally, we present positive takeaways from our analysis that may interest any political pundit of Indian democracy.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739078","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 : 2020-11-25DOI: 10.1109/ICSPIS51252.2020.9340148
T. Nazzal, Husameldin Mukhtar
In this paper, we study the impact of channel estimation errors on the performance of time-domain interleaved orthogonal frequency division multiplexing (OFDM) systems. The time domain interleaving (TDI) is known to enhance the error performance of OFDM significantly. Nevertheless, previous work analyzed TDI assuming perfect channel estimation. In practical communication systems, channel coefficients may be inaccurately estimated. Hence, the performance of TDI systems is evaluated in this work in the presence of channel estimation errors. The obtained results show that TDI with large interleaver length provides better performance than OFDM in the presence of imperfect channel estimation. However, when the channel estimation error is large enough, TDI performance significantly deteriorates as well, particularly at high SNR values.
{"title":"Impact of Channel Estimation Error on the Performance of Time-Domain Interleaved OFDM Systems","authors":"T. Nazzal, Husameldin Mukhtar","doi":"10.1109/ICSPIS51252.2020.9340148","DOIUrl":"https://doi.org/10.1109/ICSPIS51252.2020.9340148","url":null,"abstract":"In this paper, we study the impact of channel estimation errors on the performance of time-domain interleaved orthogonal frequency division multiplexing (OFDM) systems. The time domain interleaving (TDI) is known to enhance the error performance of OFDM significantly. Nevertheless, previous work analyzed TDI assuming perfect channel estimation. In practical communication systems, channel coefficients may be inaccurately estimated. Hence, the performance of TDI systems is evaluated in this work in the presence of channel estimation errors. The obtained results show that TDI with large interleaver length provides better performance than OFDM in the presence of imperfect channel estimation. However, when the channel estimation error is large enough, TDI performance significantly deteriorates as well, particularly at high SNR values.","PeriodicalId":373750,"journal":{"name":"2020 3rd International Conference on Signal Processing and Information Security (ICSPIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122223","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：