Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9942987
Xuan Thanh Pham, X. P. Tran, Khac Vu Nguyen, Van Thai Le, D. Pham, Manh Kha Hoang
Electrical neural stimulation (ENS) is widely used for implantable applications to convey information to nervous tissue. However, ENS will generate large common (CM) artifacts at the electrode tissue, leading to saturate the traditional biopotential amplifier. This paper presents a low power low noise bio chopper amplifier (BiCA) for the recording of biopotential signals. The proposed noise-efficient common mode cancelation (N-CMC) loop helps BiCA handle 650 mVpp CM artifact and avoid its noise contribution. Moreover, N-CMC helps BiCA improving the signal-noise-ratio from 12.8 to 49 dB. Beside, the proposed BiCA also uses a DC servo loop (DSL) and a ripple suppression loop (RSL) to address the electrode offset (EOS) and intenal offset (VOS), respectively. The proposed BiCA implemented in a 180 nm CMOS technology occupies only 0.11 mm2, The simulation results of the BiCA show an input referred noise of 2.73 µVrms. A common-mode rejection ratio (CMRR) and a power rejection ratio (PSRR) are 133 and 129 dB, respectively, at 50 Hz. The total current consumption of BiCA is 1.9 µA from a 1 V supply.
{"title":"A 1.9 µW 127 n V/ √Hz Bio Chopper Amplifier Using a Noise-Efficient Common Mode Cancelation Loop","authors":"Xuan Thanh Pham, X. P. Tran, Khac Vu Nguyen, Van Thai Le, D. Pham, Manh Kha Hoang","doi":"10.1109/ATC55345.2022.9942987","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942987","url":null,"abstract":"Electrical neural stimulation (ENS) is widely used for implantable applications to convey information to nervous tissue. However, ENS will generate large common (CM) artifacts at the electrode tissue, leading to saturate the traditional biopotential amplifier. This paper presents a low power low noise bio chopper amplifier (BiCA) for the recording of biopotential signals. The proposed noise-efficient common mode cancelation (N-CMC) loop helps BiCA handle 650 mVpp CM artifact and avoid its noise contribution. Moreover, N-CMC helps BiCA improving the signal-noise-ratio from 12.8 to 49 dB. Beside, the proposed BiCA also uses a DC servo loop (DSL) and a ripple suppression loop (RSL) to address the electrode offset (EOS) and intenal offset (VOS), respectively. The proposed BiCA implemented in a 180 nm CMOS technology occupies only 0.11 mm2, The simulation results of the BiCA show an input referred noise of 2.73 µVrms. A common-mode rejection ratio (CMRR) and a power rejection ratio (PSRR) are 133 and 129 dB, respectively, at 50 Hz. The total current consumption of BiCA is 1.9 µA from a 1 V supply.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943003
Duy T. Nguyen, Thanh-Hai Le, Van‐Phuc Hoang, Van-Sang Doan, Duy-Thang Thai
Direction of arrival (DOA) estimation plays a crucial role in radio signal surveillance and reconnaissance systems because it provides spatial information to localize radiated signal sources. Conventional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant technique (ESPRIT), are very sensitive to defects of antenna arrays that reduce the accuracy of estimated DOA in real applications. To mitigate this issue, an auto-encoder based on U-Net is proposed to transfer the imperfect covariance matrix to a new one; then, the MUSIC algorithm is applied to the new covariance matrix to estimate the DOAs of incoming signals. The proposed approach is investigated through simulation for a uniform linear array of eight elements with an inter-element space of half-wavelength. The simulation results indicate that our proposed method achieves a good performance in terms of DOA estimation accuracy. In comparison, the proposed model has outperformed the other models, such as conventional MUSIC, ESPRIT, and two other deep neural networks.
{"title":"Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array","authors":"Duy T. Nguyen, Thanh-Hai Le, Van‐Phuc Hoang, Van-Sang Doan, Duy-Thang Thai","doi":"10.1109/ATC55345.2022.9943003","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943003","url":null,"abstract":"Direction of arrival (DOA) estimation plays a crucial role in radio signal surveillance and reconnaissance systems because it provides spatial information to localize radiated signal sources. Conventional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant technique (ESPRIT), are very sensitive to defects of antenna arrays that reduce the accuracy of estimated DOA in real applications. To mitigate this issue, an auto-encoder based on U-Net is proposed to transfer the imperfect covariance matrix to a new one; then, the MUSIC algorithm is applied to the new covariance matrix to estimate the DOAs of incoming signals. The proposed approach is investigated through simulation for a uniform linear array of eight elements with an inter-element space of half-wavelength. The simulation results indicate that our proposed method achieves a good performance in terms of DOA estimation accuracy. In comparison, the proposed model has outperformed the other models, such as conventional MUSIC, ESPRIT, and two other deep neural networks.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943031
Nhat Do Van, Luong Duc Huy, Can Quang Truong, Bùi Trung Ninh, Dinh Thi Thai Mai
In this research, we will provide a brief overview of the SDN architecture and how DDoS can drain a controller's resources. We will then introduce a method to detect the attack based on using statistical analysis with a dynamic threshold value that changes over time, depending on the traffic over a network. Survey and simulation results show that our solution is completely feasible to quickly detect DDoS attacks as well as help improve reliability when compared to other methods using static threshold values.
{"title":"Applying Dynamic Threshold in SDN to Detect DDoS Attacks","authors":"Nhat Do Van, Luong Duc Huy, Can Quang Truong, Bùi Trung Ninh, Dinh Thi Thai Mai","doi":"10.1109/ATC55345.2022.9943031","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943031","url":null,"abstract":"In this research, we will provide a brief overview of the SDN architecture and how DDoS can drain a controller's resources. We will then introduce a method to detect the attack based on using statistical analysis with a dynamic threshold value that changes over time, depending on the traffic over a network. Survey and simulation results show that our solution is completely feasible to quickly detect DDoS attacks as well as help improve reliability when compared to other methods using static threshold values.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943046
N. Nguyen, MinhNghia Pham, Vannhu Le, Dung DuongQuoc, Van-Sang Doan
Falls are the leading cause of injury and death in people over 65. Timely detection and warning of the fall risks of humans, especially the elderly, while performing daily living activities are vitally necessary. Therefore, this paper proposes a Dense-Inception Neural Network (DINN) to classify falls among 11 human activities based on micro-Doppler signatures. The network's hyper-parameters are analyzed and fine-tuned through experiments with the simulated dataset from Simhumalator software to choose the most optimal network model. As a result, the proposed model with 24 filters achieves a good balance between prediction time and classification accuracy performance. Moreover, the proposed model's results remarkably outperform when compared with four other networks with the same input dataset due to the dense-inception structure.
{"title":"Micro-Doppler signatures based human activity classification using Dense-Inception Neural Network","authors":"N. Nguyen, MinhNghia Pham, Vannhu Le, Dung DuongQuoc, Van-Sang Doan","doi":"10.1109/ATC55345.2022.9943046","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943046","url":null,"abstract":"Falls are the leading cause of injury and death in people over 65. Timely detection and warning of the fall risks of humans, especially the elderly, while performing daily living activities are vitally necessary. Therefore, this paper proposes a Dense-Inception Neural Network (DINN) to classify falls among 11 human activities based on micro-Doppler signatures. The network's hyper-parameters are analyzed and fine-tuned through experiments with the simulated dataset from Simhumalator software to choose the most optimal network model. As a result, the proposed model with 24 filters achieves a good balance between prediction time and classification accuracy performance. Moreover, the proposed model's results remarkably outperform when compared with four other networks with the same input dataset due to the dense-inception structure.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126917328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943024
Ngoc-Tuan Dol, Phu-Cuong Le, Van‐Phuc Hoang, Van-Sang Doan, Hoai Giang Nguyen, C. Pham
Differential Deep Learning Analysis (DDLA) is the first side-channel analysis (SCA) attack using deep learning (DL) in non-profiled scenarios. However, DDLA requires many training processes to distinguish the correct key. In this paper, we propose a novel SCA technique using multi-output multi-loss neural networks, which can predict all possible hypothesis keys simultaneously in a short time of the training process. Specifically, a multi-output classification (MOC) model and a multi-output regression (MOR) model are introduced. Especially, we first suggest using identity labeling for MOR model to determine the trend of the training metric for each hypothesis key in the non-profiled SCA scenario. As a result, the correct key can be distinguished easily. The efficiency of proposed model is clarified on different SCA-protected schemes, such as masking and combined hiding-masking countermeasure methods. Significantly, our approach remarkably outperforms the DDLA model and parallel network in terms of the execution time and the success rate. In addition, by using shared layers, the proposed model achieves a higher success rate of at least 25 % in the case of combined hiding-masking countermeasure.
{"title":"MO-DLSCA: Deep Learning Based Non-profiled Side Channel Analysis Using Multi-output Neural Networks","authors":"Ngoc-Tuan Dol, Phu-Cuong Le, Van‐Phuc Hoang, Van-Sang Doan, Hoai Giang Nguyen, C. Pham","doi":"10.1109/ATC55345.2022.9943024","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943024","url":null,"abstract":"Differential Deep Learning Analysis (DDLA) is the first side-channel analysis (SCA) attack using deep learning (DL) in non-profiled scenarios. However, DDLA requires many training processes to distinguish the correct key. In this paper, we propose a novel SCA technique using multi-output multi-loss neural networks, which can predict all possible hypothesis keys simultaneously in a short time of the training process. Specifically, a multi-output classification (MOC) model and a multi-output regression (MOR) model are introduced. Especially, we first suggest using identity labeling for MOR model to determine the trend of the training metric for each hypothesis key in the non-profiled SCA scenario. As a result, the correct key can be distinguished easily. The efficiency of proposed model is clarified on different SCA-protected schemes, such as masking and combined hiding-masking countermeasure methods. Significantly, our approach remarkably outperforms the DDLA model and parallel network in terms of the execution time and the success rate. In addition, by using shared layers, the proposed model achieves a higher success rate of at least 25 % in the case of combined hiding-masking countermeasure.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116983722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943018
H. Ta, Thanh Lam Cao, Khuong Ho‐Van
We propose a new artificial noise (AN)-aided secure communication scheme that is robust to the eavesdropper's location and number of antennas to achieve a high zero-outage secrecy capacity. Unlike the traditional AN-aided secure communication schemes, the signal-to-interference-plus-noise ratio at the eavesdropper does not grow with the number of antennas in the proposed scheme. Hence, the eavesdropper cannot remove the AN no matter how many antennas it may have. We derive the secrecy outage probability and the zero-outage secrecy capacity of the proposed scheme in the slow Rayleigh fading channel and investigate the effect of the location and the number of antennas at the eavesdroppers.
{"title":"Achievable Zero-Outage Secrecy Capacity Against Eavesdroppers with Unlimited Antennas and Arbitrary Location","authors":"H. Ta, Thanh Lam Cao, Khuong Ho‐Van","doi":"10.1109/ATC55345.2022.9943018","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943018","url":null,"abstract":"We propose a new artificial noise (AN)-aided secure communication scheme that is robust to the eavesdropper's location and number of antennas to achieve a high zero-outage secrecy capacity. Unlike the traditional AN-aided secure communication schemes, the signal-to-interference-plus-noise ratio at the eavesdropper does not grow with the number of antennas in the proposed scheme. Hence, the eavesdropper cannot remove the AN no matter how many antennas it may have. We derive the secrecy outage probability and the zero-outage secrecy capacity of the proposed scheme in the slow Rayleigh fading channel and investigate the effect of the location and the number of antennas at the eavesdroppers.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117253023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9942977
Manh Linh Nguyen, T. B. Nguyen, Duc Phu Phung, Van Long Do
This paper deals with the direction-of-arrival (DOA) estimation problem in three-dimensional (3D) space in low signal-to-noise ratio (SNR) environments. The proposed solution utilizes an Isolog-3D antenna with advanced signal processing techniques for solving direction finding in 3D space (DF-3D) problem. Standard DF-3D solutions have been shown to work ineffectively in low SNR scenarios due to large power estimation errors. For solving this problem, we propose in this paper a simple but effective method for reducing DOA estimation errors. The reduction of DOA estimation errors is obtained by combining an amplitude calibration algorithm with the standard Kalman filter. The amplitude calibration helps in removing bias errors in power estimation while the Kalman filter alleviates random noises in DOA estimation. Theoretical and simulation results have been shown for demonstrating the effectiveness of the proposed solution.
{"title":"Three-Dimensional Direction Finding of Radio Sources in Low SNR Environments","authors":"Manh Linh Nguyen, T. B. Nguyen, Duc Phu Phung, Van Long Do","doi":"10.1109/ATC55345.2022.9942977","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942977","url":null,"abstract":"This paper deals with the direction-of-arrival (DOA) estimation problem in three-dimensional (3D) space in low signal-to-noise ratio (SNR) environments. The proposed solution utilizes an Isolog-3D antenna with advanced signal processing techniques for solving direction finding in 3D space (DF-3D) problem. Standard DF-3D solutions have been shown to work ineffectively in low SNR scenarios due to large power estimation errors. For solving this problem, we propose in this paper a simple but effective method for reducing DOA estimation errors. The reduction of DOA estimation errors is obtained by combining an amplitude calibration algorithm with the standard Kalman filter. The amplitude calibration helps in removing bias errors in power estimation while the Kalman filter alleviates random noises in DOA estimation. Theoretical and simulation results have been shown for demonstrating the effectiveness of the proposed solution.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123650531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9943027
Bui Anh Duc, T. Hoang, Nguyen Thu Phuong, X. Tran, Pham Thanh Hiep
A cell-free (CF) technology is gradually asserting its outstanding advantages by many researches, and it is viewed as a viable technology for use in 6G wireless networks. In this correspondence paper, we examine the performance of uplink CF multiple aerial base stations (ABSs) communication systems where ABSs are described as unmanned aerial vehicles (DAVs) mounted base stations. ABSs are configured with multiple antennas and stochastic distribution in a specific area to serve multiple ground users simultaneously. ABSs estimate the channels during the uplink training stage and then detect data symbols based on the estimated channels. To improve the overall performance of the uplink CF multi-ABS system, the optimization method for data transmission power is proposed. Furthermore, the closed-form of uplink achievable rate is derived based on the matched filtering technique and sequence of linear programs for numerical evaluation. The proposed optimization data transmission power is evaluated while changing several system parameters, such as the number of users, the number of ABSs and pilot sequence length. Our simulation findings demonstrate that the performance of the optimized system is superior to that of the non-optimized system.
{"title":"Optimizing Power for Data Transmissions in Uplink Cell-Free Multi-ABSs Communication Systems","authors":"Bui Anh Duc, T. Hoang, Nguyen Thu Phuong, X. Tran, Pham Thanh Hiep","doi":"10.1109/ATC55345.2022.9943027","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943027","url":null,"abstract":"A cell-free (CF) technology is gradually asserting its outstanding advantages by many researches, and it is viewed as a viable technology for use in 6G wireless networks. In this correspondence paper, we examine the performance of uplink CF multiple aerial base stations (ABSs) communication systems where ABSs are described as unmanned aerial vehicles (DAVs) mounted base stations. ABSs are configured with multiple antennas and stochastic distribution in a specific area to serve multiple ground users simultaneously. ABSs estimate the channels during the uplink training stage and then detect data symbols based on the estimated channels. To improve the overall performance of the uplink CF multi-ABS system, the optimization method for data transmission power is proposed. Furthermore, the closed-form of uplink achievable rate is derived based on the matched filtering technique and sequence of linear programs for numerical evaluation. The proposed optimization data transmission power is evaluated while changing several system parameters, such as the number of users, the number of ABSs and pilot sequence length. Our simulation findings demonstrate that the performance of the optimized system is superior to that of the non-optimized system.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128813908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9942997
T. Adalı
In many fields today, such as neuroscience, remote sensing, computational social science, and physical sciences, multiple sets of data are readily available. Matrix and tensor factorizations enable joint analysis, i.e., fusion, of these multiple datasets such that they can fully interact and inform each other while also minimizing the assumptions placed on their inherent relationships. A key advantage of these methods is the direct interpretability of their results. This talk presents an overview of models based on independent component analysis (ICA), and its generalization to multiple datasets, independent vector analysis (IVA) with examples using neuroimaging data. A number of important challenges and future directions of research are addressed for solutions using not only ICA and IVA but also tensors and other matrix factorizations.
{"title":"Data Fusion Using Independent Vector Analysis: Solutions, Challenges, and Opportunities","authors":"T. Adalı","doi":"10.1109/ATC55345.2022.9942997","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942997","url":null,"abstract":"In many fields today, such as neuroscience, remote sensing, computational social science, and physical sciences, multiple sets of data are readily available. Matrix and tensor factorizations enable joint analysis, i.e., fusion, of these multiple datasets such that they can fully interact and inform each other while also minimizing the assumptions placed on their inherent relationships. A key advantage of these methods is the direct interpretability of their results. This talk presents an overview of models based on independent component analysis (ICA), and its generalization to multiple datasets, independent vector analysis (IVA) with examples using neuroimaging data. A number of important challenges and future directions of research are addressed for solutions using not only ICA and IVA but also tensors and other matrix factorizations.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116277547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.1109/atc55345.2022.9943009
Patrick Tsie
The presentation will share the latest update for 3GPP on 5G standardization status. It also provides the new 5G-Advanced features, that expands the 5G technology foundations for coverage, mobility, power, reliability, etc. in order to broaden 5G's reach to new use cases, deployments, and network topologies. Those features will fuel the 5G Advanced evolution towards 6G in the future.
{"title":"Leading 5G evolution towards 6G","authors":"Patrick Tsie","doi":"10.1109/atc55345.2022.9943009","DOIUrl":"https://doi.org/10.1109/atc55345.2022.9943009","url":null,"abstract":"The presentation will share the latest update for 3GPP on 5G standardization status. It also provides the new 5G-Advanced features, that expands the 5G technology foundations for coverage, mobility, power, reliability, etc. in order to broaden 5G's reach to new use cases, deployments, and network topologies. Those features will fuel the 5G Advanced evolution towards 6G in the future.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131104749","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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