Pub Date : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9915016
Jungyeon Kim, Hyowon Lee, N. Lee
The full-duplex radio can potentially double the spectral efficiency with perfect self-interference cancellation. Traditionally, nonlinear digital self-interference cancellation (SIC) uses least mean squares (LMS) algorithms using Volterra series and Hammerstein basis expansions. However, this traditional approach slows down the convergence speed and degrades the cancellation performance due to the correlation among the nonlinear basis functions. In this demo, we develop the optimal nonlinear digital SIC for the IEEE 802.11a Wi-Fi full-duplex systems. Our approach harnesses the LMS algorithm built upon Ito-Hermite polynomials that form a set of the orthogonal basis for the complex Gaussian input process. We develop a software-defined radio full-duplex testbed compliant to the IEEE 802.11a Wi-Fi standards. Using this testbed, we show experimental results of the proposed optimal SIC algorithm and verify the superiority by comparing it with the existing SIC algorithms.
{"title":"Demo: Real-Time Implementation of Optimal Nonlinear Self-Interference Cancellation for Full-Duplex Radio","authors":"Jungyeon Kim, Hyowon Lee, N. Lee","doi":"10.1109/ICCWorkshops53468.2022.9915016","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9915016","url":null,"abstract":"The full-duplex radio can potentially double the spectral efficiency with perfect self-interference cancellation. Traditionally, nonlinear digital self-interference cancellation (SIC) uses least mean squares (LMS) algorithms using Volterra series and Hammerstein basis expansions. However, this traditional approach slows down the convergence speed and degrades the cancellation performance due to the correlation among the nonlinear basis functions. In this demo, we develop the optimal nonlinear digital SIC for the IEEE 802.11a Wi-Fi full-duplex systems. Our approach harnesses the LMS algorithm built upon Ito-Hermite polynomials that form a set of the orthogonal basis for the complex Gaussian input process. We develop a software-defined radio full-duplex testbed compliant to the IEEE 802.11a Wi-Fi standards. Using this testbed, we show experimental results of the proposed optimal SIC algorithm and verify the superiority by comparing it with the existing SIC algorithms.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063424","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9915024
C. Chae, Giyong Na, Youngseok Lee, Jongwon Lee
A well-known treatment for depression is transcranial magnetic stimulation (TMS). TMS, which activates specific cells within the brain, has been actively studied for Alzheimer's treatment. In this paper, we propose a novel magnetic multiple input multiple output (MIMO) treatment for Alzheimer's. In prior work on MIMO, one can control the beam direction by using attenuators and phase shifters. However, in MIMO with electromagnetic waves for communications, it can be a challenge to control the direction of the magnetic field. In our proposal, we use magnetic MIMO to enhance the conventional TMS system. To verify the feasibility of the system, we implement a real-time Alzheimer's treatment system and confirm the performance gain.
{"title":"Demo: Magnetic MIMO for Alzheimer's Treatment","authors":"C. Chae, Giyong Na, Youngseok Lee, Jongwon Lee","doi":"10.1109/ICCWorkshops53468.2022.9915024","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9915024","url":null,"abstract":"A well-known treatment for depression is transcranial magnetic stimulation (TMS). TMS, which activates specific cells within the brain, has been actively studied for Alzheimer's treatment. In this paper, we propose a novel magnetic multiple input multiple output (MIMO) treatment for Alzheimer's. In prior work on MIMO, one can control the beam direction by using attenuators and phase shifters. However, in MIMO with electromagnetic waves for communications, it can be a challenge to control the direction of the magnetic field. In our proposal, we use magnetic MIMO to enhance the conventional TMS system. To verify the feasibility of the system, we implement a real-time Alzheimer's treatment system and confirm the performance gain.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129337174","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814578
Shunki Kamiya, Zhengqiang Tang, T. Yamazato
In this study, we investigate the application of visible light communication (VLC) to intelligent transport systems (ITS) using rolling shutter image sensors as receivers. The use of a global shutter high-speed image sensor as a receiver has been widely examined in ITS-VLC so far. However, this image sensor is impractical for general-purpose applications due to the high cost. This study aims to perform ITS-VLC using the rolling shutter image sensor. The rolling shutter image sensor is widely used in the smartphone camera. By using it as a receiver, ITS-VLC can be used in more opportunities. In this study, we propose a ITS-VLC system using rolling shutter image sensor. The proposed system demodulates data from images captured in a moving environment. We evaluate the communication performance by measuring the bit error rate for the ITS-VLC experiments.
{"title":"Visible Light Communication System Using Rolling Shutter Image Sensor for ITS","authors":"Shunki Kamiya, Zhengqiang Tang, T. Yamazato","doi":"10.1109/iccworkshops53468.2022.9814578","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814578","url":null,"abstract":"In this study, we investigate the application of visible light communication (VLC) to intelligent transport systems (ITS) using rolling shutter image sensors as receivers. The use of a global shutter high-speed image sensor as a receiver has been widely examined in ITS-VLC so far. However, this image sensor is impractical for general-purpose applications due to the high cost. This study aims to perform ITS-VLC using the rolling shutter image sensor. The rolling shutter image sensor is widely used in the smartphone camera. By using it as a receiver, ITS-VLC can be used in more opportunities. In this study, we propose a ITS-VLC system using rolling shutter image sensor. The proposed system demodulates data from images captured in a moving environment. We evaluate the communication performance by measuring the bit error rate for the ITS-VLC experiments.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460218","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814694
Wen-Rui Shen, Zhijin Qin, A. Nallanathan
Channel estimation is one of the essential tasks of realizing reconfigurable intelligent surface (RIS)-aided communication systems. However, the RIS introduces a high-dimension cascaded channel with complicated distribution. In this case, deep learning (DL) enabled channel estimation has been proposed to tackle this problem. In most previous works, model training is conducted via centralized model learning, in which the base station (BS) collects training data from all users and lead to excessive transmission overhead. To address this challenge, this paper proposes a federated deep residual learning neural network (FDReLNet)-base channel estimation framework in an RIS-aided multi-user OFDM system. For each user, we design a deep residual neural network updated by the local dataset and only send model weights to the BS so as to train a global model. To verify the effectiveness and robustness of the FDReLNet, we update the well-trained global model to the newly joint user and test its performance. The simulation results demonstrate that our proposed FDReLNet can significantly reduce transmission over-head while maintain satisfactory channel estimation accuracy.
{"title":"Federated Learning Enabled Channel Estimation for RIS-Aided Multi-User Wireless Systems","authors":"Wen-Rui Shen, Zhijin Qin, A. Nallanathan","doi":"10.1109/iccworkshops53468.2022.9814694","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814694","url":null,"abstract":"Channel estimation is one of the essential tasks of realizing reconfigurable intelligent surface (RIS)-aided communication systems. However, the RIS introduces a high-dimension cascaded channel with complicated distribution. In this case, deep learning (DL) enabled channel estimation has been proposed to tackle this problem. In most previous works, model training is conducted via centralized model learning, in which the base station (BS) collects training data from all users and lead to excessive transmission overhead. To address this challenge, this paper proposes a federated deep residual learning neural network (FDReLNet)-base channel estimation framework in an RIS-aided multi-user OFDM system. For each user, we design a deep residual neural network updated by the local dataset and only send model weights to the BS so as to train a global model. To verify the effectiveness and robustness of the FDReLNet, we update the well-trained global model to the newly joint user and test its performance. The simulation results demonstrate that our proposed FDReLNet can significantly reduce transmission over-head while maintain satisfactory channel estimation accuracy.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122401337","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814635
Yongan Guo, Xin Tang, Hongbo Sun
In order to solve untrustworthy detection results of malicious nodes and opaque result tracking process in traditional IoT, we propose a wireless sensor network malicious node detection model in this paper. It utilizes the transparency, data traceability and immutability of the blockchain to track the detection results. A cluster head selection algorithm (CHSA) and a malicious node determination algorithm (MNDA) are proposed. Finally, a detection model of malicious nodes in wireless sensor network based on CHSA-MNDA algorithm is formed. This proposed model can improve the efficiency and accuracy of malicious node detection, thereby improving the security of IoT. The simulation results show that our proposed model can improve the malicious node detection efficiency, compared with the existing malicious node detection models for wireless sensor networks. Our proposed model can solve the node security problems such as data modification in the development process of IoT effectively.
{"title":"A Malicious Node Detection Model for Wireless Sensor Networks Security Based on CHSA-MNDA Algorithm","authors":"Yongan Guo, Xin Tang, Hongbo Sun","doi":"10.1109/iccworkshops53468.2022.9814635","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814635","url":null,"abstract":"In order to solve untrustworthy detection results of malicious nodes and opaque result tracking process in traditional IoT, we propose a wireless sensor network malicious node detection model in this paper. It utilizes the transparency, data traceability and immutability of the blockchain to track the detection results. A cluster head selection algorithm (CHSA) and a malicious node determination algorithm (MNDA) are proposed. Finally, a detection model of malicious nodes in wireless sensor network based on CHSA-MNDA algorithm is formed. This proposed model can improve the efficiency and accuracy of malicious node detection, thereby improving the security of IoT. The simulation results show that our proposed model can improve the malicious node detection efficiency, compared with the existing malicious node detection models for wireless sensor networks. Our proposed model can solve the node security problems such as data modification in the development process of IoT effectively.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126643872","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814599
L. T. Wedage, Bernard Butler, S. Balasubramaniam, M. Vuran, Y. Koucheryavy
There has been much focus on the potential for wireless links that use THz frequencies. Despite their theoretical advantages, the very high channel path loss here on Earth presents practical challenges. This paper compares the path loss performance of THz links in atmospheric gas compositions and environmental conditions on Mars and Earth. Simulations using data from the Mars Climate Database and HITRAN indicate that conditions on Mars ensure that path loss between surface-based transceivers is reduced compared to Earth. Greater effective transmission distances for THz can be achieved on Mars: at frequencies of 1.67 THz and 1.64 THz, the transmission distance is 60–70 times longer than Earth. However, severe dust storms that are common on Mars can increase path loss, so the maximum transmission distance reduces by 1–2 orders of magnitude during such storms. Some of this additional path loss can be reduced by raising antennas higher above the ground and by configuring antennas to suit different usage scenarios.
{"title":"Path Loss Analysis of Terahertz Communication in Mars' Atmospheric Conditions","authors":"L. T. Wedage, Bernard Butler, S. Balasubramaniam, M. Vuran, Y. Koucheryavy","doi":"10.1109/iccworkshops53468.2022.9814599","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814599","url":null,"abstract":"There has been much focus on the potential for wireless links that use THz frequencies. Despite their theoretical advantages, the very high channel path loss here on Earth presents practical challenges. This paper compares the path loss performance of THz links in atmospheric gas compositions and environmental conditions on Mars and Earth. Simulations using data from the Mars Climate Database and HITRAN indicate that conditions on Mars ensure that path loss between surface-based transceivers is reduced compared to Earth. Greater effective transmission distances for THz can be achieved on Mars: at frequencies of 1.67 THz and 1.64 THz, the transmission distance is 60–70 times longer than Earth. However, severe dust storms that are common on Mars can increase path loss, so the maximum transmission distance reduces by 1–2 orders of magnitude during such storms. Some of this additional path loss can be reduced by raising antennas higher above the ground and by configuring antennas to suit different usage scenarios.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126011095","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9915028
Haijun Zhang, Wanqing Guan, Dong Wang, Qize Song, A. Nallanathan
In the B5G and 6G era, service demands of diverse vertical industries are becoming increasingly complex and intelligence has become the development trend of wireless networks. By means of network slicing, resources of the infrastructure can be shared by multiple services with differentiated quality of service (QoS) guarantees. However, the uncertainty and dynamics on real-time network status requires an intelligent management scheme. Artificial intelligence (AI) algorithms are urgently needed in slice management to improve resource utilization and quickly satisfy the resource requirements of different services. This demo shows how an AI-Engine that encapsulates multiple AI algorithms can contribute to the life-cycle management of slices. In particular, our solution considers distributed deployment of the AI-Engine and provides different machine learning (ML) models for various use cases. This also enables the AI-Engine to support data analysis of network functions and intelligent applications in the edge cloud. Furthermore, this solution allows to adjust computing resource allocation for each distributed component of the AI-Engine to facilitate the intelligent network management.
{"title":"Demo: AI-Engine Enabled Intelligent Management in B5G/6G Networks","authors":"Haijun Zhang, Wanqing Guan, Dong Wang, Qize Song, A. Nallanathan","doi":"10.1109/ICCWorkshops53468.2022.9915028","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9915028","url":null,"abstract":"In the B5G and 6G era, service demands of diverse vertical industries are becoming increasingly complex and intelligence has become the development trend of wireless networks. By means of network slicing, resources of the infrastructure can be shared by multiple services with differentiated quality of service (QoS) guarantees. However, the uncertainty and dynamics on real-time network status requires an intelligent management scheme. Artificial intelligence (AI) algorithms are urgently needed in slice management to improve resource utilization and quickly satisfy the resource requirements of different services. This demo shows how an AI-Engine that encapsulates multiple AI algorithms can contribute to the life-cycle management of slices. In particular, our solution considers distributed deployment of the AI-Engine and provides different machine learning (ML) models for various use cases. This also enables the AI-Engine to support data analysis of network functions and intelligent applications in the edge cloud. Furthermore, this solution allows to adjust computing resource allocation for each distributed component of the AI-Engine to facilitate the intelligent network management.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124419011","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9882172
Guiqing Liu, Zhicheng Xi, Ruiqi Liu
Wireless interference identification plays a key role in improving the performance of mobile communication systems in terms of empowering smarter scheduling. This paper proposes to apply the convolutional neural network (CNN) to identification of wireless interference, by constructing a novel multi-level identifier which works on three different time granularities and combines the results. Exploiting the powerful feature extraction ability of CNN, the proposed approach can identify and locate 7 types of interference with high accuracy, and an adaptive threshold is calculated based on the identification result for smart scheduling. Simulation results verify that the proposed multi-level method can improve the accuracy of interference identification significantly, and achieve smart scheduling as well as increase the throughput of the network.
{"title":"A Novel Wireless Interference Identification and Scheduling Method based on Convolutional Neural Network","authors":"Guiqing Liu, Zhicheng Xi, Ruiqi Liu","doi":"10.1109/ICCWorkshops53468.2022.9882172","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9882172","url":null,"abstract":"Wireless interference identification plays a key role in improving the performance of mobile communication systems in terms of empowering smarter scheduling. This paper proposes to apply the convolutional neural network (CNN) to identification of wireless interference, by constructing a novel multi-level identifier which works on three different time granularities and combines the results. Exploiting the powerful feature extraction ability of CNN, the proposed approach can identify and locate 7 types of interference with high accuracy, and an adaptive threshold is calculated based on the identification result for smart scheduling. Simulation results verify that the proposed multi-level method can improve the accuracy of interference identification significantly, and achieve smart scheduling as well as increase the throughput of the network.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129135431","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814651
Zhitong Ni, J. A. Zhang, Kai Yang, Renping Liu
Dual-functional radar-communication (DFRC), integrating the two functions into one system and sharing one transmitted signal, shows its great potential in self-driving networks. In this paper, we develop a single-device based multi-input single-output (MISO) DFRC vehicular system. Modulations of un-slotted ALOHA frequency-hopping (UA-FH) and fast FH, commonly used in automotive radar, are adopted to transmit the DFRC waveforms and to address severe interferences caused by an interfering vehicle that serves as a communication transmitter. Due to the asynchrony between vehicles, the FH sequences of the interfering vehicle are chosen from a fixed codebook. All channel parameters are then extracted via FH decoding from radar backscattered channels and communication channels, respectively. To further increase the accuracy, we proceed to propose an iterative algorithm that divides the signals into short segments and jointly obtains all parameters with high resolution. Finally, simulation results are provided and validate the proposed DFRC vehicular system.
{"title":"Frequency-Hopping Based Joint Automotive Radar-Communication Systems Using A Single Device","authors":"Zhitong Ni, J. A. Zhang, Kai Yang, Renping Liu","doi":"10.1109/iccworkshops53468.2022.9814651","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814651","url":null,"abstract":"Dual-functional radar-communication (DFRC), integrating the two functions into one system and sharing one transmitted signal, shows its great potential in self-driving networks. In this paper, we develop a single-device based multi-input single-output (MISO) DFRC vehicular system. Modulations of un-slotted ALOHA frequency-hopping (UA-FH) and fast FH, commonly used in automotive radar, are adopted to transmit the DFRC waveforms and to address severe interferences caused by an interfering vehicle that serves as a communication transmitter. Due to the asynchrony between vehicles, the FH sequences of the interfering vehicle are chosen from a fixed codebook. All channel parameters are then extracted via FH decoding from radar backscattered channels and communication channels, respectively. To further increase the accuracy, we proceed to propose an iterative algorithm that divides the signals into short segments and jointly obtains all parameters with high resolution. Finally, simulation results are provided and validate the proposed DFRC vehicular system.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129152593","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814595
Qiaoyang Ye, C. Lo, Jeon-Hoon Jeon, Chance Tarver, M. Tonnemacher, Jeongho Yeo, Joonyoung Cho, Gary Xu, Younsun Kim, Jianzhong Zhang
As the need for limitless connectivity surges, non-terrestrial networks (NTN) will play a central role in fifth generation (5G) and beyond communications. The 3rd Gener-ation Partnership Project (3GPP) defines NTN as networks, or segments of networks, using an airborne or space-borne vehicle as a relay node or base station. An NTN-enhanced cellular network supplements a conventional terrestrial cellular network. This article provides an overview of NTN-enhanced cellular networks with a particular focus on satellite-mobile direct communications. First, we review satellite system classifications such as Geostation-ary Orbit (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO), spectrum usage, and key challenges of satellite communications. We then summarize recent 3GPP activities in NTN. In addition, we describe our recent proof-of-concept system involving a satellite channel emulator and modification of the 5G New Radio (NR) protocol stack to handle the challenge of long round-trip time - demonstrating the feasibility of NTN and the adoption of NTN-enhanced cellular networks in 5G and beyond communications. Finally, we highlight the main open issues and future research challenges of NTN-enhanced cellular networks.
{"title":"5G New Radio and Non-Terrestrial Networks: Reaching New Heights","authors":"Qiaoyang Ye, C. Lo, Jeon-Hoon Jeon, Chance Tarver, M. Tonnemacher, Jeongho Yeo, Joonyoung Cho, Gary Xu, Younsun Kim, Jianzhong Zhang","doi":"10.1109/iccworkshops53468.2022.9814595","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814595","url":null,"abstract":"As the need for limitless connectivity surges, non-terrestrial networks (NTN) will play a central role in fifth generation (5G) and beyond communications. The 3rd Gener-ation Partnership Project (3GPP) defines NTN as networks, or segments of networks, using an airborne or space-borne vehicle as a relay node or base station. An NTN-enhanced cellular network supplements a conventional terrestrial cellular network. This article provides an overview of NTN-enhanced cellular networks with a particular focus on satellite-mobile direct communications. First, we review satellite system classifications such as Geostation-ary Orbit (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO), spectrum usage, and key challenges of satellite communications. We then summarize recent 3GPP activities in NTN. In addition, we describe our recent proof-of-concept system involving a satellite channel emulator and modification of the 5G New Radio (NR) protocol stack to handle the challenge of long round-trip time - demonstrating the feasibility of NTN and the adoption of NTN-enhanced cellular networks in 5G and beyond communications. Finally, we highlight the main open issues and future research challenges of NTN-enhanced cellular networks.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122252381","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：