Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685417
S. Balakrichenan, Antoine Bernard, M. Marot, Benoît Ampeau
IoT technologies currently operate as independent silos, and roaming is possible only if there are prior interconnection agreements. To our knowledge, there are no standardised procedures for interconnecting different IoT networks for roaming. The focus of IoTRoam is to set up an operational roaming model that scales, seamlessly works with existing IoT infrastructures and interconnects on a global basis with minimum initial configuration requirements. As a Proof-of-Concept, we designed, implemented and tested a roaming LoRaWAN architecture using time-tested infrastructures on the Internet such as PKI and the DNS. The IoTRoam experience helped us to propose changes to the LoRaWAN Backend Interface Specification that have since been accepted. We also evaluated whether the proposed mechanisms satisfy constrained IoT requirements.
{"title":"IoTRoam - Design and implementation of an open LoRaWan roaming architecture","authors":"S. Balakrichenan, Antoine Bernard, M. Marot, Benoît Ampeau","doi":"10.1109/GLOBECOM46510.2021.9685417","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685417","url":null,"abstract":"IoT technologies currently operate as independent silos, and roaming is possible only if there are prior interconnection agreements. To our knowledge, there are no standardised procedures for interconnecting different IoT networks for roaming. The focus of IoTRoam is to set up an operational roaming model that scales, seamlessly works with existing IoT infrastructures and interconnects on a global basis with minimum initial configuration requirements. As a Proof-of-Concept, we designed, implemented and tested a roaming LoRaWAN architecture using time-tested infrastructures on the Internet such as PKI and the DNS. The IoTRoam experience helped us to propose changes to the LoRaWAN Backend Interface Specification that have since been accepted. We also evaluated whether the proposed mechanisms satisfy constrained IoT requirements.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123477668","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685152
Kai Vogelgesang, J. Fraire, H. Hermanns
Direct-to-Satellite IoT allows devices on the Earth surface to directly reach Low-Earth Orbit (LEO) satellites passing over them. Although an appealing approach towards a truly global IoT vision, scalability issues as well as highly dynamic topologies ask for dedicated protocol adaptations supported by novel models. This paper contributes to this research by introducing estimators and a transmission probability function to dynamically control the contending set of devices on a framed slotted Aloha model compatible with the LoRaWAN specification. In particular, we discuss techniques that account for particularities in the dynamics of sparse DtS-IoT constellations. Simulation analyses of a realistic case study show that >86% of the theoretical throughput is achievable in practice.
{"title":"Uplink Transmission Probability Functions for LoRa-Based Direct-to-Satellite IoT: A Case Study","authors":"Kai Vogelgesang, J. Fraire, H. Hermanns","doi":"10.1109/GLOBECOM46510.2021.9685152","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685152","url":null,"abstract":"Direct-to-Satellite IoT allows devices on the Earth surface to directly reach Low-Earth Orbit (LEO) satellites passing over them. Although an appealing approach towards a truly global IoT vision, scalability issues as well as highly dynamic topologies ask for dedicated protocol adaptations supported by novel models. This paper contributes to this research by introducing estimators and a transmission probability function to dynamically control the contending set of devices on a framed slotted Aloha model compatible with the LoRaWAN specification. In particular, we discuss techniques that account for particularities in the dynamics of sparse DtS-IoT constellations. Simulation analyses of a realistic case study show that >86% of the theoretical throughput is achievable in practice.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125439090","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685985
Gaoxiang Wu, Yiming Miao, B. Alzahrani, A. Barnawi, Ahmad Alhindi, Min Chen
Unmanned aerial vehicles (UAVs) with communication, computing, and storage capabilities have high mobility. Based on this advantage, it can push the service closer to the user. Our research group is concerned with implementing the Internet of Things (IoT) enabled massive crowd management platform that employs 5G to facilitate network connectivity among the UAV and sensory networks. In such a highly dynamic environment, IoT devices, users, and UAVs are the key factors to determine the caching strategies. Due to the limitations of drone batteries and changes in UAV cluster density, the environment is characterized as highly dynamic. However, the existing UAV caching strategy does not consider both the changes of the users and UAVs. Therefore, this paper proposes a three-layer UAV cache architecture in 5G network to achieve hierarchical adaptation to the dynamic changes of users and UAVs. Based on this architecture, we propose a dual dynamic adaptive caching(DDAC) algorithm. The DDAC algorithm is divided into two parts: user adaptation and UAV adaptation. For user adaptation, we designed a user-adaptive UAV trajectory model, which ensures the transmission efficiency of the UAV. For UAV adaptation, we designed and deployed a UAV-adaptive cache model based on a greedy algorithm in the cognitive center layer. The UAV can dynamically adjust the caching strategy according to the cluster density. Finally, the results of the experiment prove that our proposed UAV adaptive cache model has better performance in the cache hit ratio compared with the existing UAV cache model.
{"title":"Adaptive Edge Caching in UAV-assisted 5G Network","authors":"Gaoxiang Wu, Yiming Miao, B. Alzahrani, A. Barnawi, Ahmad Alhindi, Min Chen","doi":"10.1109/GLOBECOM46510.2021.9685985","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685985","url":null,"abstract":"Unmanned aerial vehicles (UAVs) with communication, computing, and storage capabilities have high mobility. Based on this advantage, it can push the service closer to the user. Our research group is concerned with implementing the Internet of Things (IoT) enabled massive crowd management platform that employs 5G to facilitate network connectivity among the UAV and sensory networks. In such a highly dynamic environment, IoT devices, users, and UAVs are the key factors to determine the caching strategies. Due to the limitations of drone batteries and changes in UAV cluster density, the environment is characterized as highly dynamic. However, the existing UAV caching strategy does not consider both the changes of the users and UAVs. Therefore, this paper proposes a three-layer UAV cache architecture in 5G network to achieve hierarchical adaptation to the dynamic changes of users and UAVs. Based on this architecture, we propose a dual dynamic adaptive caching(DDAC) algorithm. The DDAC algorithm is divided into two parts: user adaptation and UAV adaptation. For user adaptation, we designed a user-adaptive UAV trajectory model, which ensures the transmission efficiency of the UAV. For UAV adaptation, we designed and deployed a UAV-adaptive cache model based on a greedy algorithm in the cognitive center layer. The UAV can dynamically adjust the caching strategy according to the cluster density. Finally, the results of the experiment prove that our proposed UAV adaptive cache model has better performance in the cache hit ratio compared with the existing UAV cache model.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126942496","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9686029
Wenjing Qin, Li Yang, Jianfeng Ma
Federated learning is a promising new technology in the field of artificial intelligence. However, the unprotected model gradient parameters in federated learning may reveal sensitive participants information. To address this problem, we present a secure federated learning framework called FedGR. We use Paillier homomorphic encryption to design a new gradient security replacement algorithm, which eliminates the connections between gradient parameters and user sensitive data. In addition, we revisit the previous work by Aono and Hayashi(IEEE TIFS 2017) and show that, with their method, the user's local computing burden is too heavy. We then proved FedGR has the following characteristics to solve this problem: 1) The system does not leak any information to the server. 2) Compared with that of ordinary deep learning systems, the accuracy of federated training results yielded by our system remains unchanged. 3)The proposed approach greatly reduces the user's local computing overhead.
{"title":"FedGR: A Lossless-Obfuscation Approach for Secure Federated Learning","authors":"Wenjing Qin, Li Yang, Jianfeng Ma","doi":"10.1109/GLOBECOM46510.2021.9686029","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9686029","url":null,"abstract":"Federated learning is a promising new technology in the field of artificial intelligence. However, the unprotected model gradient parameters in federated learning may reveal sensitive participants information. To address this problem, we present a secure federated learning framework called FedGR. We use Paillier homomorphic encryption to design a new gradient security replacement algorithm, which eliminates the connections between gradient parameters and user sensitive data. In addition, we revisit the previous work by Aono and Hayashi(IEEE TIFS 2017) and show that, with their method, the user's local computing burden is too heavy. We then proved FedGR has the following characteristics to solve this problem: 1) The system does not leak any information to the server. 2) Compared with that of ordinary deep learning systems, the accuracy of federated training results yielded by our system remains unchanged. 3)The proposed approach greatly reduces the user's local computing overhead.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115028156","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685614
Huatong Jiang, Yanjun Li, Meihui Gao
To meet the real-time requirement of the edge computing applications, technologies of software defined network and network function virtualization are introduced to reconstruct the MEC system. On this basis, we consider the design of online computing and communication resource allocation solution, aiming at maximizing the long-term average rate of successfully processing the real-time tasks. The problem is formulated in a Markov decision process framework. Both Q-learning and deep reinforcement learning algorithms are proposed to obtain online resource allocation solutions with consideration of time-varying channel conditions and task loads. Simulation results show that both proposed algorithms converge quickly and the average real-time task processing success rate achieved by deep reinforcement learning algorithm is the highest among all the baseline algorithms.
{"title":"Online Resource Allocation for SDN-Based Mobile Edge Computing: Reinforcement Approaches","authors":"Huatong Jiang, Yanjun Li, Meihui Gao","doi":"10.1109/GLOBECOM46510.2021.9685614","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685614","url":null,"abstract":"To meet the real-time requirement of the edge computing applications, technologies of software defined network and network function virtualization are introduced to reconstruct the MEC system. On this basis, we consider the design of online computing and communication resource allocation solution, aiming at maximizing the long-term average rate of successfully processing the real-time tasks. The problem is formulated in a Markov decision process framework. Both Q-learning and deep reinforcement learning algorithms are proposed to obtain online resource allocation solutions with consideration of time-varying channel conditions and task loads. Simulation results show that both proposed algorithms converge quickly and the average real-time task processing success rate achieved by deep reinforcement learning algorithm is the highest among all the baseline algorithms.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115196918","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685807
Yujia Mu, Yuanlong Tan, M. Veeraraghavan, Cong Shen
Large-volume scientific data is one of the prominent driving forces behind next generation networking. In particular, Software Defined Network (SDN) makes leveraging path-based network multicast services practically feasible. In our prior work, we have developed a cross-layer architecture for supporting reliable file-streams multicasting over SDN-enabled Layer-2 network, and implemented the architecture for a meteorology data distribution application in atmospheric science. However, it is challenging to determine an optimal rate for this application with the varying type, volume, and quality of meteorological data. In this paper, we propose a Quality of Service (QoS)-driven rate management pipeline to determine the optimal rate based on the input traffic characteristics and performance constraints. Specifically, the pipeline employs a feedtype classifier using Multi-Layer Perception (MLP) to recognize the type of meteorological data and a delay prediction regressor using stacked Long Short-Term Memory (LSTM) to predict per-file delay for the file-streams. Finally, we determine the optimal rate for the given file-streams using the trained regressor. We implement this pipeline to test the real-world file-stream data collected from a trial deployment, and the results show that our regressor outperforms all baselines by selecting the optimal rate in the presence of varying file set sizes.
{"title":"A Machine Learning Approach for Rate Prediction in Multicast File-stream Distribution Networks","authors":"Yujia Mu, Yuanlong Tan, M. Veeraraghavan, Cong Shen","doi":"10.1109/GLOBECOM46510.2021.9685807","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685807","url":null,"abstract":"Large-volume scientific data is one of the prominent driving forces behind next generation networking. In particular, Software Defined Network (SDN) makes leveraging path-based network multicast services practically feasible. In our prior work, we have developed a cross-layer architecture for supporting reliable file-streams multicasting over SDN-enabled Layer-2 network, and implemented the architecture for a meteorology data distribution application in atmospheric science. However, it is challenging to determine an optimal rate for this application with the varying type, volume, and quality of meteorological data. In this paper, we propose a Quality of Service (QoS)-driven rate management pipeline to determine the optimal rate based on the input traffic characteristics and performance constraints. Specifically, the pipeline employs a feedtype classifier using Multi-Layer Perception (MLP) to recognize the type of meteorological data and a delay prediction regressor using stacked Long Short-Term Memory (LSTM) to predict per-file delay for the file-streams. Finally, we determine the optimal rate for the given file-streams using the trained regressor. We implement this pipeline to test the real-world file-stream data collected from a trial deployment, and the results show that our regressor outperforms all baselines by selecting the optimal rate in the presence of varying file set sizes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355202","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685372
Huan Huang, Xiaowen Wang, Chongfu Zhang, Kun Qiu, Zhu Han
Recently, reconfigurable intelligent surfaces (RISs) have emerged as a potential technique for future 6G communications. Considering the practical hardware constraints of RISs, e.g., the availability of only quantized phase shifts for reflecting elements, we investigate codebook-based passive beamforming, and then develop a two-phase precoding algorithm for multi-RIS-aided multi-user multiple-input single-output (MU-MISO) systems, where the required pilot overhead is much less than that for training the perfect channel state information (CSI). Compared with the maximum ratio transmission (MRT), we propose a more efficient codebook-based passive beamforming scheme based on the sum reward maximization. To verify the feasibility of the proposed reward-maximization-based passive beamforming, we compare the average sum rates achieved by the proposed method, the MRT method, as well as the exhaustive method. Further, we design a feasible set with a few codewords to reduce the computational complexity of the exhaustive method. Moreover, the obtained results based on different codebooks are given to illustrate the generality of the proposed scheme.
{"title":"Reward-Maximization-Based Passive Beamforming for Multi-RIS-Aided Multi-User MISO Systems","authors":"Huan Huang, Xiaowen Wang, Chongfu Zhang, Kun Qiu, Zhu Han","doi":"10.1109/GLOBECOM46510.2021.9685372","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685372","url":null,"abstract":"Recently, reconfigurable intelligent surfaces (RISs) have emerged as a potential technique for future 6G communications. Considering the practical hardware constraints of RISs, e.g., the availability of only quantized phase shifts for reflecting elements, we investigate codebook-based passive beamforming, and then develop a two-phase precoding algorithm for multi-RIS-aided multi-user multiple-input single-output (MU-MISO) systems, where the required pilot overhead is much less than that for training the perfect channel state information (CSI). Compared with the maximum ratio transmission (MRT), we propose a more efficient codebook-based passive beamforming scheme based on the sum reward maximization. To verify the feasibility of the proposed reward-maximization-based passive beamforming, we compare the average sum rates achieved by the proposed method, the MRT method, as well as the exhaustive method. Further, we design a feasible set with a few codewords to reduce the computational complexity of the exhaustive method. Moreover, the obtained results based on different codebooks are given to illustrate the generality of the proposed scheme.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122306982","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685782
Yuejie Zhang, Kai Sun, Xuelian Gao, W. Huang, Haijun Zhang
With the rapid increase of demand on mobile data traffic of user equipment (UE), network operators have begun to deploy abundant heterogeneous base stations (BSs) to ensure the quality of service (QoS) of UEs, which will cause new problems such as network congestion and load imbalance. If the pattern of user association (UA) can be adjusted in accordance with the results of traffic prediction, the performance of system will be greatly improved. Therefore, a new neural network approach based on spatial and temporal characteristics of traffic data is proposed for traffic prediction. The fluctuations of traffic in the future week are predicted by the proposed method. Then, UA is represented as a problem of maximizing the utility function of load balancing index, and a dynamic user association based on load prediction algorithm (DUALP) which aims to achieve a proactive load balancing is proposed. The QoS of UEs is ensured and the long-term stability of the system is achieved by DUALP. Experimental results show that compared to the classic UA strategies, the most optimal load distribution is realized by DUALP.
{"title":"Load Balancing and User Association Based on Historical Data","authors":"Yuejie Zhang, Kai Sun, Xuelian Gao, W. Huang, Haijun Zhang","doi":"10.1109/GLOBECOM46510.2021.9685782","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685782","url":null,"abstract":"With the rapid increase of demand on mobile data traffic of user equipment (UE), network operators have begun to deploy abundant heterogeneous base stations (BSs) to ensure the quality of service (QoS) of UEs, which will cause new problems such as network congestion and load imbalance. If the pattern of user association (UA) can be adjusted in accordance with the results of traffic prediction, the performance of system will be greatly improved. Therefore, a new neural network approach based on spatial and temporal characteristics of traffic data is proposed for traffic prediction. The fluctuations of traffic in the future week are predicted by the proposed method. Then, UA is represented as a problem of maximizing the utility function of load balancing index, and a dynamic user association based on load prediction algorithm (DUALP) which aims to achieve a proactive load balancing is proposed. The QoS of UEs is ensured and the long-term stability of the system is achieved by DUALP. Experimental results show that compared to the classic UA strategies, the most optimal load distribution is realized by DUALP.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122581284","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 : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685118
K. Krhac, K. Sayrafian-Pour, Uzay Bengi, S. Dumanli
In this paper we investigate the feasibility of a simple wearable system that can be used at home to detect or monitor excess fluid buildup in the lungs. This is a medical condition referred to as pulmonary edema. A methodology has been developed to computationally emulate human lungs with various levels of fluid in the alveoli. The proposed wearable system is composed of several small wearable antennas located on the chest and back area. The antennas will operate at MedRadio frequency band and will be optimized for signal penetration through the body. The frequency and time responses of the communication channel between these antennas for the lung models with varying levels of fluid have been measured and analyzed. The results show a correlation between the channel response and the level of fluids inside the lungs. This correlation can potentially be exploited by a simple wearable system to predict the onset of pulmonary edema for patients living in remote areas or people who need to be continuously monitored.
{"title":"A Wearable Wireless Monitoring System for the Detection of Pulmonary Edema","authors":"K. Krhac, K. Sayrafian-Pour, Uzay Bengi, S. Dumanli","doi":"10.1109/GLOBECOM46510.2021.9685118","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685118","url":null,"abstract":"In this paper we investigate the feasibility of a simple wearable system that can be used at home to detect or monitor excess fluid buildup in the lungs. This is a medical condition referred to as pulmonary edema. A methodology has been developed to computationally emulate human lungs with various levels of fluid in the alveoli. The proposed wearable system is composed of several small wearable antennas located on the chest and back area. The antennas will operate at MedRadio frequency band and will be optimized for signal penetration through the body. The frequency and time responses of the communication channel between these antennas for the lung models with varying levels of fluid have been measured and analyzed. The results show a correlation between the channel response and the level of fluids inside the lungs. This correlation can potentially be exploited by a simple wearable system to predict the onset of pulmonary edema for patients living in remote areas or people who need to be continuously monitored.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114147589","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}
This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.
{"title":"Enabling Ubiquitous Non-Orthogonal Multiple Access and Pervasive Federated Learning via STAR-RIS","authors":"Wanli Ni, Yuanwei Liu, Yonina C. Eldar, Zhaohui Yang, Hui Tian","doi":"10.1109/GLOBECOM46510.2021.9685556","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685556","url":null,"abstract":"This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"213 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114167411","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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