Pub Date : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814623
Yong Chen, Yueming Cai
This paper investigates the age of information (AoI) with dual-hop short-packet communications in the cognitive Internet of Things (IoT) for monitoring and control. The AoI is adopted as a performance metric to characterize the freshness of received information at the remote server by measuring the value of the packet delay. Considering the packets error, we investigate the performance of dual-hop short-packet communications under classical automatic repeat-request (ARQ) protocol and truncated automatic repeat-request (TARQ) protocol, and obtain the approximated closed-form expressions of the average AoI, respectively. Accordingly, the blocklength and the retransmission times can be got by global search algorithm to minimize the average AoI under two protocols. Illustrative numerical results substantiate that the TARQ protocol can achieve lower average AoI in high packet generation rate scenarios. Moreover, when the interference from the primary user is large, the classical ARQ can obtain lower average AoI.
{"title":"Age of Information for Short-Packet Relaying Communications in Cognitive Internet of Things","authors":"Yong Chen, Yueming Cai","doi":"10.1109/iccworkshops53468.2022.9814623","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814623","url":null,"abstract":"This paper investigates the age of information (AoI) with dual-hop short-packet communications in the cognitive Internet of Things (IoT) for monitoring and control. The AoI is adopted as a performance metric to characterize the freshness of received information at the remote server by measuring the value of the packet delay. Considering the packets error, we investigate the performance of dual-hop short-packet communications under classical automatic repeat-request (ARQ) protocol and truncated automatic repeat-request (TARQ) protocol, and obtain the approximated closed-form expressions of the average AoI, respectively. Accordingly, the blocklength and the retransmission times can be got by global search algorithm to minimize the average AoI under two protocols. Illustrative numerical results substantiate that the TARQ protocol can achieve lower average AoI in high packet generation rate scenarios. Moreover, when the interference from the primary user is large, the classical ARQ can obtain lower average AoI.","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":"114992329","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/iccworkshops53468.2022.9814613
Yijing Liu, Gang Feng, Yao Sun, Xiaoqian Li, Jianhong Zhou, Shuang Qin
Federated learning (FL) has recently become one of the hottest focuses in network edge intelligence. In the FL framework, user equipments (UEs) train local machine learning (ML) models and transmit the trained models to an aggregator where a global model is formed and then sent back to UEs, such that FL can enable collaborative model training. In large-scale and dynamic edge networks, both local model training and transmission may not be always successful due to constrained power and computing resources at mobile devices, wireless channel impairments, bandwidth limitations, etc., which directly degrades FL performance in terms of model accuracy and/or training time. On the other hand, we need to quantify the benefits and cost of deploying edge intelligence when we plan to improve network performance by using artificial intelligence (AI) techniques which definitely incur certain cost. Therefore, it is imperative to deeply understand the relationship between the required multiple-dimensional resources and FL performance to facilitate FL enabled edge intelligence. In this paper, we construct an analytical model for investigating the relationship between the accuracy of ML model and consumed network resources in FL enabled edge networks. Based on the analytical model, we can explicitly quantify the trained model accuracy given spatial-temporal domain distribution, available user computing and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis. Our analytical model in this paper provides some useful guidelines for appropriately promoting FL enabled edge network intelligence.
{"title":"Resource Consumption for Supporting Federated Learning Enabled Network Edge Intelligence","authors":"Yijing Liu, Gang Feng, Yao Sun, Xiaoqian Li, Jianhong Zhou, Shuang Qin","doi":"10.1109/iccworkshops53468.2022.9814613","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814613","url":null,"abstract":"Federated learning (FL) has recently become one of the hottest focuses in network edge intelligence. In the FL framework, user equipments (UEs) train local machine learning (ML) models and transmit the trained models to an aggregator where a global model is formed and then sent back to UEs, such that FL can enable collaborative model training. In large-scale and dynamic edge networks, both local model training and transmission may not be always successful due to constrained power and computing resources at mobile devices, wireless channel impairments, bandwidth limitations, etc., which directly degrades FL performance in terms of model accuracy and/or training time. On the other hand, we need to quantify the benefits and cost of deploying edge intelligence when we plan to improve network performance by using artificial intelligence (AI) techniques which definitely incur certain cost. Therefore, it is imperative to deeply understand the relationship between the required multiple-dimensional resources and FL performance to facilitate FL enabled edge intelligence. In this paper, we construct an analytical model for investigating the relationship between the accuracy of ML model and consumed network resources in FL enabled edge networks. Based on the analytical model, we can explicitly quantify the trained model accuracy given spatial-temporal domain distribution, available user computing and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis. Our analytical model in this paper provides some useful guidelines for appropriately promoting FL enabled edge network intelligence.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"11 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":"116713232","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/iccworkshops53468.2022.9814555
Meghana Raghavendra, Zesheng Chen
Rapid expansion in the utilization of Internet of things (IoT) devices in everyday life leads to an increase in the attack surface for cybercriminals. IoT devices have been frequently compromised and used for the creation of botnets. The goal of this research is to identify a machine learning method that can be run on resource-constrained IoT edge devices to detect IoT botnet traffic accurately in real time. Specifically, we apply both the input perturbation ranking (IPR) algorithm and decision trees to achieve this goal. We study the network snapshots of IoT traffic infected with two botnets, i.e., Mirai and Bashlite, and use IPR with XGBoost to identify nine most important features that distinguish between benign and anomalous traffic for IoT devices. We propose to use decision trees, a supervised machine learning method, because of its simplicity, less time to train and predict, ease to be translated to security policy, and flexibility on balancing detection accuracy and speed. In our experiments, we compare the performance of decision trees with a deep-learning based method, i.e., Kitsune, and other popular supervised machine learning methods. We show that decision trees are with high decision performance (e.g., more than 99.99% accuracy), but with much less training and prediction time than Kitsune and most other machine learning methods. Moreover, we demonstrate that using nine most important features in decision tress, the detection accuracy is similar, but the computation power can be significantly reduced, making botnet detection suitable on IoT edge devices.
{"title":"Detecting IoT Botnets on IoT Edge Devices","authors":"Meghana Raghavendra, Zesheng Chen","doi":"10.1109/iccworkshops53468.2022.9814555","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814555","url":null,"abstract":"Rapid expansion in the utilization of Internet of things (IoT) devices in everyday life leads to an increase in the attack surface for cybercriminals. IoT devices have been frequently compromised and used for the creation of botnets. The goal of this research is to identify a machine learning method that can be run on resource-constrained IoT edge devices to detect IoT botnet traffic accurately in real time. Specifically, we apply both the input perturbation ranking (IPR) algorithm and decision trees to achieve this goal. We study the network snapshots of IoT traffic infected with two botnets, i.e., Mirai and Bashlite, and use IPR with XGBoost to identify nine most important features that distinguish between benign and anomalous traffic for IoT devices. We propose to use decision trees, a supervised machine learning method, because of its simplicity, less time to train and predict, ease to be translated to security policy, and flexibility on balancing detection accuracy and speed. In our experiments, we compare the performance of decision trees with a deep-learning based method, i.e., Kitsune, and other popular supervised machine learning methods. We show that decision trees are with high decision performance (e.g., more than 99.99% accuracy), but with much less training and prediction time than Kitsune and most other machine learning methods. Moreover, we demonstrate that using nine most important features in decision tress, the detection accuracy is similar, but the computation power can be significantly reduced, making botnet detection suitable on IoT edge devices.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"35 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":"116931355","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/iccworkshops53468.2022.9814670
Fuwang Dong, F. Liu
In the upcoming next-generation (5G-Advanced and 6G) wireless networks, the Integrated Sensing and Communications (ISAC) technique is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. For target localization in the ISAC framework, the limited resources should be appropriately allocated to achieve optimal target localization accuracy while guaranteeing the communication quality of service (QoS). To this end, we proposed a joint power and bandwidth allocation scheme in this paper, minimizing the sum of Crámer-Rao bound (CRB) for range and azimuth estimations at the constraint of communication achievable sumrate. First, we unify the sensing effective bandwidth with the normal bandwidth by determining the sensing waveform. Then, the alternative optimization (AO) method is employed to decouple the power and bandwidth variables. Finally, we study the performance trade-off between S&C services by numerical simulations.
{"title":"Localization as a Service in Perceptive Networks: An ISAC Resource Allocation Framework","authors":"Fuwang Dong, F. Liu","doi":"10.1109/iccworkshops53468.2022.9814670","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814670","url":null,"abstract":"In the upcoming next-generation (5G-Advanced and 6G) wireless networks, the Integrated Sensing and Communications (ISAC) technique is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. For target localization in the ISAC framework, the limited resources should be appropriately allocated to achieve optimal target localization accuracy while guaranteeing the communication quality of service (QoS). To this end, we proposed a joint power and bandwidth allocation scheme in this paper, minimizing the sum of Crámer-Rao bound (CRB) for range and azimuth estimations at the constraint of communication achievable sumrate. First, we unify the sensing effective bandwidth with the normal bandwidth by determining the sensing waveform. Then, the alternative optimization (AO) method is employed to decouple the power and bandwidth variables. Finally, we study the performance trade-off between S&C services by numerical simulations.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"15 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":"117232119","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}
The power control is a significant technique for suppressing co-channel interference that severely limits the capacity and connectivity of multi-cell communication systems. In this paper, we propose a novel and efficient multi-agent deep reinforcement learning (MADRL)-based uplink power control method for multi-cell multi-user communication systems. We first formulate the multi-user uplink transmission power optimization problem to maximize the sum throughput of multiple cells. Then, the optimization problem is transformed into a Markov decision process. Since the multi-user power control needs to consider the cooperation of strategies between users, the MADRL technique can be adopted. In our MADRL model, each agent outputs the uplink transmission power of the corresponding user by leveraging the value decomposition network. We also design a pruning algorithm to accelerate the training process of the MADRL model. The experimental results indicate that the proposed MADRL-based uplink power control method is superior to the baseline methods in terms of system throughput and quality of service. The designed pruning algorithm can effectively accelerate model training and also further improve the throughput performance of the proposed method.
{"title":"Multi-Agent Deep Reinforcement Learning for Uplink Power Control in Multi-Cell Systems","authors":"Ruibao Jia, L. Liu, Xufei Zheng, Yuhan Yang, Shaoyang Wang, Pingmu Huang, Tiejun Lv","doi":"10.1109/iccworkshops53468.2022.9814468","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814468","url":null,"abstract":"The power control is a significant technique for suppressing co-channel interference that severely limits the capacity and connectivity of multi-cell communication systems. In this paper, we propose a novel and efficient multi-agent deep reinforcement learning (MADRL)-based uplink power control method for multi-cell multi-user communication systems. We first formulate the multi-user uplink transmission power optimization problem to maximize the sum throughput of multiple cells. Then, the optimization problem is transformed into a Markov decision process. Since the multi-user power control needs to consider the cooperation of strategies between users, the MADRL technique can be adopted. In our MADRL model, each agent outputs the uplink transmission power of the corresponding user by leveraging the value decomposition network. We also design a pruning algorithm to accelerate the training process of the MADRL model. The experimental results indicate that the proposed MADRL-based uplink power control method is superior to the baseline methods in terms of system throughput and quality of service. The designed pruning algorithm can effectively accelerate model training and also further improve the throughput performance of the proposed method.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"7 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":"116578280","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/iccworkshops53468.2022.9814556
Cheikh Saliou Mbacke Babou, Y. Owada, M. Inoue, K. Takizawa, T. Kuri
Edge computing system is facing real challenges with the evolution of new information and communication technologies (ICT). In addition, with the advent of Internet of Things (IoT) and real-time services, current edge computing system is becoming an architecture that is inappropriate for some services that require ultra low-latency and very high throughput. In order to meet all the requirements of these services, a new edge computing architecture is required. For this purpose, Home Edge Computing (HEC) architecture has been proposed. However, edge computing system has encountered some issues such as resource limitation on home servers. Furthermore, most distributed systems (e.g. HEC) operate in online mode. In other words, once failures occur in the Internet (remote servers), applications/services will be not longer accessible. In this paper, we propose HEC-NerveNet architecture, a technique based on the HEC architecture and NerveNet technology (HEC-N), to allow users to have continuity of services (resilient), even if they do not access to the remote servers (cloud computing). As a reminder, NerveNet is a resilient distributed architecture that we proposed in 2011 after the natural disaster in Japan. It allows maintaining connectivity and services in case of network failure. In addition, NerveNet solution allows the automatic clustering, and fast recovery thanks to the mesh topology on NerveNet network. This can overcome the need to manually create clusters in the HEC architecture. In the simulation, we prove that our proposal (HEC- N) is very suitable for resilient architecture and the need for future generation networks (beyond 5G/6G networks) with improving the metrics such as ultra-low latency and very high throughput compared with the current HEC and NerveNet systems.
{"title":"HEC-NerveNet: A Resilient Edge Cloud Architecture for Beyond 5G Networks","authors":"Cheikh Saliou Mbacke Babou, Y. Owada, M. Inoue, K. Takizawa, T. Kuri","doi":"10.1109/iccworkshops53468.2022.9814556","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814556","url":null,"abstract":"Edge computing system is facing real challenges with the evolution of new information and communication technologies (ICT). In addition, with the advent of Internet of Things (IoT) and real-time services, current edge computing system is becoming an architecture that is inappropriate for some services that require ultra low-latency and very high throughput. In order to meet all the requirements of these services, a new edge computing architecture is required. For this purpose, Home Edge Computing (HEC) architecture has been proposed. However, edge computing system has encountered some issues such as resource limitation on home servers. Furthermore, most distributed systems (e.g. HEC) operate in online mode. In other words, once failures occur in the Internet (remote servers), applications/services will be not longer accessible. In this paper, we propose HEC-NerveNet architecture, a technique based on the HEC architecture and NerveNet technology (HEC-N), to allow users to have continuity of services (resilient), even if they do not access to the remote servers (cloud computing). As a reminder, NerveNet is a resilient distributed architecture that we proposed in 2011 after the natural disaster in Japan. It allows maintaining connectivity and services in case of network failure. In addition, NerveNet solution allows the automatic clustering, and fast recovery thanks to the mesh topology on NerveNet network. This can overcome the need to manually create clusters in the HEC architecture. In the simulation, we prove that our proposal (HEC- N) is very suitable for resilient architecture and the need for future generation networks (beyond 5G/6G networks) with improving the metrics such as ultra-low latency and very high throughput compared with the current HEC and NerveNet systems.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"26 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":"114605702","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/iccworkshops53468.2022.9814625
Suhyeon Jeon, Jeongho Kwak, Jihwan P. Choi
For B5G/6G networks, security will be a critical issue due to data explosion from the launch of non-terrestrial net-works (NTN) and space-terrestrial integrated networks (STIN). To this end, encryption and physical layer security (PLS) on top of the NTN or STIN have been studied extensively. However, a use of either encryption or PLS only has critical drawbacks: the encryption induces an additional power or time cost, while the performance of PLS can be limited by the capability of eavesdroppers (Eves). In this paper, we propose a multibeam satellite network security solution by exploiting encryption and beamforming technologies. Satellites are assumed to be capable of transmitting two types of encrypted and non-encrypted signals for broadcast and private messages, simultaneously. We first design a security threat under non-colluding and colluding eavesdropping attacks. Thereafter, we explore power allocation, user selection, and beam scheduling based on security threats and channel conditions over satellite downlinks in orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) systems, respectively. We show the improved secrecy performance of the proposed method compared to the secrecy capacity of PLS. Finally, the simulation results demonstrate that NOMA has better total capacity and secrecy performances than OMA.
{"title":"Advanced Multibeam Satellite Network Security with Encryption and Beamforming Technologies","authors":"Suhyeon Jeon, Jeongho Kwak, Jihwan P. Choi","doi":"10.1109/iccworkshops53468.2022.9814625","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814625","url":null,"abstract":"For B5G/6G networks, security will be a critical issue due to data explosion from the launch of non-terrestrial net-works (NTN) and space-terrestrial integrated networks (STIN). To this end, encryption and physical layer security (PLS) on top of the NTN or STIN have been studied extensively. However, a use of either encryption or PLS only has critical drawbacks: the encryption induces an additional power or time cost, while the performance of PLS can be limited by the capability of eavesdroppers (Eves). In this paper, we propose a multibeam satellite network security solution by exploiting encryption and beamforming technologies. Satellites are assumed to be capable of transmitting two types of encrypted and non-encrypted signals for broadcast and private messages, simultaneously. We first design a security threat under non-colluding and colluding eavesdropping attacks. Thereafter, we explore power allocation, user selection, and beam scheduling based on security threats and channel conditions over satellite downlinks in orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) systems, respectively. We show the improved secrecy performance of the proposed method compared to the secrecy capacity of PLS. Finally, the simulation results demonstrate that NOMA has better total capacity and secrecy performances than OMA.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"39 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":"117093578","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/iccworkshops53468.2022.9814585
N. Okati, T. Riihonen
Providing backhaul access for airborne networks ensures their seamless connectivity to other aerial or terrestrial users with sufficient data rate. The backhaul for aerial platforms (APs) has been mostly provided through geostationary Earth orbit satellites and the terrestrial base stations (BSs). However, the former limits the achievable throughput due to significant path loss and latency, and the latter is unable to provide full sky coverage due to existence of wide under-served regions on Earth. Therefore, the emerging low Earth orbit (LEO) Internet constellations have the potential to address this problem by providing a thorough coverage for APs with higher data rate and lower latency. In this paper, we analyze the coverage probability and data rate of a LEO backhaul network for an AP located at an arbitrary altitude above the ground. The satellites' locality is modeled as a nonhomogeneous Poisson point process which not only enables tractable analysis by utilizing the tools from stochastic geometry, but also considers the latitude-dependent density of satellites. To demonstrate a compromise on the backhaul network's selection for the airborne network, we also compare the aforementioned setup with a reference terrestrial backhaul network, where AP directly connects to the ground BSs. Based on the numerical results, we can conclude that, for low BS densities, LEO satellites provide a better backhaul connection, which improves by increasing the AP's altitude.
{"title":"Downlink and Uplink Low Earth Orbit Satellite Backhaul for Airborne Networks","authors":"N. Okati, T. Riihonen","doi":"10.1109/iccworkshops53468.2022.9814585","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814585","url":null,"abstract":"Providing backhaul access for airborne networks ensures their seamless connectivity to other aerial or terrestrial users with sufficient data rate. The backhaul for aerial platforms (APs) has been mostly provided through geostationary Earth orbit satellites and the terrestrial base stations (BSs). However, the former limits the achievable throughput due to significant path loss and latency, and the latter is unable to provide full sky coverage due to existence of wide under-served regions on Earth. Therefore, the emerging low Earth orbit (LEO) Internet constellations have the potential to address this problem by providing a thorough coverage for APs with higher data rate and lower latency. In this paper, we analyze the coverage probability and data rate of a LEO backhaul network for an AP located at an arbitrary altitude above the ground. The satellites' locality is modeled as a nonhomogeneous Poisson point process which not only enables tractable analysis by utilizing the tools from stochastic geometry, but also considers the latitude-dependent density of satellites. To demonstrate a compromise on the backhaul network's selection for the airborne network, we also compare the aforementioned setup with a reference terrestrial backhaul network, where AP directly connects to the ground BSs. Based on the numerical results, we can conclude that, for low BS densities, LEO satellites provide a better backhaul connection, which improves by increasing the AP's altitude.","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":"129334284","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/iccworkshops53468.2022.9814510
Zicheng Xu, Xiaoyu Yan, Wei Tang, Xiaoyang Liao, H. Zhang, B. Chang
Terahertz (THz) communications have the potential to provide ultra-high transmission data rate for applications with high rate requirements, e.g., virtual reality, augmented reality, and autonomous vehicles. However, it is very easy to be blocked and misaligned for THz transmissions due to its ultra-high frequency and narrow beamwidth. To deal with the blocked issue, we adopt reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicles (UAV) to achieve seamless wide-area coverage with little or no human assistance. Furthermore, to deal with the misaligned issue in mobile RIS-assisted UAV scenarios, we propose a novel joint motion control and communication scheduling method in the aforementioned scenario, which can maximize the sum rate of all users while guaranteeing data rate requirement for each user and maintaining good motion control performance for UAV networks. Specifically, we first obtain a closed-form expression for data rate triggered motion control design, where both data rate requirement in THz communications and motion control performance of UA V are guaranteed. Then, the sum rate is maximized for all users based on the motion control performance. Simulation results show remarkable performance of the proposed method.
{"title":"Maximizing Sum Rate by Joint Control and Communication Scheduling for RIS-Assisted Cellular Connected UAV in THz Communications","authors":"Zicheng Xu, Xiaoyu Yan, Wei Tang, Xiaoyang Liao, H. Zhang, B. Chang","doi":"10.1109/iccworkshops53468.2022.9814510","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814510","url":null,"abstract":"Terahertz (THz) communications have the potential to provide ultra-high transmission data rate for applications with high rate requirements, e.g., virtual reality, augmented reality, and autonomous vehicles. However, it is very easy to be blocked and misaligned for THz transmissions due to its ultra-high frequency and narrow beamwidth. To deal with the blocked issue, we adopt reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicles (UAV) to achieve seamless wide-area coverage with little or no human assistance. Furthermore, to deal with the misaligned issue in mobile RIS-assisted UAV scenarios, we propose a novel joint motion control and communication scheduling method in the aforementioned scenario, which can maximize the sum rate of all users while guaranteeing data rate requirement for each user and maintaining good motion control performance for UAV networks. Specifically, we first obtain a closed-form expression for data rate triggered motion control design, where both data rate requirement in THz communications and motion control performance of UA V are guaranteed. Then, the sum rate is maximized for all users based on the motion control performance. Simulation results show remarkable performance of the proposed method.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"168 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":"124696731","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/iccworkshops53468.2022.9814591
Jide Yuan, G. Alexandropoulos, Eleftherios Kofidis, T. L. Jensen, E. Carvalho
Estimation of channel state information, balancing accuracy and pilot overhead, is critical for Single-Input Multiple-Output communication (SIMO) systems empowered by the emerging technology of Reconfigurable Intelligent Surfaces (RISs). Due to the predominantly passive nature of the RIS, the reflected signals are coupled together, rendering the estimation of the multiple cascaded channels a challenging task. Additionally, the time-varying feature of most of the realistic wireless channels drives up the cost of real-time channel tracking significantly, especially when RISs with massive numbers of unit elements are deployed. In this paper, capitalizing on a PARAllel FACtor (PARAFAC) decomposition of the received signal model, we unfold the three-dimension signal into matrices and present a low complexity channel tracking framework for the uplink of RIS-enabled multi-user SIMO communication systems. The proposed algorithm incorporates a PARAFAC-based estimator together with the generalized approximate message passing technique, combining their advantages for reducing the computational complexity and pilot overhead of channel tracking, while maintaining improved accuracy. Our numerical results validate the feasibility and efficiency of the proposed channel tracking approach for various system parameters.
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