Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this article a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.
{"title":"Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data","authors":"Youssra Cheriguene;Wael Jaafar;Halim Yanikomeroglu;Chaker Abdelaziz Kerrache","doi":"10.1109/OJVT.2023.3341304","DOIUrl":"10.1109/OJVT.2023.3341304","url":null,"abstract":"Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this article a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"125-141"},"PeriodicalIF":6.4,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10360280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138967179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-13DOI: 10.1109/OJVT.2023.3342074
Ivan Sánchez;F. Javier López-Martínez
We investigate the application of the Lomax distribution for wireless fading modeling purposes. By a proper redefinition of its scale parameter, we present closed-form expressions for its main statistics: probability density function, cumulative distribution function, raw moments and Laplace-domain statistics. Then, relevant performance indicators are derived, including the amount of fading, channel capacity, outage probability and error rate. Other applications include diversity reception using selection combining, as well as composite fading modeling. The Lomax distribution is compared to the relevant case of Rayleigh fading, and to other benchmark distributions of similar complexity used in the literature.
{"title":"The Lomax Distribution for Wireless Channel Modeling: Theory and Applications","authors":"Ivan Sánchez;F. Javier López-Martínez","doi":"10.1109/OJVT.2023.3342074","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3342074","url":null,"abstract":"We investigate the application of the Lomax distribution for wireless fading modeling purposes. By a proper redefinition of its scale parameter, we present closed-form expressions for its main statistics: probability density function, cumulative distribution function, raw moments and Laplace-domain statistics. Then, relevant performance indicators are derived, including the amount of fading, channel capacity, outage probability and error rate. Other applications include diversity reception using selection combining, as well as composite fading modeling. The Lomax distribution is compared to the relevant case of Rayleigh fading, and to other benchmark distributions of similar complexity used in the literature.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"162-171"},"PeriodicalIF":6.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10356745","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto–Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto–Istanbul and Toronto–London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.
{"title":"Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability","authors":"Jintao Liang;Aizaz U. Chaudhry;Eylem Erdogan;Halim Yanikomeroglu;Gunes Karabulut Kurt;Peng Hu;Khaled Ahmed;Stephane Martel","doi":"10.1109/OJVT.2023.3341409","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3341409","url":null,"abstract":"In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto–Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto–Istanbul and Toronto–London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"244-261"},"PeriodicalIF":6.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139916610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1109/OJVT.2023.3340993
Andrea Conti;Gianluca Torsoli;Carlos A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;Moe Z. Win
Location awareness is vital in next generation (xG) wireless networks to enable different use cases, including location-based services (LBSs) and efficient network management. However, achieving the service level requirements specified by the 3rd Generation Partnership Project (3GPP) is challenging. This calls for new localization algorithms as well as for 3GPP-standardized scenarios to support their systematic development and testing. In this context, the availability of public datasets with 3GPP-compliant configurations is essential to advance the evolution of xG networks. This paper introduces xG-Loc, the first open dataset for localization algorithms and services fully compliant with 3GPP technical reports and specifications. xG-Loc includes received localization signals, measurements, and analytics for different network and signal configurations in indoor and outdoor scenarios with center frequencies from micro-waves in frequency range 1 (FR1) to millimeter-waves in frequency range 2 (FR2). Position estimates obtained via soft information-based localization and wireless channel quality indicators via blockage intelligence are also provided. The rich set of data provided by xG-Loc enables the characterization of localization algorithms and services under common 3GPP-standardized scenarios in xG networks.
{"title":"3GPP-Compliant Datasets for xG Location-Aware Networks","authors":"Andrea Conti;Gianluca Torsoli;Carlos A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;Moe Z. Win","doi":"10.1109/OJVT.2023.3340993","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3340993","url":null,"abstract":"Location awareness is vital in next generation (xG) wireless networks to enable different use cases, including location-based services (LBSs) and efficient network management. However, achieving the service level requirements specified by the 3rd Generation Partnership Project (3GPP) is challenging. This calls for new localization algorithms as well as for 3GPP-standardized scenarios to support their systematic development and testing. In this context, the availability of public datasets with 3GPP-compliant configurations is essential to advance the evolution of xG networks. This paper introduces xG-Loc, the first open dataset for localization algorithms and services fully compliant with 3GPP technical reports and specifications. xG-Loc includes received localization signals, measurements, and analytics for different network and signal configurations in indoor and outdoor scenarios with center frequencies from micro-waves in frequency range 1 (FR1) to millimeter-waves in frequency range 2 (FR2). Position estimates obtained via soft information-based localization and wireless channel quality indicators via blockage intelligence are also provided. The rich set of data provided by xG-Loc enables the characterization of localization algorithms and services under common 3GPP-standardized scenarios in xG networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"473-484"},"PeriodicalIF":6.4,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10349917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140339974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1109/OJVT.2023.3339799
Huixin Xu;Jianhua Zhang;Pan Tang;Lei Tian;Qixing Wang;Guangyi Liu
The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band; A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity; Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval; Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.
{"title":"An Empirical Study on Channel Reciprocity in TDD and FDD Systems","authors":"Huixin Xu;Jianhua Zhang;Pan Tang;Lei Tian;Qixing Wang;Guangyi Liu","doi":"10.1109/OJVT.2023.3339799","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3339799","url":null,"abstract":"The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band; A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity; Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval; Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"108-124"},"PeriodicalIF":6.4,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345764","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1109/OJVT.2023.3337357
Yingshuang Bai;Jiawei Zhang;Chen Sun;Le Zhao;Haojin Li;Xiaoxue Wang
Artificial Intelligence (AI) has gained significant attention and extensive research across various fields in recent years. In the realm of wireless communication, researchers are exploring the use of AI to facilitate various physical layer (PHY) procedures. Within the standardization efforts of the Third Generation Partnership Project (3GPP), one prominent direction being explored is AI-based beam management (BM). The primary objective is to harness AI techniques for predicting optimal beams, thereby reducing measurement overhead and latency. This paper aims to discuss the progress made in AI-based beam management within the Release 18 standardization. Furthermore, through our research, we have identified the mobile speed of user equipment (UE) as a crucial factor that impacts the optimal time window size for collecting input data in AI models. We have observed an inverse correlation between UE speed and the time window size. Accordingly, to mitigate unnecessary measurement overhead and latency, we propose that the determination of the time window size for input data collection should be based on the UE speed. Additionally, we will present our simulation results and provide a comprehensive analysis of this relationship.
{"title":"AI-Based Beam Management in 3GPP: Optimizing Data Collection Time Window for Temporal Beam Prediction","authors":"Yingshuang Bai;Jiawei Zhang;Chen Sun;Le Zhao;Haojin Li;Xiaoxue Wang","doi":"10.1109/OJVT.2023.3337357","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3337357","url":null,"abstract":"Artificial Intelligence (AI) has gained significant attention and extensive research across various fields in recent years. In the realm of wireless communication, researchers are exploring the use of AI to facilitate various physical layer (PHY) procedures. Within the standardization efforts of the Third Generation Partnership Project (3GPP), one prominent direction being explored is AI-based beam management (BM). The primary objective is to harness AI techniques for predicting optimal beams, thereby reducing measurement overhead and latency. This paper aims to discuss the progress made in AI-based beam management within the Release 18 standardization. Furthermore, through our research, we have identified the mobile speed of user equipment (UE) as a crucial factor that impacts the optimal time window size for collecting input data in AI models. We have observed an inverse correlation between UE speed and the time window size. Accordingly, to mitigate unnecessary measurement overhead and latency, we propose that the determination of the time window size for input data collection should be based on the UE speed. Additionally, we will present our simulation results and provide a comprehensive analysis of this relationship.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"48-55"},"PeriodicalIF":6.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1109/OJVT.2023.3335180
Amit Chougule;Vinay Chamola;Aishwarya Sam;Fei Richard Yu;Biplab Sikdar
The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, self-driving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.
{"title":"A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions","authors":"Amit Chougule;Vinay Chamola;Aishwarya Sam;Fei Richard Yu;Biplab Sikdar","doi":"10.1109/OJVT.2023.3335180","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3335180","url":null,"abstract":"The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, self-driving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"142-161"},"PeriodicalIF":6.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Next-generation Internet of Things (IoT) systems require faster data transmission, support for moving objects, and long-distance transmission when compared to the currently available IoT systems. The IEEE 802.15.4 smart utility network (SUN) orthogonal frequency-division multiplexing (OFDM) can satisfy these requirements. Mobile-communication-oriented receiver systems are typically used in urban environments for SUN OFDM. However, the evaluation depends on computer simulations and requires an experimental evaluation platform based on software-defined radio (SDR) that can modify transmitter-receiver functions. We present a platform for SUN OFDM that enables high-speed mobile communication. The proposed platform comprises a signal generator-based transmitter and an SDR-based receiver; the receiver baseband signal processing is performed by MATLAB. We also proposed signal processing functions that can receive the SUN OFDM packets even at speeds of tens of km/h. We applied a simplified universal time-domain windowed (UTW)-OFDM scheme to this platform to operate even at sub-1 GHz, where the spectrum mask is more limited. In the experimental evaluation, the required packet error rate for SUN OFDM was achieved in an 80 km/h multipath fading environment, and out-of-band emission can be suppressed by over 43 dB from the peak power while achieving performance equivalent to that without applying the simplified UTW.
与目前可用的物联网系统相比，下一代物联网（IoT）系统需要更快的数据传输、支持移动物体和长距离传输。IEEE 802.15.4 智能公用事业网络（SUN）正交频分复用（OFDM）可满足这些要求。面向移动通信的接收器系统通常用于城市环境中的 SUN OFDM。然而，评估依赖于计算机模拟，需要一个基于软件定义无线电（SDR）的实验评估平台，该平台可修改发射机-接收机功能。我们提出了一个可实现高速移动通信的 SUN OFDM 平台。所提议的平台包括一个基于信号发生器的发射器和一个基于 SDR 的接收器；接收器的基带信号处理由 MATLAB 完成。我们还提出了信号处理功能，即使在几十公里/小时的速度下也能接收 SUN OFDM 数据包。我们将简化的通用时域窗口（UTW）-OFDM 方案应用于该平台，即使在频谱掩码较为有限的 1 GHz 以下频率也能运行。在实验评估中，SUN OFDM 在 80 km/h 的多径衰落环境中达到了所需的数据包错误率，带外发射的峰值功率被抑制了 43 dB 以上，性能与未应用简化 UTW 时的性能相当。
{"title":"Software-Defined Radio-Based IEEE 802.15.4 SUN OFDM Evaluation Platform for Highly Mobile Environments","authors":"Keito Nakura;Shota Mori;Hiroko Masaki;Hiroshi Harada","doi":"10.1109/OJVT.2023.3337315","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3337315","url":null,"abstract":"Next-generation Internet of Things (IoT) systems require faster data transmission, support for moving objects, and long-distance transmission when compared to the currently available IoT systems. The IEEE 802.15.4 smart utility network (SUN) orthogonal frequency-division multiplexing (OFDM) can satisfy these requirements. Mobile-communication-oriented receiver systems are typically used in urban environments for SUN OFDM. However, the evaluation depends on computer simulations and requires an experimental evaluation platform based on software-defined radio (SDR) that can modify transmitter-receiver functions. We present a platform for SUN OFDM that enables high-speed mobile communication. The proposed platform comprises a signal generator-based transmitter and an SDR-based receiver; the receiver baseband signal processing is performed by MATLAB. We also proposed signal processing functions that can receive the SUN OFDM packets even at speeds of tens of km/h. We applied a simplified universal time-domain windowed (UTW)-OFDM scheme to this platform to operate even at sub-1 GHz, where the spectrum mask is more limited. In the experimental evaluation, the required packet error rate for SUN OFDM was achieved in an 80 km/h multipath fading environment, and out-of-band emission can be suppressed by over 43 dB from the peak power while achieving performance equivalent to that without applying the simplified UTW.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"95-107"},"PeriodicalIF":6.4,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10329434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.1109/OJVT.2023.3336619
Remon Polus;Claude D'Amours
In this article, an unmanned aerial vehicle (UAV), acting as a transmitter, employs different power adaptation strategies in order to enhance the ergodic capacity of the wireless channel between it and a receiver on the ground. We present the derivation of closed-form expressions for the channel capacity of the recently developed UAV-to-ground fading channels under different power adaptation strategies. The power adaptation strategies considered in this paper are optimal rate adaptation with fixed power (ORA), optimal power and rate adaptation (OPRA), channel inversion with fixed rate (CIFR), and truncated channel inversion with fixed rate (TIFR). In addition to ergodic capacity analysis, precise analytical formulas for the effective capacity of the UAV-to-ground fading channels are derived. Additionally, all of these closed-form expressions are verified by comparing them with numerical results obtained through Monte Carlo simulations.
{"title":"Capacity Analysis of UAV-to-Ground Channels With Shadowing: Power Adaptation Schemes and Effective Capacity","authors":"Remon Polus;Claude D'Amours","doi":"10.1109/OJVT.2023.3336619","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3336619","url":null,"abstract":"In this article, an unmanned aerial vehicle (UAV), acting as a transmitter, employs different power adaptation strategies in order to enhance the ergodic capacity of the wireless channel between it and a receiver on the ground. We present the derivation of closed-form expressions for the channel capacity of the recently developed UAV-to-ground fading channels under different power adaptation strategies. The power adaptation strategies considered in this paper are optimal rate adaptation with fixed power (ORA), optimal power and rate adaptation (OPRA), channel inversion with fixed rate (CIFR), and truncated channel inversion with fixed rate (TIFR). In addition to ergodic capacity analysis, precise analytical formulas for the effective capacity of the UAV-to-ground fading channels are derived. Additionally, all of these closed-form expressions are verified by comparing them with numerical results obtained through Monte Carlo simulations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"71-77"},"PeriodicalIF":6.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10328795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.1109/OJVT.2023.3335358
Khaled A. Alaghbari;Heng Siong Lim;Benzhou Jin;Yutong Shen
Source separation of a mixed signal in the time-frequency domain is critical for joint communication and radar (JCR) systems to achieve the required performance, especially at a low signal-to-noise ratio (SNR). In this paper, we propose the use of a generative model, such as the unsupervised variational autoencoder (VAE), to separate sensing and data communication signals. We first analyse the VAE system using different mask techniques; then, the best technique is selected for comparison with popular blind source separation (BSS) algorithms. We verify the performance of the proposed VAE by adopting different metrics such as the signal-to-distortion ratio (SDR), source-to-interference ratio (SIR), and sources-to-artifacts ratio (SAR). Simulation results show that the proposed VAE outperforms the BSS techniques at low SNR for the case of a mixed signal in the time-frequency domain and at low and high SNR for a mixed signal in the time domain. It enables the JCR system in the challenging first scenario to obtain SDR gains of 11.1 dB and 6 dB at 0 dB SNR for recovering the sensing and data communication signals respectively. Finally, we analyse the robustness of the JCR system in detecting an interference signal operating in the same frequency band, where the simulation result indicates an accuracy of 91% based on the proposed steps.
{"title":"Source Separation in Joint Communication and Radar Systems Based on Unsupervised Variational Autoencoder","authors":"Khaled A. Alaghbari;Heng Siong Lim;Benzhou Jin;Yutong Shen","doi":"10.1109/OJVT.2023.3335358","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3335358","url":null,"abstract":"Source separation of a mixed signal in the time-frequency domain is critical for joint communication and radar (JCR) systems to achieve the required performance, especially at a low signal-to-noise ratio (SNR). In this paper, we propose the use of a generative model, such as the unsupervised variational autoencoder (VAE), to separate sensing and data communication signals. We first analyse the VAE system using different mask techniques; then, the best technique is selected for comparison with popular blind source separation (BSS) algorithms. We verify the performance of the proposed VAE by adopting different metrics such as the signal-to-distortion ratio (SDR), source-to-interference ratio (SIR), and sources-to-artifacts ratio (SAR). Simulation results show that the proposed VAE outperforms the BSS techniques at low SNR for the case of a mixed signal in the time-frequency domain and at low and high SNR for a mixed signal in the time domain. It enables the JCR system in the challenging first scenario to obtain SDR gains of 11.1 dB and 6 dB at 0 dB SNR for recovering the sensing and data communication signals respectively. Finally, we analyse the robustness of the JCR system in detecting an interference signal operating in the same frequency band, where the simulation result indicates an accuracy of 91% based on the proposed steps.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"56-70"},"PeriodicalIF":6.4,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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