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":null,"pages":null},"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":null,"pages":null},"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.2023.3335180
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":null,"pages":null},"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}
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":null,"pages":null},"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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
Pub Date : 2023-11-20DOI: 10.1109/OJVT.2023.3334822
Xinyu Feng;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo
Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multi-dimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.
索引调制（IM）是一种灵活的传输方案，能够在性能、吞吐量、多样性和复杂性之间进行灵活权衡。多维 IM（MIM）概念的提出，将 IM 在空间和频率等多个维度上的优势结合在一起。此外，压缩传感（CS）可与 IM 有效结合，以提高吞吐量。然而，准确的信道状态信息（CSI）对可靠的 MIM 至关重要，而这需要很高的先导开销。因此，联合信道估计和检测（Joint Channel Estimation and Detection，JCED）被用来减少先导开销，并在适度增加估计复杂度的情况下提高检测性能。随后，我们提出了基于深度学习（DL）的联合信道估计和检测（JCED），用于 CS 辅助 MIM（CS-MIM），以显著降低复杂性，同时减少信道估计（CE）所需的先导开销。此外，我们还设想了训练辅助软决策（SD）检测。我们首先分析了传统联合 CE 和 SD 检测的复杂性，然后提出了我们的降低复杂性的学习辅助联合 CE 和 SD 检测。我们的仿真结果证实，深度神经网络（DNN）能够以较低的先导开销和较低的复杂度对 CS-MIM 进行接近容量的 JCED，包括硬判定（HD）和软判定（SD）检测。
{"title":"Reduced Complexity Learning-Assisted Joint Channel Estimation and Detection of Compressed Sensing-Aided Multi-Dimensional Index Modulation","authors":"Xinyu Feng;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2023.3334822","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3334822","url":null,"abstract":"Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multi-dimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822281","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 : 2023-11-17DOI: 10.1109/OJVT.2023.3333676
DOMENICO LOFÙ;Pietro Di Gennaro;Pietro Tedeschi;Tommaso Di Noia;Eugenio Di Sciascio
Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with 90% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE $approx 0.29$ , MAE $approx 0.04$ , and $R^{2}approx 0.93$ . Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.
{"title":"URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles","authors":"DOMENICO LOFÙ;Pietro Di Gennaro;Pietro Tedeschi;Tommaso Di Noia;Eugenio Di Sciascio","doi":"10.1109/OJVT.2023.3333676","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3333676","url":null,"abstract":"Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with 90% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE \u0000<inline-formula><tex-math>$approx 0.29$</tex-math></inline-formula>\u0000, MAE \u0000<inline-formula><tex-math>$approx 0.04$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$R^{2}approx 0.93$</tex-math></inline-formula>\u0000. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138491044","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 : 2023-11-09DOI: 10.1109/OJVT.2023.3331185
Mohamed Elsayed;Ahmad A. Aziz El-Banna;Octavia A. Dobre;Wan Yi Shiu;Peiwei Wang
In contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant potential, FD systems suffer from an inherent self-interference (SI) due to a coupling of the transmit signal to its own FD receive chain. Self-interference cancellation (SIC) techniques are the key enablers for realizing the FD operation, and they could be implemented in the propagation, analog, and/or digital domains. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be insufficient to seize the growing complexity of forthcoming communication systems. For the time being, machine learning (ML) data-driven approaches have been introduced for digital SIC to overcome the complexity hurdles of traditional methods. This article reviews and summarizes the recent advances in applying ML to SIC in FD systems. Further, it analyzes the performance of various ML approaches using different performance metrics, such as the achieved SIC, training overhead, memory storage, and computational complexity. Finally, this article discusses the challenges of applying ML-based techniques to SIC, highlights their potential solutions, and provides a guide for future research directions.
长期以来，人们一直认为无线系统只能在半双工模式下工作，而全双工（FD）系统却能在同一频段上同时发送和接收信息，理论上可将频谱效率提高两倍。尽管全双工系统具有巨大的潜力，但由于发射信号与自身的全双工接收链耦合，全双工系统存在固有的自干扰（SI）问题。自干扰消除（SIC）技术是实现 FD 操作的关键因素，可以在传播、模拟和/或数字域中实现。特别是，数字域的干扰消除通常采用模型驱动方法，但事实证明，这种方法不足以应对即将到来的通信系统日益增长的复杂性。目前，针对数字 SIC 引入了机器学习（ML）数据驱动方法，以克服传统方法的复杂性障碍。本文回顾并总结了将 ML 应用于 FD 系统中 SIC 的最新进展。此外，文章还使用不同的性能指标分析了各种 ML 方法的性能，如实现的 SIC、训练开销、内存存储和计算复杂度。最后，本文讨论了将基于 ML 的技术应用于 SIC 所面临的挑战，强调了其潜在的解决方案，并为未来的研究方向提供了指导。
{"title":"Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions","authors":"Mohamed Elsayed;Ahmad A. Aziz El-Banna;Octavia A. Dobre;Wan Yi Shiu;Peiwei Wang","doi":"10.1109/OJVT.2023.3331185","DOIUrl":"10.1109/OJVT.2023.3331185","url":null,"abstract":"In contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant potential, FD systems suffer from an inherent self-interference (SI) due to a coupling of the transmit signal to its own FD receive chain. Self-interference cancellation (SIC) techniques are the key enablers for realizing the FD operation, and they could be implemented in the propagation, analog, and/or digital domains. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be insufficient to seize the growing complexity of forthcoming communication systems. For the time being, machine learning (ML) data-driven approaches have been introduced for digital SIC to overcome the complexity hurdles of traditional methods. This article reviews and summarizes the recent advances in applying ML to SIC in FD systems. Further, it analyzes the performance of various ML approaches using different performance metrics, such as the achieved SIC, training overhead, memory storage, and computational complexity. Finally, this article discusses the challenges of applying ML-based techniques to SIC, highlights their potential solutions, and provides a guide for future research directions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10314438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562407","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：