Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.
{"title":"Edge Information Hub-Empowered 6G NTN: Latency-Oriented Resource Orchestration and Configuration","authors":"Yueshan Lin;Wei Feng;Yunfei Chen;Ning Ge;Zhiyong Feng;Yue Gao","doi":"10.1109/OJCOMS.2024.3423363","DOIUrl":"10.1109/OJCOMS.2024.3423363","url":null,"abstract":"Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10585305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552378","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}
Accurate sensing and localisation are considered as necessary features of future communication systems, including 6G. To harness the full potential of radio frequency (RF) and optical wireless communication (OWC), the localisation of user devices is essential, which further facilitates efficient beam steering, handover, and resource allocation. In this paper, we have considered a practical scenario where users are mobile with random device orientation. A convolutional neural network (CNN) is introduced to estimate the user position and orientation based on the received signal strength (RSS). CNN demonstrates superior performance in optical wireless positioning by proficiently extracting features from only RSS data. According to the simulation results it is observed that, by adjusting the structure of the dataset, a significant improvement in the estimation of the location is obtained in comparison with previous methods. We also consider having the noisy orientation data from the device sensors and investigate localisation performance in such a scenario. Finally, the impact of configuration of access points (APs) on the model is studied. This work demonstrates that a low-complexity accurate localisation, with average error as low as 1.8 cm, is indeed feasible.
{"title":"Optical Wireless 3-D-Positioning and Device Orientation Estimation","authors":"Yifan Huang;Majid Safari;Harald Haas;Iman Tavakkolnia","doi":"10.1109/OJCOMS.2024.3423420","DOIUrl":"10.1109/OJCOMS.2024.3423420","url":null,"abstract":"Accurate sensing and localisation are considered as necessary features of future communication systems, including 6G. To harness the full potential of radio frequency (RF) and optical wireless communication (OWC), the localisation of user devices is essential, which further facilitates efficient beam steering, handover, and resource allocation. In this paper, we have considered a practical scenario where users are mobile with random device orientation. A convolutional neural network (CNN) is introduced to estimate the user position and orientation based on the received signal strength (RSS). CNN demonstrates superior performance in optical wireless positioning by proficiently extracting features from only RSS data. According to the simulation results it is observed that, by adjusting the structure of the dataset, a significant improvement in the estimation of the location is obtained in comparison with previous methods. We also consider having the noisy orientation data from the device sensors and investigate localisation performance in such a scenario. Finally, the impact of configuration of access points (APs) on the model is studied. This work demonstrates that a low-complexity accurate localisation, with average error as low as 1.8 cm, is indeed feasible.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10585312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546333","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 : 2024-07-03DOI: 10.1109/OJCOMS.2024.3422872
O. S. Peñaherrera-Pulla;Carlos Baena;Sergio Fortes;Raquel Barco
The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET_{score}, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR’s. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is publicly accessible and properly referenced. Furthermore, this work serves as a foundation for future research on end-to-end (E2E) quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).
扩展现实（XR）等尖端技术和服务的出现有望改变人们的日常生活方式。与此同时，现代分散式架构方法的出现带来了新一代移动网络，如 5G，并勾勒出 B5G（Beyond-5G）和进一步发展的路线图。这些网络有望成为实现元宇宙和其他未来服务的推动力。在此背景下，量化服务性能是实现动态、环境适应和主动网络管理的关键因素。这项工作提出了一个基于 ML（机器学习）的框架，该框架利用无线电测量、统计和配置参数等网络数据来推断适合各种 XR 关键质量指标（KQI）的最佳 ML 模型。输出模型集成了可增强模型大小和性能的特征工程技术。建议的框架包括数据预处理、模型定义、训练、调整和验证。此外，为了选择最佳的组合算法，本研究还引入了一个名为 PET_{score} 的指标，从误差性能和预测时间的角度对候选算法进行评估。这些都是像 XR 这样的时间敏感型服务需要考虑的因素。为了验证我们的建议，我们选择了 360 视频服务，通过真实的 XR 用例来展示这一 ML 框架的潜力。此外，为评估用例而生成的数据集是可公开访问的，并有适当的参考文献。此外，这项工作还为未来研究基于端到端（E2E）体验质量（QoE）的网络管理以及其他使能技术（包括网络切片、虚拟化和多接入边缘计算（MEC））奠定了基础。
{"title":"ML-Powered KQI Estimation for XR Services: A Case Study on 360-Video","authors":"O. S. Peñaherrera-Pulla;Carlos Baena;Sergio Fortes;Raquel Barco","doi":"10.1109/OJCOMS.2024.3422872","DOIUrl":"10.1109/OJCOMS.2024.3422872","url":null,"abstract":"The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET_{score}, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR’s. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is publicly accessible and properly referenced. Furthermore, this work serves as a foundation for future research on end-to-end (E2E) quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546335","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}
Orthogonal time frequency space (OTFS) modulation is strongly considered as a promising solution for high-mobility communications. In contrast to conventional modulation techniques, wherein information symbols are multiplexed in a one-dimensional time or frequency domain, OTFS employs a two-dimensional modulation scheme by multiplexing information symbols in the delay-Doppler domain. This paper presents a comprehensive survey of OTFS. It starts with an overview of OTFS, its advantages over conventional air interface techniques, general block diagrams, and implementations. Subsequently, the paper explores the potential integration of multiple-input multiple-output and OTFS techniques. The paper further discusses the feasibility of integrating OTFS into multiple access techniques as a solution for maintaining acceptable performance in high-mobility scenarios. Then, widespread applications of OTFS in satellite communications are highlighted. Also, the potential utilization of OTFS modulation in integrated sensing and communications paradigm is thoroughly treated. In addition, the survey covers further applications of OTFS in deep learning, index modulation, underwater acoustic, and unmanned aerial vehicle communications. The paper concludes by pointing out numerous challenging and promising directions for future OTFS research.
{"title":"A Survey on Orthogonal Time Frequency Space Modulation","authors":"Mahmoud Aldababsa;Serdar Özyurt;Güneş Karabulut Kurt;Oğuz Kucur","doi":"10.1109/OJCOMS.2024.3422801","DOIUrl":"10.1109/OJCOMS.2024.3422801","url":null,"abstract":"Orthogonal time frequency space (OTFS) modulation is strongly considered as a promising solution for high-mobility communications. In contrast to conventional modulation techniques, wherein information symbols are multiplexed in a one-dimensional time or frequency domain, OTFS employs a two-dimensional modulation scheme by multiplexing information symbols in the delay-Doppler domain. This paper presents a comprehensive survey of OTFS. It starts with an overview of OTFS, its advantages over conventional air interface techniques, general block diagrams, and implementations. Subsequently, the paper explores the potential integration of multiple-input multiple-output and OTFS techniques. The paper further discusses the feasibility of integrating OTFS into multiple access techniques as a solution for maintaining acceptable performance in high-mobility scenarios. Then, widespread applications of OTFS in satellite communications are highlighted. Also, the potential utilization of OTFS modulation in integrated sensing and communications paradigm is thoroughly treated. In addition, the survey covers further applications of OTFS in deep learning, index modulation, underwater acoustic, and unmanned aerial vehicle communications. The paper concludes by pointing out numerous challenging and promising directions for future OTFS research.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546334","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}
With the deployment of satellite constellations, Internet-of-Things (IoT) devices in remote areas have gained access to low-cost network connectivity. In this paper, we investigate the performance of IoT devices connecting in up-link through low Earth orbit (LEO) satellites to geosynchronous equatorial orbit (GEO) links. We model the dynamic LEO satellite constellation using the stochastic geometry method and provide an analysis of end-to-end availability with low-complexity and coverage performance estimates for the mentioned link. Based on the analytical expressions derived in this research, we make a sound investigation on the impact of constellation configuration, transmission power, and the relative positions of IoT devices and GEO satellites on end-to-end performance.
{"title":"End-to-End Uplink Performance Analysis of Satellite-Based IoT Networks: A Stochastic Geometry Approach","authors":"Jiusi Zhou;Ruibo Wang;Basem Shihada;Mohamed-Slim Alouini","doi":"10.1109/OJCOMS.2024.3422110","DOIUrl":"10.1109/OJCOMS.2024.3422110","url":null,"abstract":"With the deployment of satellite constellations, Internet-of-Things (IoT) devices in remote areas have gained access to low-cost network connectivity. In this paper, we investigate the performance of IoT devices connecting in up-link through low Earth orbit (LEO) satellites to geosynchronous equatorial orbit (GEO) links. We model the dynamic LEO satellite constellation using the stochastic geometry method and provide an analysis of end-to-end availability with low-complexity and coverage performance estimates for the mentioned link. Based on the analytical expressions derived in this research, we make a sound investigation on the impact of constellation configuration, transmission power, and the relative positions of IoT devices and GEO satellites on end-to-end performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10580980","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528064","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 : 2024-07-02DOI: 10.1109/OJCOMS.2024.3421901
Maria Hanif;Rizwan Ahmad;Waqas Ahmed;Micheal Drieberg;Muhammad Mahtab Alam
Wireless Body Area Networks (WBANs) have significantly enhanced various aspects of human life, particularly in healthcare, fitness, entertainment, sports, and etc. In WBANs, the sensor nodes are placed in and around the body along with the sink node, which collects the physiological data from these sensors and forwards it for further processing. The placement of the sink node is one of the critical aspects in the design of WABNs as it affects both the energy efficiency and connectivity. To this end, this paper introduces a hybrid method called Distance and Angulation based AGglomerative Clustering (DAAG). DAAG, initially clusters the WBAN sensors using Distance and Angulation based k-Mean clustering. Afterward, Agglomerative Clustering is applied to determine the optimal placement of the sink node. The results of DAAG are compared with various machine learning and optimization approaches, including D-RMS (Distance based Random mean shift clustering), Reinforcement Q-Learning Approach (QL), Humpback Whale optimization (HWOA), Multi-Angulation (MA) and Closeness Centrality (CC). Given an initial energy, the results show that the DAAG exhibits superior performance in terms of latency, packet error rate (PER), and energy consumption. DAAG shows an energy consumption of only 1.51% outperforming QL, HWOA, MA, CC, and D-RMS along with an improved localization accuracy of 0.36 m.
{"title":"DAAG-SNP: Energy Efficient Distance and Angulation-Based Agglomerative Clustering for Sink Node Placement","authors":"Maria Hanif;Rizwan Ahmad;Waqas Ahmed;Micheal Drieberg;Muhammad Mahtab Alam","doi":"10.1109/OJCOMS.2024.3421901","DOIUrl":"10.1109/OJCOMS.2024.3421901","url":null,"abstract":"Wireless Body Area Networks (WBANs) have significantly enhanced various aspects of human life, particularly in healthcare, fitness, entertainment, sports, and etc. In WBANs, the sensor nodes are placed in and around the body along with the sink node, which collects the physiological data from these sensors and forwards it for further processing. The placement of the sink node is one of the critical aspects in the design of WABNs as it affects both the energy efficiency and connectivity. To this end, this paper introduces a hybrid method called Distance and Angulation based AGglomerative Clustering (DAAG). DAAG, initially clusters the WBAN sensors using Distance and Angulation based k-Mean clustering. Afterward, Agglomerative Clustering is applied to determine the optimal placement of the sink node. The results of DAAG are compared with various machine learning and optimization approaches, including D-RMS (Distance based Random mean shift clustering), Reinforcement Q-Learning Approach (QL), Humpback Whale optimization (HWOA), Multi-Angulation (MA) and Closeness Centrality (CC). Given an initial energy, the results show that the DAAG exhibits superior performance in terms of latency, packet error rate (PER), and energy consumption. DAAG shows an energy consumption of only 1.51% outperforming QL, HWOA, MA, CC, and D-RMS along with an improved localization accuracy of 0.36 m.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10580965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528065","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 : 2024-07-02DOI: 10.1109/OJCOMS.2024.3422030
Jose Manuel Gimenez-Guzman;Israel Leyva-Mayorga;Amirhossein Azarbahram;Onel Alcaraz López;Petar Popovski
Future intelligent transportation systems will require complex networking infrastructures with communication among a huge number of vehicles and roadside nodes to support services such as autonomous driving. However, the deployment and operation of such a large number of roadside nodes is expensive due to either the cost of battery replacement or the maintenance of a continuous energy supply in long highways or rural areas. In this work, we evaluate the feasibility of a roadside unit harvesting energy from radio frequency (RF) signals transmitted by a nearby moving vehicle, with the incentive of using a part of the harvested energy to transmit small amounts of data to the vehicle. We consider a realistic model with the timing elements related to the movement of the vehicle, beam tracking errors, a non-linear model for energy harvesting, and potential line-of-sight obstructions in multi-vehicle scenarios. Results show that, with typical off-the-shelf components, it is feasible to use the RF harvested energy to transmit between a few hundred and several thousand bytes, depending on the speed of vehicles and the frequency of operation for energy harvesting, among other parameters.
{"title":"Energy-Autonomous Roadside Nodes in V2I Using RF Energy Harvesting","authors":"Jose Manuel Gimenez-Guzman;Israel Leyva-Mayorga;Amirhossein Azarbahram;Onel Alcaraz López;Petar Popovski","doi":"10.1109/OJCOMS.2024.3422030","DOIUrl":"10.1109/OJCOMS.2024.3422030","url":null,"abstract":"Future intelligent transportation systems will require complex networking infrastructures with communication among a huge number of vehicles and roadside nodes to support services such as autonomous driving. However, the deployment and operation of such a large number of roadside nodes is expensive due to either the cost of battery replacement or the maintenance of a continuous energy supply in long highways or rural areas. In this work, we evaluate the feasibility of a roadside unit harvesting energy from radio frequency (RF) signals transmitted by a nearby moving vehicle, with the incentive of using a part of the harvested energy to transmit small amounts of data to the vehicle. We consider a realistic model with the timing elements related to the movement of the vehicle, beam tracking errors, a non-linear model for energy harvesting, and potential line-of-sight obstructions in multi-vehicle scenarios. Results show that, with typical off-the-shelf components, it is feasible to use the RF harvested energy to transmit between a few hundred and several thousand bytes, depending on the speed of vehicles and the frequency of operation for energy harvesting, among other parameters.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10580985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527859","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 : 2024-07-01DOI: 10.1109/OJCOMS.2024.3421519
Randy Verdecia-Peña;Rodolfo Oliveira;José I. Alonso
This paper introduces a novel methodology for wireless channel estimation in millimeter-wave (mmWave) bands, with a primary focus on addressing diverse physical (PHY)-layer impairments, including phase noise (PN), in-phase and quadrature-phase imbalance (IQI), carrier frequency offset (CFO), and power amplifier non-linearity (PAN). The key contribution centers around the innovative approach of training a convolutional neural network (CNN) using a synthetic and labeled dataset that encompasses a wide range of wireless channel conditions. The methodology involves the synthetic generation of labeled datasets, representing various types of wireless channels and PHY-layer impairments, which are subsequently employed in the CNN training stage. The resulting model-based trained CNN demonstrates exceptional adaptability to diverse operational scenarios, showcasing its capability to operate effectively under various channel conditions. To validate the efficacy of the proposed methodology, the trained CNN is deployed in a practical wireless testbed. Experimental results underscore the superiority of the proposed channel estimation methodology across different signal-to-noise ratio (SNR) regions and delay spread channel types. The trained CNN exhibits robust performance, confirming its effectiveness in mitigating the impact of PHY-layer impairments in real-world mmWave communication environments. This research not only advances reliable channel estimation techniques for mmWave systems but also provides valuable practical assessment results, with potential applications in next-generation wireless communication networks.
{"title":"Enhancing mmWave Channel Estimation: A Practical Experimentation Approach With Modeled Physical Layer Impairments Incorporated in Deep Learning Training","authors":"Randy Verdecia-Peña;Rodolfo Oliveira;José I. Alonso","doi":"10.1109/OJCOMS.2024.3421519","DOIUrl":"10.1109/OJCOMS.2024.3421519","url":null,"abstract":"This paper introduces a novel methodology for wireless channel estimation in millimeter-wave (mmWave) bands, with a primary focus on addressing diverse physical (PHY)-layer impairments, including phase noise (PN), in-phase and quadrature-phase imbalance (IQI), carrier frequency offset (CFO), and power amplifier non-linearity (PAN). The key contribution centers around the innovative approach of training a convolutional neural network (CNN) using a synthetic and labeled dataset that encompasses a wide range of wireless channel conditions. The methodology involves the synthetic generation of labeled datasets, representing various types of wireless channels and PHY-layer impairments, which are subsequently employed in the CNN training stage. The resulting model-based trained CNN demonstrates exceptional adaptability to diverse operational scenarios, showcasing its capability to operate effectively under various channel conditions. To validate the efficacy of the proposed methodology, the trained CNN is deployed in a practical wireless testbed. Experimental results underscore the superiority of the proposed channel estimation methodology across different signal-to-noise ratio (SNR) regions and delay spread channel types. The trained CNN exhibits robust performance, confirming its effectiveness in mitigating the impact of PHY-layer impairments in real-world mmWave communication environments. This research not only advances reliable channel estimation techniques for mmWave systems but also provides valuable practical assessment results, with potential applications in next-generation wireless communication networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528071","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 : 2024-07-01DOI: 10.1109/ojcoms.2024.3420102
Juncheng Li, Shenjie Huang, Mohammad Dehghani Soltani, Harald Haas, Majid Safari
{"title":"Laser-Based Indoor Mobile Wireless Communication Aided by Stabilizers","authors":"Juncheng Li, Shenjie Huang, Mohammad Dehghani Soltani, Harald Haas, Majid Safari","doi":"10.1109/ojcoms.2024.3420102","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3420102","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528066","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 : 2024-07-01DOI: 10.1109/OJCOMS.2024.3421518
Eito Kurihara;Hideki Ochiai
In this work, we investigate the performance of geometric constellation shaping for highorder coded quadrature amplitude modulation (QAM) over an additive white Gaussian noise (AWGN) channel. We focus on a systematic design where a single parameter uniquely determines the entire constellation points according to the truncated Gaussian distribution, and the parameter is optimized based on the resulting mutual information. Our main objective is to combine the proposed systematic geometric shaping with practical coded modulation so as to achieve high bandwidth efficiency with low design/decoding complexity. To this end, we investigate the use of multilevel coding (MLC) under multistage decoding (MSD) as well as bit-interleaved coded modulation (BICM), along with pulse amplitude modulation (PAM) consisting of as much as 128 signal points, i.e., leading to 16 384-ary QAM in the two-dimensional case. Our comparative studies employing the off-the-shelf binary punctured turbo codes show that, as we target higher spectral efficiency, MLC with MSD is more attractive than BICM in view of both bit error rate (BER) performance and decoding complexity. In addition, we introduce new closed-form bounds related to constellation constrained capacity, based on which one can quickly assess the capacity behavior of given discrete PAM constellations.
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