Pub Date : 2024-08-28DOI: 10.1109/OJCOMS.2024.3450809
Mohammed S. Elbasheir;Rashid A. Saeed;Salaheldin Edam
Nowadays, significant developments in wireless technologies and solutions have led to the rapid expansion of mobile networks, and it’s expected to grow more, particularly with the launch of the Fifth Generation New Radio (5G NR). The deployment of a large number of base stations (BSs) is raising concerns about the potential for increased exposure to electromagnetic field radiation (EMF). Many international and national regulators have set guidelines and regulations to control the amount of EMF radiation. This paper presents a design model to de-concentrate the total exposure from sectorized antennas of the multi-technology base station with no drawback on network coverage level and key performance indicators (KPIs). The model applies the concept of weighted antenna’s azimuth to spread the total exposure by horizontally separating the installed antennas in the same sector. A set of simulations is conducted to calculate the reduction in total exposure ratio (TER) for widely used setups in antenna deployment for multi-technology mobile networks. Additionally, A field test was done in a life network to evaluate the proposed model in the geographical cluster, and a set of field measurements was conducted to assess the TER and the compliance distance (CD) before and after the test implementation. Further, the operation support system (OSS) records and counters were analyzed to evaluate the impact on the network coverage and capacity behavior, especially for the carried traffic and number of users. The pre-and-post results show that the TER and CD are improved by a valuable reduction after applying the proposed model. Overall, the system records show no significant impacts were registered on network coverage level and capacity performance for all transmitting technologies of the sites involved in the test.
如今，无线技术和解决方案的重大发展导致了移动网络的快速扩张，尤其是随着第五代新无线电（5G NR）的推出，预计这种扩张将更加迅猛。大量基站（BS）的部署引发了人们对电磁场辐射（EMF）暴露可能增加的担忧。许多国际和国家监管机构已经制定了控制电磁场辐射量的指导方针和法规。本文提出了一种设计模型，可在不影响网络覆盖水平和关键性能指标（KPIs）的情况下，分散来自多技术基站扇区化天线的总辐射量。该模型应用了加权天线方位角的概念，通过水平分离同一扇区内已安装的天线来分散总曝光量。该模型进行了一系列模拟，以计算在多技术移动网络天线部署中广泛使用的设置所减少的总暴露率（TER）。此外，还在一个生活网络中进行了实地测试，以评估地理集群中的拟议模型，并进行了一系列实地测量，以评估测试实施前后的总暴露率和符合距离（CD）。此外，还分析了运营支持系统（OSS）的记录和计数器，以评估其对网络覆盖和容量行为的影响，尤其是对承载流量和用户数量的影响。前后结果表明，在应用建议的模型后，TER 和 CD 都有了可观的改善。总体而言，系统记录显示，对参与测试的所有站点的所有传输技术而言，网络覆盖水平和容量性能均未受到明显影响。
{"title":"Weighted Antenna’s Azimuth for Minimal EMF With Sustainable KPIs of Multi-Technology BS","authors":"Mohammed S. Elbasheir;Rashid A. Saeed;Salaheldin Edam","doi":"10.1109/OJCOMS.2024.3450809","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450809","url":null,"abstract":"Nowadays, significant developments in wireless technologies and solutions have led to the rapid expansion of mobile networks, and it’s expected to grow more, particularly with the launch of the Fifth Generation New Radio (5G NR). The deployment of a large number of base stations (BSs) is raising concerns about the potential for increased exposure to electromagnetic field radiation (EMF). Many international and national regulators have set guidelines and regulations to control the amount of EMF radiation. This paper presents a design model to de-concentrate the total exposure from sectorized antennas of the multi-technology base station with no drawback on network coverage level and key performance indicators (KPIs). The model applies the concept of weighted antenna’s azimuth to spread the total exposure by horizontally separating the installed antennas in the same sector. A set of simulations is conducted to calculate the reduction in total exposure ratio (TER) for widely used setups in antenna deployment for multi-technology mobile networks. Additionally, A field test was done in a life network to evaluate the proposed model in the geographical cluster, and a set of field measurements was conducted to assess the TER and the compliance distance (CD) before and after the test implementation. Further, the operation support system (OSS) records and counters were analyzed to evaluate the impact on the network coverage and capacity behavior, especially for the carried traffic and number of users. The pre-and-post results show that the TER and CD are improved by a valuable reduction after applying the proposed model. Overall, the system records show no significant impacts were registered on network coverage level and capacity performance for all transmitting technologies of the sites involved in the test.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5436-5451"},"PeriodicalIF":6.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143717","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449341
Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi
Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.
信道预测可以补偿多输入多输出（MIMO）系统中过时的信道状态信息。最近，人们利用无线信道的时间相关性，采用机器学习（ML）技术设计信道预测器。然而，大多数基于 ML 的信道预测技术在生成信道预测器时只考虑了离线训练，这可能导致在遇到与训练时不同的信道环境时性能不佳。为了确保在不同信道条件下的预测性能，我们提出了一个在线再训练框架，从头开始训练信道预测器，以有效捕捉和应对无线环境的变化。训练时间包括数据收集时间和神经网络训练时间，对于实用的信道预测器来说，训练时间应最小化。为了减少训练时间，尤其是数据收集时间，我们提出了一种基于 ML 的新型信道预测技术，即针对宽带大规模 MIMO 系统的聚合学习（AL）方法。在所提出的 AL 方法中，训练数据可以在阵列域或频域（即 MIMO-OFDM 系统的信道域）中进行拆分和聚合。这种处理方法可以大大减少数据收集的时间。我们的数值结果表明，AL 方法甚至能在各种情况下提高信道预测性能，而训练时间开销很小。
{"title":"Machine Learning-Based Channel Prediction in Wideband Massive MIMO Systems With Small Overhead for Online Training","authors":"Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi","doi":"10.1109/OJCOMS.2024.3449341","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449341","url":null,"abstract":"Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5289-5305"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10647106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143604","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449701
Vitali Korzhun;Andrea M. Tonello
In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).
{"title":"Cooperative Bit Allocation in In-Band Full-Duplex Power Line Communication","authors":"Vitali Korzhun;Andrea M. Tonello","doi":"10.1109/OJCOMS.2024.3449701","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449701","url":null,"abstract":"In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5306-5322"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143801","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449633
Karel Toledo;Jorge Torres Gómez;Falko Dressler;M. Julia Fernández-Getino García
The Internet of Things (IoT) enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as Internet of Mobile Things (IoMT), since their positions may be unknown. To deal with this uncertainty, we propose using the ordered weighted averaging (OWA) operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.
{"title":"Energy-Aware Cooperative Spectrum Sensing Under Ignorance on Internet of Mobile Things","authors":"Karel Toledo;Jorge Torres Gómez;Falko Dressler;M. Julia Fernández-Getino García","doi":"10.1109/OJCOMS.2024.3449633","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449633","url":null,"abstract":"The Internet of Things (IoT) enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as Internet of Mobile Things (IoMT), since their positions may be unknown. To deal with this uncertainty, we propose using the ordered weighted averaging (OWA) operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5323-5336"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143603","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3450348
Mohammad Javad Saber;Mazen Hasna
High-altitude platform (HAP) stations are pivotal in non-terrestrial networks, enhancing communication capabilities and extending cost-effective network access to rural or remote areas. HAP-assisted free-space optical (FSO) communications provide a promising solution for improving data rates. To safeguard against eavesdropping, especially during emergencies, we propose a physical-layer security mechanism to enhance the control signaling resilience in disaster response and network failure detection. We investigate the secrecy performance of an integrated HAP-based FSO and unmanned aerial vehicle (UAV)-enabled radio frequency (RF) downlink system using the decode-and-forward relaying protocol. While optical links inherently provide better security, our focus is on the eavesdropping threats to the RF link. We derive novel and exact analytical and asymptotic closed-form expressions for the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (PSPSC). Our results reveal the significant impact of atmospheric turbulence, RF fading, pointing errors, and optical detection technologies on the overall secrecy performance, providing valuable insights for designing secure mixed FSO and RF downlink communication systems.
{"title":"Security Analysis of Integrated HAP-Based FSO and UAV-Enabled RF Downlink Communications","authors":"Mohammad Javad Saber;Mazen Hasna","doi":"10.1109/OJCOMS.2024.3450348","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450348","url":null,"abstract":"High-altitude platform (HAP) stations are pivotal in non-terrestrial networks, enhancing communication capabilities and extending cost-effective network access to rural or remote areas. HAP-assisted free-space optical (FSO) communications provide a promising solution for improving data rates. To safeguard against eavesdropping, especially during emergencies, we propose a physical-layer security mechanism to enhance the control signaling resilience in disaster response and network failure detection. We investigate the secrecy performance of an integrated HAP-based FSO and unmanned aerial vehicle (UAV)-enabled radio frequency (RF) downlink system using the decode-and-forward relaying protocol. While optical links inherently provide better security, our focus is on the eavesdropping threats to the RF link. We derive novel and exact analytical and asymptotic closed-form expressions for the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (PSPSC). Our results reveal the significant impact of atmospheric turbulence, RF fading, pointing errors, and optical detection technologies on the overall secrecy performance, providing valuable insights for designing secure mixed FSO and RF downlink communication systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5427-5435"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143803","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449692
Engin Zeydan;Luis Blanco;Josep Mangues-Bafalluy;Suayb S. Arslan;Yekta Turk;Awaneesh Kumar Yadav;Madhusanka Liyanage
Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.
{"title":"Blockchain-Based Self-Sovereign Identity: Taking Control of Identity in Federated Learning","authors":"Engin Zeydan;Luis Blanco;Josep Mangues-Bafalluy;Suayb S. Arslan;Yekta Turk;Awaneesh Kumar Yadav;Madhusanka Liyanage","doi":"10.1109/OJCOMS.2024.3449692","DOIUrl":"10.1109/OJCOMS.2024.3449692","url":null,"abstract":"Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5764-5781"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209471","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 : 2024-08-26DOI: 10.1109/OJCOMS.2024.3450089
Sajjad Hussain;Syed Faraz Naeem Bacha;Adnan Ahmad Cheema;Berk Canberk;Trung Q. Duong
Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.
无人飞行器（UAVs）在现代电信和无线传感器网络中发挥着举足轻重的作用，可在各种环境中为通信和数据收集提供无与伦比的灵活性和机动性。本文对无人机辅助毫米波（mmWave）无线电网络中用于路径损耗（PL）预测的有监督机器学习（ML）模型的性能进行了全面研究。利用一组独特的可解释几何特征，使用典型城市环境中大量光线跟踪（RT）模拟生成的海量数据集，对线性回归（LR）、支持向量回归器（SVR）、K 近邻（KNN）、随机森林（RF）、极梯度提升（XGBoost）和深度神经网络（DNN）六种不同的 ML 模型进行了严格评估。结果表明，射频算法优于其他模型，在测试数据集上显示出卓越的预测性能，均方根误差 (RMSE) 为 2.38 dB。与 3GPP 和 ITU-R 模型相比，所提出的 ML 模型在毫米波无线网络方面表现出更高的准确性。这项研究深入探讨了这些模型对未知环境的适应性，并研究了用稀疏数据集训练这些模型以提高准确性的可行性。研究评估了使用 ML 模型代替大量 RT 计算稀疏训练数据集所减少的计算时间，并提出了训练此类模型的高效算法。此外，我们还分析了 ML 模型对噪声输入特征的敏感性。我们还评估了几何特征的重要性以及依次增加这些特征的数量对模型性能的影响。结果强调了所提出的几何特征的重要性，并证明了 ML 模型在不同城市环境中提供计算效率高且相对准确的 PL 预测的潜力。
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Pub Date : 2024-08-26DOI: 10.1109/ojcoms.2024.3449563
Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis
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This article addresses the joint power allocation and channel assignment (JPACA) problem in uplink non-orthogonal multiple access (NOMA) networks, an essential consideration for enhancing the performance of wireless communication systems. We introduce a novel methodology that integrates convex optimization (CO) and machine learning (ML) techniques to optimize resource allocation efficiently and effectively. Initially, we develop a CO-based algorithm that employs an alternating optimization strategy to iteratively solve for channel and power allocation, ensuring quality of service (QoS) while maximizing the system’s sum-rate. To overcome the inherent challenges of real-time application due to computational complexity, we further propose a ML-based approach that utilizes a stacking ensemble model combining convolutional neural network (CNN), feed-forward neural network (FNN), and random forest (RF). This model is trained on a dataset generated via the CO algorithm to predict optimal resource allocation in real-time scenarios. Simulation results demonstrate that our proposed methods not only reduce the computational load significantly but also maintain high system performance, closely approximating the results of more computationally intensive exhaustive search methods. The dual approach presented not only enhances computational efficiency but also aligns with the evolving demands of future wireless networks, marking a significant step towards intelligent and adaptive resource management in NOMA systems.
本文探讨了上行非正交多址（NOMA）网络中的联合功率分配和信道分配（JPACA）问题，这是提高无线通信系统性能的一个基本考虑因素。我们介绍了一种新颖的方法，它整合了凸优化（CO）和机器学习（ML）技术，能高效地优化资源分配。首先，我们开发了一种基于 CO 的算法，该算法采用交替优化策略迭代解决信道和功率分配问题，在确保服务质量（QoS）的同时最大化系统总速率。为了克服实时应用因计算复杂性而面临的固有挑战，我们进一步提出了一种基于 ML 的方法，该方法利用了结合卷积神经网络 (CNN)、前馈神经网络 (FNN) 和随机森林 (RF) 的堆叠集合模型。该模型通过 CO 算法生成的数据集进行训练，以预测实时场景中的最优资源分配。仿真结果表明，我们提出的方法不仅能显著降低计算负荷，还能保持较高的系统性能，与计算密集型穷举搜索方法的结果非常接近。所提出的双重方法不仅提高了计算效率，而且符合未来无线网络不断发展的需求，标志着向 NOMA 系统中的智能和自适应资源管理迈出了重要一步。
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Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449691
Omar Naserallah;Sherif B. Azmy;Nizar Zorba;Hossam S. Hassanein
Edge sensing (ES) systems employ users’ owned smart devices with built-in sensors to gather data from users’ surrounding environments and use their processors to carry out edge computing tasks. Therefore, ES is emerging as a potential solution for remote sensing challenges. Additionally, ES systems are recognized for their favorable characteristics, including efficient time and cost management, scalability, and the ability to gather real-time data. To improve the performance of ES systems, enormous efforts have been made to enhance the quality of data (QoD) and the systems’ spatiotemporal coverage. Moreover, the research community has focused on developing better incentive schemes, as user incentivization is essential for enhancing system performance. In this study, we assess the impact of users’ mobility and availability on the spatiotemporal coverage and QoD of ES systems, taking into account the heterogeneity of users. We propose a distribution-aware and learning-based dynamic incentive scheme. Specifically, we consider the randomness of users’ mobility and velocity using a 2-dimensional random waypoint (RWP) model and support the learning-based incentive scheme with a long short-term memory (LSTM) model. The LSTM model utilizes the users’ historical data to predict their availability to perform the sensing tasks. The learning-based incentive scheme is further used to enhance system performance and effectively manage the trade-off between quality and cost, by recruiting users based on the required quality and cost constraints, to meet the minimum quality requirement within a constrained incentivization budget.
边缘传感（ES）系统利用用户拥有的内置传感器的智能设备从用户周围环境中收集数据，并利用其处理器执行边缘计算任务。因此，ES 正在成为应对遥感挑战的潜在解决方案。此外，ES 系统还因其高效的时间和成本管理、可扩展性以及收集实时数据的能力等有利特性而备受认可。为了提高 ES 系统的性能，人们在提高数据质量（QoD）和系统的时空覆盖范围方面做出了巨大努力。此外，研究界还致力于开发更好的激励方案，因为用户激励对于提高系统性能至关重要。在本研究中，我们评估了用户的移动性和可用性对 ES 系统时空覆盖范围和 QoD 的影响，同时考虑到了用户的异质性。我们提出了一种基于分布感知和学习的动态激励方案。具体来说，我们使用二维随机航点（RWP）模型考虑了用户移动性和速度的随机性，并使用长短期记忆（LSTM）模型支持基于学习的激励方案。LSTM 模型利用用户的历史数据来预测他们是否可以执行传感任务。基于学习的激励方案进一步用于提高系统性能和有效管理质量与成本之间的权衡，根据所需的质量和成本约束条件招募用户，在受限的激励预算内满足最低质量要求。
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