IEEE Open Journal of the Communications Society最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Geometrical Features Based-mmWave UAV Path Loss Prediction Using Machine Learning for 5G and Beyond","authors":"Sajjad Hussain;Syed Faraz Naeem Bacha;Adnan Ahmad Cheema;Berk Canberk;Trung Q. Duong","doi":"10.1109/OJCOMS.2024.3450089","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450089","url":null,"abstract":"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5667-5679"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173898","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}

{"title":"Federated Learning For Enhanced Cybersecurity And Trustworthiness In 5G and 6G Networks: A Comprehensive Survey","authors":"Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis","doi":"10.1109/ojcoms.2024.3449563","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3449563","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"20 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209473","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}

{"title":"Resource Allocation in NOMA Networks: Convex Optimization and Stacking Ensemble Machine Learning","authors":"Vali Ghanbarzadeh;Mohammadreza Zahabi;Hamid Amiriara;Farahnaz Jafari;Georges Kaddoum","doi":"10.1109/OJCOMS.2024.3450207","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450207","url":null,"abstract":"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5276-5288"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143683","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}

{"title":"Novel Distribution-Aware and Learning-Based Dynamic Scheme for Efficient User Incentivization in Edge Sensing Systems","authors":"Omar Naserallah;Sherif B. Azmy;Nizar Zorba;Hossam S. Hassanein","doi":"10.1109/OJCOMS.2024.3449691","DOIUrl":"10.1109/OJCOMS.2024.3449691","url":null,"abstract":"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5735-5744"},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209472","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}