Mohamed Bouni, Badr Hssina, K. Douzi, Samira Douzi
The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for this method. Digital image processing can be used to monitor plant disease. Deep learning has made remarkable improvements in digital image processing in recent years, surpassing the older techniques. This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. The CNN’s backbone comprises AlexNet, ResNet, VGG-16, and DenseNet. The Adam and RmsProp optimization methods examine these networks’ relative performance, demonstrating that the DenseNet model with the RmsProp optimization approach achieves the most significant outcomes with the best accuracy of 99.9%.
{"title":"Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction","authors":"Mohamed Bouni, Badr Hssina, K. Douzi, Samira Douzi","doi":"10.1155/2023/5051005","DOIUrl":"https://doi.org/10.1155/2023/5051005","url":null,"abstract":"The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for this method. Digital image processing can be used to monitor plant disease. Deep learning has made remarkable improvements in digital image processing in recent years, surpassing the older techniques. This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. The CNN’s backbone comprises AlexNet, ResNet, VGG-16, and DenseNet. The Adam and RmsProp optimization methods examine these networks’ relative performance, demonstrating that the DenseNet model with the RmsProp optimization approach achieves the most significant outcomes with the best accuracy of 99.9%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"696 1","pages":"5051005:1-5051005:11"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86929729","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}
Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.
{"title":"Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT","authors":"Shibo Yang, W. Fu, Lishuo Zhang, Zhaolei Wang","doi":"10.1155/2023/6859102","DOIUrl":"https://doi.org/10.1155/2023/6859102","url":null,"abstract":"Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"19 1","pages":"6859102:1-6859102:8"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037246","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}
Ginbar Ensermu, M. Vijayashanthi, Suresh Merugu, A. Shaik, B. Premalatha, G. Devadasu
The development of a network termed microgrid (MG) has been motivated owing to augmentation in renewable energy source (RES) infiltration along with the utilization of enhanced power electronic technologies. Recently, more popularity has been gained by the hybrid MG (HMG). Maintaining the power system’s (PS) small-signal stability (SSS) is highly complicated during the energy enhancement of RES. The enhancement of the SSS has been focused on by numerous existing methodologies; however, the optimal solution was not obtained by those methodologies. A new controller with the assistance of bell-curved squirrel search optimization (BCSSO) is proposed to address the aforementioned issue. Initially, for PSs such as photovoltaic (PV), wind turbines, along with fuel cells, a mathematical model is ascertained. Then, in this, the converter design has been developed. The PV’s maximum power flow is recognized by maximum power point tracking (MPPT) in the bidirectional switched buck-boost converter (BSBBC), which is utilized in this research, and by utilizing the fuzzy ruled linear quadratic Gaussian (FRLQG), the converters are controlled to assure safe operation along with soft dynamics. By employing the BCSSO, the parameters are modified in this controller which in turn ameliorates the SSS. The experiential evaluation of the proposed system’s performance is analogized with the existing methodologies. Consequently, the outcomes confirmed that a better performance was attained by the proposed methodology than the prevailing works.
{"title":"An FRLQG Controller-Based Small-Signal Stability Enhancement of Hybrid Microgrid Using the BCSSO Algorithm","authors":"Ginbar Ensermu, M. Vijayashanthi, Suresh Merugu, A. Shaik, B. Premalatha, G. Devadasu","doi":"10.1155/2023/8404457","DOIUrl":"https://doi.org/10.1155/2023/8404457","url":null,"abstract":"The development of a network termed microgrid (MG) has been motivated owing to augmentation in renewable energy source (RES) infiltration along with the utilization of enhanced power electronic technologies. Recently, more popularity has been gained by the hybrid MG (HMG). Maintaining the power system’s (PS) small-signal stability (SSS) is highly complicated during the energy enhancement of RES. The enhancement of the SSS has been focused on by numerous existing methodologies; however, the optimal solution was not obtained by those methodologies. A new controller with the assistance of bell-curved squirrel search optimization (BCSSO) is proposed to address the aforementioned issue. Initially, for PSs such as photovoltaic (PV), wind turbines, along with fuel cells, a mathematical model is ascertained. Then, in this, the converter design has been developed. The PV’s maximum power flow is recognized by maximum power point tracking (MPPT) in the bidirectional switched buck-boost converter (BSBBC), which is utilized in this research, and by utilizing the fuzzy ruled linear quadratic Gaussian (FRLQG), the converters are controlled to assure safe operation along with soft dynamics. By employing the BCSSO, the parameters are modified in this controller which in turn ameliorates the SSS. The experiential evaluation of the proposed system’s performance is analogized with the existing methodologies. Consequently, the outcomes confirmed that a better performance was attained by the proposed methodology than the prevailing works.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"59 1","pages":"8404457:1-8404457:15"},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85737672","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":"Retracted: The Path of Digital Government and University Asset Intelligence Value-Added Service Driven by Block Chain Technology","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9803514","DOIUrl":"https://doi.org/10.1155/2023/9803514","url":null,"abstract":"<jats:p />","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"31 4","pages":"9803514:1-9803514:1"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72620841","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}
Muhammed Nura Yusuf, Kamalrulnizam bin Abu Bakar, Babangida Isyaku, Fadhil Mukhlif
Software-defined networking (SDN) brings an innovative approach to networking by adopting a flow-centric model and removing networking decisions from the data plane to provide them centrally from the control plane. A single centralized controller is used in a traditional SDN design. However, the complexity of modern networks, due to their size and requirements’ coarseness, has made using a single controller a source of performance bottlenecks. Similarly, the solution found by using multiple controllers in distributed control planes brings forth the profound issue of interoperability, consistency, and the “controller placement problem” (CPP). It is an NP-hard problem that deals with positioning controllers at optimum locations within the network and mapping with resources at the data plane to meet quality of service (QoS) requirements. Over the years, the problem has received significant attention from the research community, and many solutions have been considered. This paper offers an in-depth review of the proposals by providing an updated evolution of the problem concerning the application environment, design objectives, and cost and controller type. Based on our findings, new research ideas were identified and discussed.
{"title":"Distributed Controller Placement in Software-Defined Networks with Consistency and Interoperability Problems","authors":"Muhammed Nura Yusuf, Kamalrulnizam bin Abu Bakar, Babangida Isyaku, Fadhil Mukhlif","doi":"10.1155/2023/6466996","DOIUrl":"https://doi.org/10.1155/2023/6466996","url":null,"abstract":"Software-defined networking (SDN) brings an innovative approach to networking by adopting a flow-centric model and removing networking decisions from the data plane to provide them centrally from the control plane. A single centralized controller is used in a traditional SDN design. However, the complexity of modern networks, due to their size and requirements’ coarseness, has made using a single controller a source of performance bottlenecks. Similarly, the solution found by using multiple controllers in distributed control planes brings forth the profound issue of interoperability, consistency, and the “controller placement problem” (CPP). It is an NP-hard problem that deals with positioning controllers at optimum locations within the network and mapping with resources at the data plane to meet quality of service (QoS) requirements. Over the years, the problem has received significant attention from the research community, and many solutions have been considered. This paper offers an in-depth review of the proposals by providing an updated evolution of the problem concerning the application environment, design objectives, and cost and controller type. Based on our findings, new research ideas were identified and discussed.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"51 1","pages":"6466996:1-6466996:33"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73409477","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}
Samsuryadi, Rudi Kurniawan, Julian Supardi, Sukemi, F. Mohamad
Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.
{"title":"A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology","authors":"Samsuryadi, Rudi Kurniawan, Julian Supardi, Sukemi, F. Mohamad","doi":"10.1155/2023/1249004","DOIUrl":"https://doi.org/10.1155/2023/1249004","url":null,"abstract":"Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"12 1","pages":"1249004:1-1249004:15"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90343932","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":"Retracted: Defect Point Location Method of Civil Bridge Based on Internet of Things Wireless Communication","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9759313","DOIUrl":"https://doi.org/10.1155/2023/9759313","url":null,"abstract":"<jats:p />","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"108 1","pages":"9759313:1-9759313:1"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75019739","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}
Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.
{"title":"Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning","authors":"Zhengxiong Mao, Hui Li, Zuyuan Huang, Yuan Tian, Peng Zhao, Yanan Li","doi":"10.1155/2023/9914169","DOIUrl":"https://doi.org/10.1155/2023/9914169","url":null,"abstract":"Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"26 1","pages":"9914169:1-9914169:9"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78097810","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}
In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.
{"title":"Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm","authors":"T. Zhang","doi":"10.1155/2023/3748089","DOIUrl":"https://doi.org/10.1155/2023/3748089","url":null,"abstract":"In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at \u0000 \u0000 c\u0000 \u0000 = 0.3 \u0000 \u0000 \u0000 \u0000 ∗\u0000 \u0000 \u0000 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter \u0000 \u0000 α\u0000 =\u0000 \u0000 \u0000 α\u0000 \u0000 ∗\u0000 \u0000 \u0000 , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"1 1","pages":"3748089:1-3748089:11"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83058632","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}
Hise Teferi Dumari, Gelmecha Demissie Jobir, R. Shakya, R. Singh
Nowadays, multicarrier modulation schemes are being widely used in wireless communication system than single-carrier modulation techniques. Single-carrier modulation schemes are less capable of dealing with multipath fading channels than multicarrier modulation schemes, which results in lower spectral efficiency. Multicarrier modulation schemes have the ability to overcome multipath fading channels. Multicarrier modulation technique currently used in 4G technology in many countries is OFDM and it is easy for implementation, immune to interference, and provide fast data rate. However, the rising users demand on wireless communication resulted in need for further advancement of wireless communication system. The present OFDM transmission does not fulfill the requirements of 5G wireless communication system and beyond due to major limitations such as out of band emission and usage of cyclic prefix. To overcome the challenges of OFDM, different modulation schemes like Filter Bank Multicarrier with Offset-QAM, Filter Bank Multicarrier with QAM, Universal Filter Multicarrier, Filtered-OFDM, and Weighted Overlap and Added-OFDM are proposed. In this study, the Filter Bank Multicarrier with QAM using Hermite prototype filter is proposed to overcome drawbacks of OFDM and all other proposed waveforms. The performances of each multicarrier technique are analyzed based on power spectral density and bit error rate. Simulation result shows that the power spectral density of FBMC with QAM using Hermite filter resulted in 4.7 dB reduction of out of band emission compared to FBMC with QAM using PHYDYAS filter. The bit error rate is also reduced for Vehicular A, Vehicular B, Pedestrian A, and Pedestrian B channel models.
{"title":"BER and PSD Improvement of FBMC with Higher Order QAM Using Hermite Filter for 5G Wireless Communication and beyond","authors":"Hise Teferi Dumari, Gelmecha Demissie Jobir, R. Shakya, R. Singh","doi":"10.1155/2023/7232488","DOIUrl":"https://doi.org/10.1155/2023/7232488","url":null,"abstract":"Nowadays, multicarrier modulation schemes are being widely used in wireless communication system than single-carrier modulation techniques. Single-carrier modulation schemes are less capable of dealing with multipath fading channels than multicarrier modulation schemes, which results in lower spectral efficiency. Multicarrier modulation schemes have the ability to overcome multipath fading channels. Multicarrier modulation technique currently used in 4G technology in many countries is OFDM and it is easy for implementation, immune to interference, and provide fast data rate. However, the rising users demand on wireless communication resulted in need for further advancement of wireless communication system. The present OFDM transmission does not fulfill the requirements of 5G wireless communication system and beyond due to major limitations such as out of band emission and usage of cyclic prefix. To overcome the challenges of OFDM, different modulation schemes like Filter Bank Multicarrier with Offset-QAM, Filter Bank Multicarrier with QAM, Universal Filter Multicarrier, Filtered-OFDM, and Weighted Overlap and Added-OFDM are proposed. In this study, the Filter Bank Multicarrier with QAM using Hermite prototype filter is proposed to overcome drawbacks of OFDM and all other proposed waveforms. The performances of each multicarrier technique are analyzed based on power spectral density and bit error rate. Simulation result shows that the power spectral density of FBMC with QAM using Hermite filter resulted in 4.7 dB reduction of out of band emission compared to FBMC with QAM using PHYDYAS filter. The bit error rate is also reduced for Vehicular A, Vehicular B, Pedestrian A, and Pedestrian B channel models.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"39 1","pages":"7232488:1-7232488:16"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76300369","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}
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