. This paper investigates a dual-hop satellite-marine communication network that employs mixed radio-frequency/underwater wireless optical communication (RF/UWOC). The study focuses on investigating the impacts of non-zero pointing errors and the additive white generalized Gaussian noise (AWGGN) on the dual-hop system. To address the challenge of computing the probability density function (PDF) for the UWOC system with non-zero bore-sight error, we apply the Laplace transformation and the generalized integro exponential function. Next, we utilize the generalized Gaussian noise to calculate the signal-to-noise ratio (SNR) and the conditional bit error rate (BER). Then, we present system performance metrics such as the outage probability (OP) and BER. We also calculate the asymptotic analysis of the OP and BER by considering poles coinciding, resulting in the proposal of four asymptotic formulas to gain additional insights into the diversity gain. Finally, we provide simulation results that analyze the performance of the proposed satellite-marine network with different system parameters, such as boresight displacements and bubble levels, and validate the accuracy of the numerical results.
{"title":"Performance of Satellite UWOC Network with Generalized Boresight Error and AWGGN","authors":"Tao Teng, HE Ansu","doi":"10.13164/re.2023.0616","DOIUrl":"https://doi.org/10.13164/re.2023.0616","url":null,"abstract":". This paper investigates a dual-hop satellite-marine communication network that employs mixed radio-frequency/underwater wireless optical communication (RF/UWOC). The study focuses on investigating the impacts of non-zero pointing errors and the additive white generalized Gaussian noise (AWGGN) on the dual-hop system. To address the challenge of computing the probability density function (PDF) for the UWOC system with non-zero bore-sight error, we apply the Laplace transformation and the generalized integro exponential function. Next, we utilize the generalized Gaussian noise to calculate the signal-to-noise ratio (SNR) and the conditional bit error rate (BER). Then, we present system performance metrics such as the outage probability (OP) and BER. We also calculate the asymptotic analysis of the OP and BER by considering poles coinciding, resulting in the proposal of four asymptotic formulas to gain additional insights into the diversity gain. Finally, we provide simulation results that analyze the performance of the proposed satellite-marine network with different system parameters, such as boresight displacements and bubble levels, and validate the accuracy of the numerical results.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" 15","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Wireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA). To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).
{"title":"Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks","authors":"P. Kavitha, K. Kavitha","doi":"10.13164/re.2023.0594","DOIUrl":"https://doi.org/10.13164/re.2023.0594","url":null,"abstract":". Wireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA). To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":"28 30","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. This paper introduces a novel approach for hiding personal information in speech signals. The proposed approach applied a transform warping function, which is obtained from a long-term linear prediction spectrum individually for each speaker. The depersonalized speech was compared with the often used technique based on vocal tract length normalization. The proposed approach performs wider manipulation of fundamental frequency and provides higher intelligibility by 5% in clean speech and by 8% for signal-to-noise ratio 5 dB. It also significantly alters the derived glottal pulses, making them difficult to use for personality analysis. Speech intelligibility index and glottal pulse distortion are new aspects in the field of voice depersonalization.
{"title":"Depersonalization of Speech Using Speaker-Specific Transform Based on Long-Term Spectrum","authors":"M. Rujzl, M. Sigmund","doi":"10.13164/re.2023.0523","DOIUrl":"https://doi.org/10.13164/re.2023.0523","url":null,"abstract":". This paper introduces a novel approach for hiding personal information in speech signals. The proposed approach applied a transform warping function, which is obtained from a long-term linear prediction spectrum individually for each speaker. The depersonalized speech was compared with the often used technique based on vocal tract length normalization. The proposed approach performs wider manipulation of fundamental frequency and provides higher intelligibility by 5% in clean speech and by 8% for signal-to-noise ratio 5 dB. It also significantly alters the derived glottal pulses, making them difficult to use for personality analysis. Speech intelligibility index and glottal pulse distortion are new aspects in the field of voice depersonalization.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" 35","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. A method for compensation of frequency-selective (FS) in-phase/quadrature (IQ) imbalance of a wideband transceiver is proposed in the paper. It is dedicated for implementation in software defined radio (SDR) cellular base stations. Both transmitter (TX) and receiver (RX) IQ impairments are corrected by complex valued finite impulse response (FIR) filters which are designed based on previously found imbalance correction models. The compensation performance is assessed after the method was implemented in the SDR platform capable of transmitting signals at different central frequencies. At frequencies higher than 3 GHz measured IQ gain and phase error functions exhibit asymmetrical characteristic. In order to reduce the level of asymmetry, adopted IQ gain correction model incorporates odd polynomial elements while the phase correction model includes even polynomial parts. Regardless of utilized central frequency IQ impairments are efficiently compensated. The advantage of the proposed method is low complexity. The method doesn't require specialized hardware for calibration, instead, it uses the RF loopback. At central frequency of 3.5 GHz, transmitter image rejection ratio (IRR) is increased from 20 dBc to 45–50 dBc by applying the proposed method. After receiver imbalance is compensated, the improvement in IRR of more than 25 dBc is achieved.
{"title":"IQ Imbalance Correction in Wideband Software Defined Radio Transceivers","authors":"B. Jovanović, S. Milenković","doi":"10.13164/re.2023.0479","DOIUrl":"https://doi.org/10.13164/re.2023.0479","url":null,"abstract":". A method for compensation of frequency-selective (FS) in-phase/quadrature (IQ) imbalance of a wideband transceiver is proposed in the paper. It is dedicated for implementation in software defined radio (SDR) cellular base stations. Both transmitter (TX) and receiver (RX) IQ impairments are corrected by complex valued finite impulse response (FIR) filters which are designed based on previously found imbalance correction models. The compensation performance is assessed after the method was implemented in the SDR platform capable of transmitting signals at different central frequencies. At frequencies higher than 3 GHz measured IQ gain and phase error functions exhibit asymmetrical characteristic. In order to reduce the level of asymmetry, adopted IQ gain correction model incorporates odd polynomial elements while the phase correction model includes even polynomial parts. Regardless of utilized central frequency IQ impairments are efficiently compensated. The advantage of the proposed method is low complexity. The method doesn't require specialized hardware for calibration, instead, it uses the RF loopback. At central frequency of 3.5 GHz, transmitter image rejection ratio (IRR) is increased from 20 dBc to 45–50 dBc by applying the proposed method. After receiver imbalance is compensated, the improvement in IRR of more than 25 dBc is achieved.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" 9","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138617020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Modulation classification is a crucial technique to utilize the unconsumed spectrum in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) systems to meet the required traffic demands for future-generation cellular networks. This paper presents an end-to-end experimental setup as a generic methodology to implement various Transfer Learning (TL) models in an indoor environment. This allows us to learn the features from multiple modulation signals to train and test the model. The performance evaluation of proposed TL models such as Convolutional Neural Network - Random Forest (CNN-RF), and Convolutional Long Short Term Deep Neural Network (CLDNN) - Random Forest (CLDNN-RF) have been thoroughly discussed. The result shows that the proposed TL models yield more than 90% classification accuracy for various modulation types. A proposed framework for location-specific TL model selection based on the maximum classification accuracy has been investigated.
{"title":"Transfer Learning based Location-Aided Modulation Classification in Indoor Environments for Cognitive Radio Applications","authors":"K. Tamizhelakkiya, S. Gauni, P. Chandhar","doi":"10.13164/re.2023.0531","DOIUrl":"https://doi.org/10.13164/re.2023.0531","url":null,"abstract":". Modulation classification is a crucial technique to utilize the unconsumed spectrum in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) systems to meet the required traffic demands for future-generation cellular networks. This paper presents an end-to-end experimental setup as a generic methodology to implement various Transfer Learning (TL) models in an indoor environment. This allows us to learn the features from multiple modulation signals to train and test the model. The performance evaluation of proposed TL models such as Convolutional Neural Network - Random Forest (CNN-RF), and Convolutional Long Short Term Deep Neural Network (CLDNN) - Random Forest (CLDNN-RF) have been thoroughly discussed. The result shows that the proposed TL models yield more than 90% classification accuracy for various modulation types. A proposed framework for location-specific TL model selection based on the maximum classification accuracy has been investigated.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. In this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade. The outputs of the neural network models determine the direction of arrival of the incoming signals. Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise. Both considered neural network models were tested with noisy samples. The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm. The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm. Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.
{"title":"Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction","authors":"Milan Ilic, Z. Stanković, N. M. Ilić","doi":"10.13164/re.2023.0381","DOIUrl":"https://doi.org/10.13164/re.2023.0381","url":null,"abstract":". In this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade. The outputs of the neural network models determine the direction of arrival of the incoming signals. Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise. Both considered neural network models were tested with noisy samples. The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm. The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm. Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41490174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. In this paper, a modified vector fitting technique to extract coupling matrix from S-parameters is introduced. This work allows designers to extract the coupling matrix of different or any pre-defined topologies from the simulated or measured S-parameter data. A study on vector fitting (VF) equations that can extract the rational polynomial of bandpass filter responses is carried out. VF is a robust numerical method which is applied widely in rational approximations due to its fast convergence and able to apply for high order polynomials. The rational polynomials are formed by applying the VF process to S-parameter responses without having to remove the phase offset and de-embedding the transmission lines. Focus fitting as the first enhancement can avoid VF overfitting spurious as poles; Poles forcing as the second enhancement is able to ensure the poles of all S-parameters are the same. Finally, the desired coupling matrix configuration is generated directly from the extracted polynomials using unconstrained and finitely bounded non-linear polynomials (NLP) optimization. Without the need for matrix transformation, the matrix elements are still able to show a one-to-one relationship in coupling values of resonators. Two bandpass filters are shown as examples to illustrate the performance of the new variation of VF.
{"title":"A Modified Vector Fitting Technique to Extract Coupling Matrix from S-parameters","authors":"C. Ng, S. Soeung, S. Cheab, K. Y. Leong","doi":"10.13164/re.2023.0325","DOIUrl":"https://doi.org/10.13164/re.2023.0325","url":null,"abstract":". In this paper, a modified vector fitting technique to extract coupling matrix from S-parameters is introduced. This work allows designers to extract the coupling matrix of different or any pre-defined topologies from the simulated or measured S-parameter data. A study on vector fitting (VF) equations that can extract the rational polynomial of bandpass filter responses is carried out. VF is a robust numerical method which is applied widely in rational approximations due to its fast convergence and able to apply for high order polynomials. The rational polynomials are formed by applying the VF process to S-parameter responses without having to remove the phase offset and de-embedding the transmission lines. Focus fitting as the first enhancement can avoid VF overfitting spurious as poles; Poles forcing as the second enhancement is able to ensure the poles of all S-parameters are the same. Finally, the desired coupling matrix configuration is generated directly from the extracted polynomials using unconstrained and finitely bounded non-linear polynomials (NLP) optimization. Without the need for matrix transformation, the matrix elements are still able to show a one-to-one relationship in coupling values of resonators. Two bandpass filters are shown as examples to illustrate the performance of the new variation of VF.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45332975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. In this paper, a novel index modulation-based non-orthogonal multiple access (IM-NOMA) system is proposed and investigated for both perfect and imperfect channel state information (CSI) uncertainty over Nakagami-m fading channel. The proposed system has added advantages of NOMA and IM systems. NOMA supports more users by allowing all users to utilize the same resources simultaneously whereas IM boosts spectral efficiency by conveying information to the users through both constellation domain and index domain symbols. Maximum likelihood (ML) and successive interference cancellation (SIC) detectors are used at the receiver side to detect index and data symbols. The proposed system is analyzed for different values of Nakagami-m channel parameters as well as for three different CSI conditions - perfect, fixed, and MMSE-based variable CSI uncertainty. The simulation results for the bit error rate and spectral efficiency parameters show that the proposed system outperforms the existing NOMA and OMA schemes.
{"title":"Performance Analysis of Novel Index Modulation-Based Non-Orthogonal Multiple Access Systems over Nakagami-m Fading Channels with Imperfect CSI","authors":"H. Shwetha, S. Anuradha","doi":"10.13164/re.2023.0425","DOIUrl":"https://doi.org/10.13164/re.2023.0425","url":null,"abstract":". In this paper, a novel index modulation-based non-orthogonal multiple access (IM-NOMA) system is proposed and investigated for both perfect and imperfect channel state information (CSI) uncertainty over Nakagami-m fading channel. The proposed system has added advantages of NOMA and IM systems. NOMA supports more users by allowing all users to utilize the same resources simultaneously whereas IM boosts spectral efficiency by conveying information to the users through both constellation domain and index domain symbols. Maximum likelihood (ML) and successive interference cancellation (SIC) detectors are used at the receiver side to detect index and data symbols. The proposed system is analyzed for different values of Nakagami-m channel parameters as well as for three different CSI conditions - perfect, fixed, and MMSE-based variable CSI uncertainty. The simulation results for the bit error rate and spectral efficiency parameters show that the proposed system outperforms the existing NOMA and OMA schemes.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45926039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Class imbalance is a prevalent problem in many real-world applications, and imbalanced data distribution can dramatically skew the performance of classifiers. In general, the higher the imbalance ratio of a dataset, the more difficult it is to classify. However, it is found that standard classifiers can still achieve good classification results on some highly imbalanced datasets. Obviously, the class imbalance is only a superficial characteristic of the data, and the underlying structural information is often the key factor affecting the classification performance. As implicit prior knowledge, structural information has been validated to be crucial for designing a good classifier. This paper proposes a Wasserstein-based cost-sensitive support vector machine (CS-WSVM) for class imbalance learning, incorporating prior structural information and a cost-sensitive strategy. The Wasserstein distance is introduced to model the distribution of majority and minority samples to capture the structural information, which is employed to weight the majority and minority samples. Comprehensive experiments on synthetic and real-world datasets, especially on the radar emitter signal dataset, demonstrated that CS-WSVM can achieve outstanding performance in imbalanced scenarios.
{"title":"A Wasserstein Distance-Based Cost-Sensitive Framework for Imbalanced Data Classification","authors":"R. Feng, H. Ji, Z. Zhu, L. Wang","doi":"10.13164/re.2023.0451","DOIUrl":"https://doi.org/10.13164/re.2023.0451","url":null,"abstract":". Class imbalance is a prevalent problem in many real-world applications, and imbalanced data distribution can dramatically skew the performance of classifiers. In general, the higher the imbalance ratio of a dataset, the more difficult it is to classify. However, it is found that standard classifiers can still achieve good classification results on some highly imbalanced datasets. Obviously, the class imbalance is only a superficial characteristic of the data, and the underlying structural information is often the key factor affecting the classification performance. As implicit prior knowledge, structural information has been validated to be crucial for designing a good classifier. This paper proposes a Wasserstein-based cost-sensitive support vector machine (CS-WSVM) for class imbalance learning, incorporating prior structural information and a cost-sensitive strategy. The Wasserstein distance is introduced to model the distribution of majority and minority samples to capture the structural information, which is employed to weight the majority and minority samples. Comprehensive experiments on synthetic and real-world datasets, especially on the radar emitter signal dataset, demonstrated that CS-WSVM can achieve outstanding performance in imbalanced scenarios.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42780388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. There are nonlinear drift memristor models uti-lizing window functions in the literature. The resistive memories can also be modeled using memristors. If the memristor’s resistance switches from its minimum value to its maximum value or from its maximum value to its minimum value, the transition phenomenon is called resistive or memristive switching. The value of the time required for this transition is especially important for resistive computer memory applications. The switching time is measured by experiments and should be calculatable from the parameters of the memristor model used. In the literature, to the best of our knowledge, the resistive switching times have not been calculated except for the HP memristor model and a piecewise linear memristor model. In this study, the memristive switching times of some of the well-known memristor models using a window function are calculated and found to be infinite. This is not feasible according to the experiments in which a finite memristive switching time is reported. Inspired by these results, a new memristor window function that results in a finite switching time is proposed. The results of this study and the criteria given here can be used to make more realistic memristor models in the future.
{"title":"A Zeno Paradox: Some Well-known Nonlinear Dopant Drift Memristor Models Have Infinite Resistive Switching Time","authors":"R. Mutlu, T. D. Kumru","doi":"10.13164/re.2023.0312","DOIUrl":"https://doi.org/10.13164/re.2023.0312","url":null,"abstract":". There are nonlinear drift memristor models uti-lizing window functions in the literature. The resistive memories can also be modeled using memristors. If the memristor’s resistance switches from its minimum value to its maximum value or from its maximum value to its minimum value, the transition phenomenon is called resistive or memristive switching. The value of the time required for this transition is especially important for resistive computer memory applications. The switching time is measured by experiments and should be calculatable from the parameters of the memristor model used. In the literature, to the best of our knowledge, the resistive switching times have not been calculated except for the HP memristor model and a piecewise linear memristor model. In this study, the memristive switching times of some of the well-known memristor models using a window function are calculated and found to be infinite. This is not feasible according to the experiments in which a finite memristive switching time is reported. Inspired by these results, a new memristor window function that results in a finite switching time is proposed. The results of this study and the criteria given here can be used to make more realistic memristor models in the future.","PeriodicalId":54514,"journal":{"name":"Radioengineering","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42280842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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