Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528722
Hyeonsu Kim, Sangmi Moon, I. Hwang
The use of millimeter-wave (mmWave) frequencies is a promising technology for meeting the ever-growing data traffic in next-generation wireless communications. A major challenge of mmWave communications is the high path loss. To overcome this issue, mmWave systems adopt beamforming techniques, which require robust channel estimation and tracking algorithms to maintain an adequate quality of service. In this study, we propose the machine learning-based channel tracking algorithm for vehicular mmWave communications. In this paper, we propose a long short-term memory (LSTM)-based channel tracking algorithm for vehicle-to-infrastructure mmWave communications. The bidirectional LSTM is leveraged to track the channel. Simulation results demonstrate that the proposed algorithm efficiently tracks the mmWave channel with negligible training overhead.
{"title":"Machine Learning-based Channel Tracking for Next-Generation 5G Communication System","authors":"Hyeonsu Kim, Sangmi Moon, I. Hwang","doi":"10.1109/ICUFN49451.2021.9528722","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528722","url":null,"abstract":"The use of millimeter-wave (mmWave) frequencies is a promising technology for meeting the ever-growing data traffic in next-generation wireless communications. A major challenge of mmWave communications is the high path loss. To overcome this issue, mmWave systems adopt beamforming techniques, which require robust channel estimation and tracking algorithms to maintain an adequate quality of service. In this study, we propose the machine learning-based channel tracking algorithm for vehicular mmWave communications. In this paper, we propose a long short-term memory (LSTM)-based channel tracking algorithm for vehicle-to-infrastructure mmWave communications. The bidirectional LSTM is leveraged to track the channel. Simulation results demonstrate that the proposed algorithm efficiently tracks the mmWave channel with negligible training overhead.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116601302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528575
Hyungbin Kim, Yongho Kim, Hyunhee Park
Federated Learning (FL) has a different learning framework from existing machine learning, which had to centralize training data. Federated learning has the advantage of protecting privacy because learning is performed on each client device rather than the central server, and only the weight parameter values, which are the learning results, are sent to the central server. However, the performance of federated learning shows relatively low performance compared to cloud computing, and in reality, it is difficult to build a federated learning environment due to the high communication cost between the server and multiple clients. In this paper, we propose Federated Learning with Clustering algorithms (FLC). The proposed FLC is a method of clustering clients with similar characteristics by analyzing the weights of each layer of a machine learning model, and performing federated learning among the clustered clients. The proposed FLC can reduce the communication cost for each model by reducing the number of clients corresponding to each model. As a result of extensive simulation, it is confirmed that the accuracy is improved by 2.4% and the loss by 47% through the proposed FLC compared to the standard federated learning.
{"title":"Reducing Model Cost Based on the Weights of Each Layer for Federated Learning Clustering","authors":"Hyungbin Kim, Yongho Kim, Hyunhee Park","doi":"10.1109/ICUFN49451.2021.9528575","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528575","url":null,"abstract":"Federated Learning (FL) has a different learning framework from existing machine learning, which had to centralize training data. Federated learning has the advantage of protecting privacy because learning is performed on each client device rather than the central server, and only the weight parameter values, which are the learning results, are sent to the central server. However, the performance of federated learning shows relatively low performance compared to cloud computing, and in reality, it is difficult to build a federated learning environment due to the high communication cost between the server and multiple clients. In this paper, we propose Federated Learning with Clustering algorithms (FLC). The proposed FLC is a method of clustering clients with similar characteristics by analyzing the weights of each layer of a machine learning model, and performing federated learning among the clustered clients. The proposed FLC can reduce the communication cost for each model by reducing the number of clients corresponding to each model. As a result of extensive simulation, it is confirmed that the accuracy is improved by 2.4% and the loss by 47% through the proposed FLC compared to the standard federated learning.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114899027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528819
Hyun-Woo Jeong, Ji-Eun Shin, Ji-Won Jung
This paper presented an efficient multiband FSK signals with direct sequence spread spectrum for maintaining covertness and performance. In aspect to covertness, direct sequence spread spectrum method, which multiplying by PN codes whose rate is much higher than that of data sequence, is employed. In aspect to performance, we applied multiband, turbo equalization, and weighting algorithm. Underwater acoustic communication experiments were conducted in the lake. In the lake experimental results, we confirmed that the performance was improved as the number of bands and chips are increased. Furthermore, the performance of multiband was improved when the proposed weighting algorithm was applied.
{"title":"Multiband FSK with Direct Sequence Spread Spectrum for Underwater Acoustic Communications","authors":"Hyun-Woo Jeong, Ji-Eun Shin, Ji-Won Jung","doi":"10.1109/ICUFN49451.2021.9528819","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528819","url":null,"abstract":"This paper presented an efficient multiband FSK signals with direct sequence spread spectrum for maintaining covertness and performance. In aspect to covertness, direct sequence spread spectrum method, which multiplying by PN codes whose rate is much higher than that of data sequence, is employed. In aspect to performance, we applied multiband, turbo equalization, and weighting algorithm. Underwater acoustic communication experiments were conducted in the lake. In the lake experimental results, we confirmed that the performance was improved as the number of bands and chips are increased. Furthermore, the performance of multiband was improved when the proposed weighting algorithm was applied.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128609332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528529
Soonhyun Kwon, Jaehak Yu, Se Jin Park, Jong-Arm Jun, C. Pyo
In this paper, we propose a stroke medical ontology that provides medical knowledge to accompany AI-based stroke disease prediction system's results that were arrived at based on EMG information. This system was developed as a result of the limitations mentioned above being encountered in previous studies. We approached the problem from a viewpoint of knowledge engineering with the aim of modeling medical knowledge related to strokes. Using web ontology language (OWL), a standard ontology language, we developed schema-level stroke ontologies with concepts and properties based on the brain's anatomical structures, lesions, and disease related to strokes. Also, we developed an instance-level medical terms ontology that can span standard medical terms such as those in the international classification diseases (ICD), systematized nomenclature of medicine - clinical terms (SNOMED-CT), and foundational model of anatomy (FMA). The above schema ontology and instance ontology are meaningfully mapped to each other to apply layered ontology modeling techniques that separate schemas from instances. Through semantic web rule language (SWRL)-based inference, we predict lesions, diseases, and anatomical brain structural ripple effects based on the patient's current lesions and diseases. The inferred knowledge information is provided via the SPARQL protocol and RDF query language (SPARQL), a standard ontology query language. To verify the stroke medical ontology proposed in this paper, we developed an ontology-based stroke disease prediction system. This system achieved knowledge augmentation performance of 67.82% by comparing the patients' current lesions and diseases with the lesions, diseases, and areas of disability found by SWRL-based inference using actual stroke emergency data from 37 patients.
{"title":"Stroke Medical Ontology for Supporting AI-based Stroke Prediction System using Bio-Signals","authors":"Soonhyun Kwon, Jaehak Yu, Se Jin Park, Jong-Arm Jun, C. Pyo","doi":"10.1109/ICUFN49451.2021.9528529","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528529","url":null,"abstract":"In this paper, we propose a stroke medical ontology that provides medical knowledge to accompany AI-based stroke disease prediction system's results that were arrived at based on EMG information. This system was developed as a result of the limitations mentioned above being encountered in previous studies. We approached the problem from a viewpoint of knowledge engineering with the aim of modeling medical knowledge related to strokes. Using web ontology language (OWL), a standard ontology language, we developed schema-level stroke ontologies with concepts and properties based on the brain's anatomical structures, lesions, and disease related to strokes. Also, we developed an instance-level medical terms ontology that can span standard medical terms such as those in the international classification diseases (ICD), systematized nomenclature of medicine - clinical terms (SNOMED-CT), and foundational model of anatomy (FMA). The above schema ontology and instance ontology are meaningfully mapped to each other to apply layered ontology modeling techniques that separate schemas from instances. Through semantic web rule language (SWRL)-based inference, we predict lesions, diseases, and anatomical brain structural ripple effects based on the patient's current lesions and diseases. The inferred knowledge information is provided via the SPARQL protocol and RDF query language (SPARQL), a standard ontology query language. To verify the stroke medical ontology proposed in this paper, we developed an ontology-based stroke disease prediction system. This system achieved knowledge augmentation performance of 67.82% by comparing the patients' current lesions and diseases with the lesions, diseases, and areas of disability found by SWRL-based inference using actual stroke emergency data from 37 patients.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130826887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528740
Keita Katagiri, T. Fujii
We have proposed a method of the radio map construction using clustering algorithm in our conventional work. The method enables us to accurately predict the radio environment while reducing the registered data size. However, this clustering algorithm has been only applied to the wireless system with fixed transmitter location. Thus, this paper considers the radio maps construction based on the clustering for the autonomous distributed networks that both transmitter and receiver dynamically move. The proposed method classifies the similar average received signal power samples using k-means++. The emulation results clarify that the proposed method can estimate the radio environment with high accuracy while reducing the registered data size compared to the conventional radio map.
{"title":"Mesh-Clustering-Based Radio Maps Construction for Autonomous Distributed Networks","authors":"Keita Katagiri, T. Fujii","doi":"10.1109/ICUFN49451.2021.9528740","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528740","url":null,"abstract":"We have proposed a method of the radio map construction using clustering algorithm in our conventional work. The method enables us to accurately predict the radio environment while reducing the registered data size. However, this clustering algorithm has been only applied to the wireless system with fixed transmitter location. Thus, this paper considers the radio maps construction based on the clustering for the autonomous distributed networks that both transmitter and receiver dynamically move. The proposed method classifies the similar average received signal power samples using k-means++. The emulation results clarify that the proposed method can estimate the radio environment with high accuracy while reducing the registered data size compared to the conventional radio map.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"6 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124605417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528659
M. Shahjalal, Md. Habibur Rahman, Md. Osman Ali, ByungDeok Chung, Y. Jang
Recently, Cooperative massive multiple-input multiple-output and non-orthogonal multiple access (mMIMO-NOMA) has been considered as a promising solution that can significantly improve the system capacity and the spectral efficiency of the sixth-generation (6G) high frequency spectrum such as Millimeter Wave and Terahertz networks. In this paper, we consider a mMIMO-NOMA enabled base station that can support a number of single antenna users in different clusters. Cooperative use of NOMA can support the users in a cluster by sharing the same frequency and time resources. However, in 6G the networks will be congested with ultra-massive interconnected users and that arises challenges in clustering the users efficiently. Therefore. we briefly summarize the studies about user clustering solutions in mMIMO-NOMA systems and divided them into two categories; resource aware user clustering (RAUC) and learning assisted user clustering (LAUC) approaches. A comparison among those techniques has been tabulated considering the computational complexities. The result depicts that the RAUC demonstrates a polynomial complexity function while that for the LAUC is comparatively low.
{"title":"User Clustering Techniques for Massive MIMO-NOMA Enabled mmWave/THz Communications in 6G","authors":"M. Shahjalal, Md. Habibur Rahman, Md. Osman Ali, ByungDeok Chung, Y. Jang","doi":"10.1109/ICUFN49451.2021.9528659","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528659","url":null,"abstract":"Recently, Cooperative massive multiple-input multiple-output and non-orthogonal multiple access (mMIMO-NOMA) has been considered as a promising solution that can significantly improve the system capacity and the spectral efficiency of the sixth-generation (6G) high frequency spectrum such as Millimeter Wave and Terahertz networks. In this paper, we consider a mMIMO-NOMA enabled base station that can support a number of single antenna users in different clusters. Cooperative use of NOMA can support the users in a cluster by sharing the same frequency and time resources. However, in 6G the networks will be congested with ultra-massive interconnected users and that arises challenges in clustering the users efficiently. Therefore. we briefly summarize the studies about user clustering solutions in mMIMO-NOMA systems and divided them into two categories; resource aware user clustering (RAUC) and learning assisted user clustering (LAUC) approaches. A comparison among those techniques has been tabulated considering the computational complexities. The result depicts that the RAUC demonstrates a polynomial complexity function while that for the LAUC is comparatively low.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126408758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528547
Nguyen Thi Hoai Thu, Dong Seog Han
Freezing of gait (FoG) detection using wearable sensors plays an important role in both online and offline monitoring of Parkinson's disease patients. In a FoG detector, feature extraction is commonly considered as a critical part for distilling the sensor signals before the FoG classification. Manually extracted features with domain knowledge are widely used in conventional machine learning methods while recent deep learning algorithms introduce the automatic feature learning approach. In this paper, we propose a FoG detection framework, in which hand-crafted features are used as input to a hybrid deep learning model for further feature learning and classification task. The hand-crafted features with time-frequency representation are extracted from the raw sensor signal by using a multi-level discrete wavelet transform (DWT). A hybrid deep learning architecture constructed from two algorithms: convolutional neural network (CNN) and bidirectional long short-term memory network is then deployed to extract deep features and classify FoG events. For performance comparison purposes, experiments on different input data types and machine learning methods are carried out on the Daphnet public dataset.
{"title":"Freezing of Gait Detection Using Discrete Wavelet Transform and Hybrid Deep Learning Architecture","authors":"Nguyen Thi Hoai Thu, Dong Seog Han","doi":"10.1109/ICUFN49451.2021.9528547","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528547","url":null,"abstract":"Freezing of gait (FoG) detection using wearable sensors plays an important role in both online and offline monitoring of Parkinson's disease patients. In a FoG detector, feature extraction is commonly considered as a critical part for distilling the sensor signals before the FoG classification. Manually extracted features with domain knowledge are widely used in conventional machine learning methods while recent deep learning algorithms introduce the automatic feature learning approach. In this paper, we propose a FoG detection framework, in which hand-crafted features are used as input to a hybrid deep learning model for further feature learning and classification task. The hand-crafted features with time-frequency representation are extracted from the raw sensor signal by using a multi-level discrete wavelet transform (DWT). A hybrid deep learning architecture constructed from two algorithms: convolutional neural network (CNN) and bidirectional long short-term memory network is then deployed to extract deep features and classify FoG events. For performance comparison purposes, experiments on different input data types and machine learning methods are carried out on the Daphnet public dataset.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132057329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528538
Farhan Siddiqui, Quan Chau
The Millimeter Wave technology can provide very high data rates and is a key enabler of 5G communication. However, mmWave signals suffer with high penetration loss and poor isotropic propagation which causes intermittent packet losses. TCP's congestion control algorithms consider packet loss as an implicit notification of network congestion and react by reducing the data transmission rate. In this research we examine how TCP's congestion control algorithms impact the achievable data rate over mmWave links. We discuss the performance of different TCP versions using metrics such as congestion window size (cwnd), throughput, Round Trip Time (RTT), and Signal-to-Interference-plus-Noise Ratio (SINR).
{"title":"Analysis of Transport Layer Congestion Control Algorithms over 5G Millimeter Wave Networks","authors":"Farhan Siddiqui, Quan Chau","doi":"10.1109/ICUFN49451.2021.9528538","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528538","url":null,"abstract":"The Millimeter Wave technology can provide very high data rates and is a key enabler of 5G communication. However, mmWave signals suffer with high penetration loss and poor isotropic propagation which causes intermittent packet losses. TCP's congestion control algorithms consider packet loss as an implicit notification of network congestion and react by reducing the data transmission rate. In this research we examine how TCP's congestion control algorithms impact the achievable data rate over mmWave links. We discuss the performance of different TCP versions using metrics such as congestion window size (cwnd), throughput, Round Trip Time (RTT), and Signal-to-Interference-plus-Noise Ratio (SINR).","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133852528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528697
S. Kameda, Yusaku Honma, N. Suematsu, S. Yasuda, N. Shiga
Synchronized spread spectrum code division multiple access (SS-CDMA) is very effective for increasing the capacity and reducing the interference with a rapid spread of Internet of things (IoT) devices. Since the synchronized SS-CDMA requires to receive timing synchronization, it is essential to realize transmission timing control of each node using spacetime synchronization. In this paper, we investigate precise time synchronization between nodes using Wireless Two-Way Interferometry (Wi-Wi). The measurement results show that the precision of initial timing synchronization of the Wi-Wi module is nearly equal to 400 ns. Furthermore, we implement transmission timing control function on Universal Software Radio Peripheral (USRP) synchronized by reference signals of Wi-Wi module. As a result of the measurement evaluation of the implemented system, it is realized that the transmission timing is controlled at the accuracy of the sampling rate of USRP.
{"title":"USRP Implementation of Transmission Timing Control Function for Synchronized SS-CDMA Using Wireless Two-Way Interferometry (Wi-Wi)","authors":"S. Kameda, Yusaku Honma, N. Suematsu, S. Yasuda, N. Shiga","doi":"10.1109/ICUFN49451.2021.9528697","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528697","url":null,"abstract":"Synchronized spread spectrum code division multiple access (SS-CDMA) is very effective for increasing the capacity and reducing the interference with a rapid spread of Internet of things (IoT) devices. Since the synchronized SS-CDMA requires to receive timing synchronization, it is essential to realize transmission timing control of each node using spacetime synchronization. In this paper, we investigate precise time synchronization between nodes using Wireless Two-Way Interferometry (Wi-Wi). The measurement results show that the precision of initial timing synchronization of the Wi-Wi module is nearly equal to 400 ns. Furthermore, we implement transmission timing control function on Universal Software Radio Peripheral (USRP) synchronized by reference signals of Wi-Wi module. As a result of the measurement evaluation of the implemented system, it is realized that the transmission timing is controlled at the accuracy of the sampling rate of USRP.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528677
E. Kudoh, Keisuke Watanabe
To clearly understand wireless communication technology for educational purposes, an inexpensive wireless modulation and demodulation system is required. A simple carrier-signal generator can generate a carrier signal, and a spectrum analyzer can search for a peak frequency and be controlled by a PC. Therefore, a frequency-hopping wireless transmitter and receiver system can be constructed using a carrier-signal generator and spectrum analyzer. We constructed a frequency-hopping transmission system using a carrier-signal generator and spectrum analyzer. To validate this system, we evaluated the peak frequency detection probability and compared it with theoretical values. The results indicate that when the hopping time interval was 2000 ms, the peak frequency detection probability almost coincided with the theoretical values.
{"title":"Construction of Frequency-Hopping System Using Carrier-Signal Generator","authors":"E. Kudoh, Keisuke Watanabe","doi":"10.1109/ICUFN49451.2021.9528677","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528677","url":null,"abstract":"To clearly understand wireless communication technology for educational purposes, an inexpensive wireless modulation and demodulation system is required. A simple carrier-signal generator can generate a carrier signal, and a spectrum analyzer can search for a peak frequency and be controlled by a PC. Therefore, a frequency-hopping wireless transmitter and receiver system can be constructed using a carrier-signal generator and spectrum analyzer. We constructed a frequency-hopping transmission system using a carrier-signal generator and spectrum analyzer. To validate this system, we evaluated the peak frequency detection probability and compared it with theoretical values. The results indicate that when the hopping time interval was 2000 ms, the peak frequency detection probability almost coincided with the theoretical values.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580848","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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