Pub Date : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668988
Dengjun Zhu, Haiwei Yuan, Jinlong Yan, Yanping Qing, Weijie Yang
In recent years, Wireless Sensor Network (WSN) have been widely used in the Industrial Internet of Things (IIOT), especially in smart grid. The sensors not only extract the key attributes of the basic data onto operating state of various underground cables, but also remove the redundant description of the data onto the data systems. Further, the sensors can also deal with inconsistent information about the data systems. In order to ensure the reliable fusion data containing all the key information of the basic data and meeting the standard requirements of underground cable, we could overlap the sensing areas of the sensor nodes with each other. The deployment often has the problems of weak expansion ability, network delay and uneven energy consumption of nodes. Therefore, this paper focuses on the optimal weight data fusion analysis to improve the current situation. This method is more efficient for data fusion processing and data extraction.
{"title":"Data Fusion Analysis with Optimal Weight in Smart Grid","authors":"Dengjun Zhu, Haiwei Yuan, Jinlong Yan, Yanping Qing, Weijie Yang","doi":"10.1109/ICAIIC.2019.8668988","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668988","url":null,"abstract":"In recent years, Wireless Sensor Network (WSN) have been widely used in the Industrial Internet of Things (IIOT), especially in smart grid. The sensors not only extract the key attributes of the basic data onto operating state of various underground cables, but also remove the redundant description of the data onto the data systems. Further, the sensors can also deal with inconsistent information about the data systems. In order to ensure the reliable fusion data containing all the key information of the basic data and meeting the standard requirements of underground cable, we could overlap the sensing areas of the sensor nodes with each other. The deployment often has the problems of weak expansion ability, network delay and uneven energy consumption of nodes. Therefore, this paper focuses on the optimal weight data fusion analysis to improve the current situation. This method is more efficient for data fusion processing and data extraction.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132037920","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668975
K. Yano, Naoto Egashira, Julian Webber, M. Usui, Yoshinori Suzuki
The authors have studied a multiband wireless local area network (MB-WLAN) which can effectively detect and exploit unused radio resources scattered in time and frequency domains. The MB-WLAN sets one or more primary channels (PCHs) in multiple frequency bands, and each station (STA) carries out random back-off process on the multiple primary channels to obtain a transmission opportunity (TXOP). Once a STA obtains a TXOP on any PCH, it checks whether or not another TXOP can be obtained on any other PCH in near future. If the STA judges that it can obtain another TXOP, it pends its transmission until another TXOP is obtained on any other PCH, and then a channel-bonded frame is transmitted. A suitable pending duration depends on the level of congestion on each PCH because the STA lose its TXOP more frequently to other STA’s frame transmission as the PCH gets more crowded. This paper, therefore, proposes a method to control the maximum pending duration with the aid of idle length prediction based on probabilistic neural network (PNN). This paper also proposes a method to control the timing to invoke learning of channel usage for PNN in order to get rid of the impact of self-transmission on the characteristics of channel usage. In order to validate the effectiveness of the proposals, this paper evaluates the achievable throughput of the MB-WLAN by computer simulation assuming IEEE 802.11n/ac-based WLAN operated in the 2.4GHz and 5GHz bands and 4-antenna STA. It is confirmed that the MBWLAN with two proposals can achieve almost best performance regardless the level of congestion on PCHs.
{"title":"Achievable Throughput of Multiband Wireless LAN using Simultaneous Transmission over Multiple Primary Channels Assisted by Idle Length Prediction Based on PNN","authors":"K. Yano, Naoto Egashira, Julian Webber, M. Usui, Yoshinori Suzuki","doi":"10.1109/ICAIIC.2019.8668975","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668975","url":null,"abstract":"The authors have studied a multiband wireless local area network (MB-WLAN) which can effectively detect and exploit unused radio resources scattered in time and frequency domains. The MB-WLAN sets one or more primary channels (PCHs) in multiple frequency bands, and each station (STA) carries out random back-off process on the multiple primary channels to obtain a transmission opportunity (TXOP). Once a STA obtains a TXOP on any PCH, it checks whether or not another TXOP can be obtained on any other PCH in near future. If the STA judges that it can obtain another TXOP, it pends its transmission until another TXOP is obtained on any other PCH, and then a channel-bonded frame is transmitted. A suitable pending duration depends on the level of congestion on each PCH because the STA lose its TXOP more frequently to other STA’s frame transmission as the PCH gets more crowded. This paper, therefore, proposes a method to control the maximum pending duration with the aid of idle length prediction based on probabilistic neural network (PNN). This paper also proposes a method to control the timing to invoke learning of channel usage for PNN in order to get rid of the impact of self-transmission on the characteristics of channel usage. In order to validate the effectiveness of the proposals, this paper evaluates the achievable throughput of the MB-WLAN by computer simulation assuming IEEE 802.11n/ac-based WLAN operated in the 2.4GHz and 5GHz bands and 4-antenna STA. It is confirmed that the MBWLAN with two proposals can achieve almost best performance regardless the level of congestion on PCHs.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125197554","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668844
Hang Liu, Xu Zhu, T. Fujii
Spectrum sensing of orthogonal frequency division multiplex (OFDM) system has always been a challenge in cognitive radios (CR). In this paper on the basis of “classification converted sensing” scheme, the cyclostationary periodogram generated by OFDM pilots is deduced in the form of images. These images are then plugged into the convolutional neural networks (CNNs) for classifications due to CNN's strength in image classification. More importantly, certain of concerns about CNN adoption in CR system is settled. Firstly, to achieve spectrum sensing against severe noise pollution and channel fading, we use the adversarial training where a CR-specific, modified training database is proposed. Then, to settle the serviceability which is constrained by the computing power at the CR user end, the input images and the CNN architecture are refined to guarantee a low-complexity but high-performance sensing scheme. Simulation results proved our method possesses an excellent sensing capability while achieving higher detection accuracy over the conventional way.
{"title":"Adversarial training for low-complexity convolutional neural networks using in spectrum sensing","authors":"Hang Liu, Xu Zhu, T. Fujii","doi":"10.1109/ICAIIC.2019.8668844","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668844","url":null,"abstract":"Spectrum sensing of orthogonal frequency division multiplex (OFDM) system has always been a challenge in cognitive radios (CR). In this paper on the basis of “classification converted sensing” scheme, the cyclostationary periodogram generated by OFDM pilots is deduced in the form of images. These images are then plugged into the convolutional neural networks (CNNs) for classifications due to CNN's strength in image classification. More importantly, certain of concerns about CNN adoption in CR system is settled. Firstly, to achieve spectrum sensing against severe noise pollution and channel fading, we use the adversarial training where a CR-specific, modified training database is proposed. Then, to settle the serviceability which is constrained by the computing power at the CR user end, the input images and the CNN architecture are refined to guarantee a low-complexity but high-performance sensing scheme. Simulation results proved our method possesses an excellent sensing capability while achieving higher detection accuracy over the conventional way.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633466","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669080
Shuhei Kawaguchi, Y. Fukuyama
This paper proposes parallel hybrid particle swarm optimization (PHPSO) for the integration framework of optimal operational planning problem of an energy plant and production scheduling problem for actual reduction of the secondary energy costs in factories. Conventionally, fixed loads of the various tertiary energies have been utilized for solving optimal operational planning of the energy plant so far. On the contrary, in this paper, the loads of the various tertiary energies are calculated according to candidates of production scheduling and actual reduction of the secondary energy costs in factories is realized. The proposed method is applied to 10 jobs and 10 machines problem and it is verified that it can minimize the secondary energy cost and production time simultaneously with higher quality solutions compared with the conventional HPSO, and realize fast computation by parallel computation using PHPSO.
{"title":"Parallel Hybrid Particle Swarm Optimization for Integration Framework of Optimal Operational Planning Problem of an Energy Plant and Production Scheduling Problem","authors":"Shuhei Kawaguchi, Y. Fukuyama","doi":"10.1109/ICAIIC.2019.8669080","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669080","url":null,"abstract":"This paper proposes parallel hybrid particle swarm optimization (PHPSO) for the integration framework of optimal operational planning problem of an energy plant and production scheduling problem for actual reduction of the secondary energy costs in factories. Conventionally, fixed loads of the various tertiary energies have been utilized for solving optimal operational planning of the energy plant so far. On the contrary, in this paper, the loads of the various tertiary energies are calculated according to candidates of production scheduling and actual reduction of the secondary energy costs in factories is realized. The proposed method is applied to 10 jobs and 10 machines problem and it is verified that it can minimize the secondary energy cost and production time simultaneously with higher quality solutions compared with the conventional HPSO, and realize fast computation by parallel computation using PHPSO.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129421338","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}
The aim of this study is detecting the edge of the medical MRI (Magnetic Resonance Imaging) images. This paper describes as efficient and accurate enhancement and detection method by designing 5-Tap bandpass filter signal and two high frequency sub-band MRI images in digital wavelet domain. Simulation results shows that the proposed method has high accuracy and enhancement in detecting the edge images as is compared to existing method. It provide a helpful and efficient solution for detecting disease lots of medical MRI image, and this method provide new insights in overcoming the scale sensitivity and noises in edge detection.
{"title":"Design of Wavelet Digital Filter for Edge Detection of Medical MRI image","authors":"Woon Cho, Daewon Chung, Gyungmin Hwang, Joonhyeon Jeon","doi":"10.1109/ICAIIC.2019.8668966","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668966","url":null,"abstract":"The aim of this study is detecting the edge of the medical MRI (Magnetic Resonance Imaging) images. This paper describes as efficient and accurate enhancement and detection method by designing 5-Tap bandpass filter signal and two high frequency sub-band MRI images in digital wavelet domain. Simulation results shows that the proposed method has high accuracy and enhancement in detecting the edge images as is compared to existing method. It provide a helpful and efficient solution for detecting disease lots of medical MRI image, and this method provide new insights in overcoming the scale sensitivity and noises in edge detection.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130988330","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668976
Alberto Fornaser, M. Cecco, Teruhiro Mizumoto, K. Yasumoto
Recognition of activity of daily living (ADL) with ubiquitous sensors has been studied so far, aiming to provide services like automatic life logging, elderly monitoring and energy saving in domestic environments. Although existing studies achieve good accuracy of ADL recognition on average, mis-classification of some activities often occur. In this paper, we try to minimize mis-classification in ADL recognition through reliability assessment of the recognition results obtained by machine learning. Specifically, we propose a novel ADL recognition model which extends the random forest classifier trained by ADL data-set by adding the real time uncertainty propagation of the measured variables to each decision tree providing thus the confidence probability of each output class. This adds to the classifier output a confidence value that holds an important role for many purposes such as decision making, features design to improve the classification rate for some classes, etc. The proposed model classifies the input data samples into activity classes with high confidence probability (e.g., more than 50% confidence) and an unclassifiable class, where higher confidence probability leads to the higher recognition accuracy but higher ratio of unclassifiable samples. Through experiments, we confirmed that the proposed model achieve 75% accuracy with less than 30% unclassifiable samples and 95% accuracy with 50% unclassifiable samples.
{"title":"Reliability assessment on human activity recognition","authors":"Alberto Fornaser, M. Cecco, Teruhiro Mizumoto, K. Yasumoto","doi":"10.1109/ICAIIC.2019.8668976","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668976","url":null,"abstract":"Recognition of activity of daily living (ADL) with ubiquitous sensors has been studied so far, aiming to provide services like automatic life logging, elderly monitoring and energy saving in domestic environments. Although existing studies achieve good accuracy of ADL recognition on average, mis-classification of some activities often occur. In this paper, we try to minimize mis-classification in ADL recognition through reliability assessment of the recognition results obtained by machine learning. Specifically, we propose a novel ADL recognition model which extends the random forest classifier trained by ADL data-set by adding the real time uncertainty propagation of the measured variables to each decision tree providing thus the confidence probability of each output class. This adds to the classifier output a confidence value that holds an important role for many purposes such as decision making, features design to improve the classification rate for some classes, etc. The proposed model classifies the input data samples into activity classes with high confidence probability (e.g., more than 50% confidence) and an unclassifiable class, where higher confidence probability leads to the higher recognition accuracy but higher ratio of unclassifiable samples. Through experiments, we confirmed that the proposed model achieve 75% accuracy with less than 30% unclassifiable samples and 95% accuracy with 50% unclassifiable samples.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124613258","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669020
Hsinying Liang, Hao-Yue Jiang
The artificial bee colony-based SLM (ABC-SLM) scheme, which is a novel PAPR reduction scheme, has been proposed to reduce the peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. High PAPR can degrade the efficiency of high-power amplifier and is one of the major disadvantages of OFDM systems. This paper proposes a modified ABC-SLM scheme to further improve the PAPR reduction performance of ABC-SLM scheme. The proposed method, called GA-ABC-SLM, is combined the gene algorithm (GA) with the artificial bee colony-based SLM scheme. The simulation results show that the GA-ABC-SLM scheme has better PAPR reduction performance than the ABC-SLM scheme.
{"title":"The Modified Artificial Bee Colony-Based SLM Scheme for PAPR Reduction in OFDM Systems","authors":"Hsinying Liang, Hao-Yue Jiang","doi":"10.1109/ICAIIC.2019.8669020","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669020","url":null,"abstract":"The artificial bee colony-based SLM (ABC-SLM) scheme, which is a novel PAPR reduction scheme, has been proposed to reduce the peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. High PAPR can degrade the efficiency of high-power amplifier and is one of the major disadvantages of OFDM systems. This paper proposes a modified ABC-SLM scheme to further improve the PAPR reduction performance of ABC-SLM scheme. The proposed method, called GA-ABC-SLM, is combined the gene algorithm (GA) with the artificial bee colony-based SLM scheme. The simulation results show that the GA-ABC-SLM scheme has better PAPR reduction performance than the ABC-SLM scheme.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126463515","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668992
H. You, Hyung-jik Kim, Dong-Kyun Joo, Seung Min Lee, Jeongung Kim, Sunwoong Choi
Near Infrared (NIR) spectroscopy is fast and non-destructive methods for analyzing materials without pretreatment. Especially as portable NIR spectrometers have been developed, the research of spectral analysis has applied to various open environment and field. In this paper, we classify visually indistinguishable eight food powders using portable VIS-NIR spectrometer with a wavelength range of 450 to 1000 nm with CNN (Convolutional Neural Network), one of the machine learnings. Further we consider open set recognition where unknown classes should be rejected at test time. The proposed CNN model achieved an accuracy of 100% for eight food powders, and 91.2% with open set. Our experimental results demonstrate the potential of material analysis using a portable VIS-NIR spectrometer with machine learning.
{"title":"Classification of Food Powders with Open Set using Portable VIS-NIR Spectrometer","authors":"H. You, Hyung-jik Kim, Dong-Kyun Joo, Seung Min Lee, Jeongung Kim, Sunwoong Choi","doi":"10.1109/ICAIIC.2019.8668992","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668992","url":null,"abstract":"Near Infrared (NIR) spectroscopy is fast and non-destructive methods for analyzing materials without pretreatment. Especially as portable NIR spectrometers have been developed, the research of spectral analysis has applied to various open environment and field. In this paper, we classify visually indistinguishable eight food powders using portable VIS-NIR spectrometer with a wavelength range of 450 to 1000 nm with CNN (Convolutional Neural Network), one of the machine learnings. Further we consider open set recognition where unknown classes should be rejected at test time. The proposed CNN model achieved an accuracy of 100% for eight food powders, and 91.2% with open set. Our experimental results demonstrate the potential of material analysis using a portable VIS-NIR spectrometer with machine learning.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126074230","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669084
Hyun-Woo Kim, Keonsoo Lee, Chanki Moon, Yunyoung Nam
In this paper, we present an implementation of a smart scale that can measure a subject’s weight, heart rate and detect atrial fibrillation (AF). For weight measurement, four load cell sensors are used. For measuring heart rates and detecting AF, PSL-iECG2 is used. Load cell sensors and PSL-iECG2 are connected to Arduino Uno. As Arduino Uno has not enough computing power to analyze ECG signals and determine AF, Arduino Uno is connected to smartphone in Bluetooth. From the ECG signals, R peaks are extracted and using the R-R intervals, heart rates are calculated. AF is detected using RMSSD and Shannon entropy extracted from R-R intervals. We evaluate three classifiers that are kNN, DT, and NNs. The accuracies of each classifier for detecting AF are 83.7%, 83.7%, and 89.1%, respectively.
{"title":"Comparative Analysis of Machine Learning Algorithms along with Classifiers for AF Detection using a Scale","authors":"Hyun-Woo Kim, Keonsoo Lee, Chanki Moon, Yunyoung Nam","doi":"10.1109/ICAIIC.2019.8669084","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669084","url":null,"abstract":"In this paper, we present an implementation of a smart scale that can measure a subject’s weight, heart rate and detect atrial fibrillation (AF). For weight measurement, four load cell sensors are used. For measuring heart rates and detecting AF, PSL-iECG2 is used. Load cell sensors and PSL-iECG2 are connected to Arduino Uno. As Arduino Uno has not enough computing power to analyze ECG signals and determine AF, Arduino Uno is connected to smartphone in Bluetooth. From the ECG signals, R peaks are extracted and using the R-R intervals, heart rates are calculated. AF is detected using RMSSD and Shannon entropy extracted from R-R intervals. We evaluate three classifiers that are kNN, DT, and NNs. The accuracies of each classifier for detecting AF are 83.7%, 83.7%, and 89.1%, respectively.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916456","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669012
E. Dahlman, S. Parkvall, J. Peisa, H. Tullberg, H. Murai, M. Fujioka
With the completion of the first release (Rel-15) of the 3GPP fifth-generation (5G) NR specifications [1, 2], the research community should now direct its focus towards the next step in the evolution of wireless mobile communication. Similar to earlier generations, it can be expected that the next ten years will see a gradual evolution of NR, introducing new innovative technology components and further enhancing the capabilities and expanding the scope of 5G wireless access. In a longer-time perspective, we may see the emergence of completely new “beyond 5G” radio-access technology.
{"title":"Artificial Intelligence in Future Evolution of Mobile Communication","authors":"E. Dahlman, S. Parkvall, J. Peisa, H. Tullberg, H. Murai, M. Fujioka","doi":"10.1109/ICAIIC.2019.8669012","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669012","url":null,"abstract":"With the completion of the first release (Rel-15) of the 3GPP fifth-generation (5G) NR specifications [1, 2], the research community should now direct its focus towards the next step in the evolution of wireless mobile communication. Similar to earlier generations, it can be expected that the next ten years will see a gradual evolution of NR, introducing new innovative technology components and further enhancing the capabilities and expanding the scope of 5G wireless access. In a longer-time perspective, we may see the emergence of completely new “beyond 5G” radio-access technology.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127542451","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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