Pub Date : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669015
Yu-Huei Cheng
The zoo is a local facility where some wild or exotic animals are placed in a fence. The main significance of the zoo is to provide educational and animal conservation functions, and secondly to provide public viewing and entertainment. Animal care and management in the zoo is almost open all year round. Its basic tasks include accommodation, breeding, health care, and medical care etc. Because there are nearly hundreds, thousands, or even ten thousands animals with different body shape and characteristics in the zoo that need to be cared for and managed, animal administrators must be skilled in various tools and real time control the condition of all animals, resulting in the heavy workload of the animal administrators and the huge operating expenses of the zoo. Therefore, it is necessary to find ways to reduce the workload of the animal administrators, but also to immediately control the current state of the animals, while saving animal care and management expenses. This study proposes a development architecture for the intelligent animal management system based on the Internet of Things (IoT) and artificial intelligence (AI). Its main purpose is to automate some tedious procedures for caring animals through the IoT and AI to help animal administrators to take care of them and manage them more systematically.
{"title":"A Development Architecture for the Intelligent Animal Care and Management System Based on the Internet of Things and Artificial Intelligence","authors":"Yu-Huei Cheng","doi":"10.1109/ICAIIC.2019.8669015","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669015","url":null,"abstract":"The zoo is a local facility where some wild or exotic animals are placed in a fence. The main significance of the zoo is to provide educational and animal conservation functions, and secondly to provide public viewing and entertainment. Animal care and management in the zoo is almost open all year round. Its basic tasks include accommodation, breeding, health care, and medical care etc. Because there are nearly hundreds, thousands, or even ten thousands animals with different body shape and characteristics in the zoo that need to be cared for and managed, animal administrators must be skilled in various tools and real time control the condition of all animals, resulting in the heavy workload of the animal administrators and the huge operating expenses of the zoo. Therefore, it is necessary to find ways to reduce the workload of the animal administrators, but also to immediately control the current state of the animals, while saving animal care and management expenses. This study proposes a development architecture for the intelligent animal management system based on the Internet of Things (IoT) and artificial intelligence (AI). Its main purpose is to automate some tedious procedures for caring animals through the IoT and AI to help animal administrators to take care of them and manage them more systematically.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"29 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":"129558086","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.2019.8668977
Itaru Aso, K. Okuhara
In this paper, we suggest learning algorithm of a high precision classifier for multivariate signal. The method deals with environmental influences. In this proposal technique, we define the features of the classification target and the environment as population parameters of probability distribution. We estimate the parameters by using the Bayesian inference. The Bayesian decision rule is used for the selection of similar environment properly in the proposed method. We try to evaluate the influence of the environmental change. In the numerical experiments, we verify that the proposed method has high classification accuracy. As the results, we show that our method can adapt environmental influence.
{"title":"A Bayes Classifier Considering Environmental Change for Multivariate Signal Data","authors":"Itaru Aso, K. Okuhara","doi":"10.1109/ICAIIC.2019.8668977","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668977","url":null,"abstract":"In this paper, we suggest learning algorithm of a high precision classifier for multivariate signal. The method deals with environmental influences. In this proposal technique, we define the features of the classification target and the environment as population parameters of probability distribution. We estimate the parameters by using the Bayesian inference. The Bayesian decision rule is used for the selection of similar environment properly in the proposed method. We try to evaluate the influence of the environmental change. In the numerical experiments, we verify that the proposed method has high classification accuracy. As the results, we show that our method can adapt environmental influence.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"76 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":"129302221","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.2019.8668978
Seung Jae Pee, E. Kang, J. Song, J. Jang
The energy market is entering the transitional period, and various types of energy markets such as solar energy will be formed beyond oil and gas. Correspondingly, energy prosumers that individuals and institutions produce and trade surplus electricity will become more widespread. Using the block chain, it guarantees the immutability and transparency of energy transactions, generates ERC20 tokens based on smart contracts, and transactions are automatically executed without third party intervention and can be extended to various transaction conditions. In the transaction, the energy is transferred using the Energy Storage System(ESS) which the seller and the buyer belong, and payment is made by transferring the token through a transaction. Based on this information, this paper suggests proposes a peer-to-peer (P2P) system that can freely trade the produced energy.
{"title":"Blockchain based smart energy trading platform using smart contract","authors":"Seung Jae Pee, E. Kang, J. Song, J. Jang","doi":"10.1109/ICAIIC.2019.8668978","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668978","url":null,"abstract":"The energy market is entering the transitional period, and various types of energy markets such as solar energy will be formed beyond oil and gas. Correspondingly, energy prosumers that individuals and institutions produce and trade surplus electricity will become more widespread. Using the block chain, it guarantees the immutability and transparency of energy transactions, generates ERC20 tokens based on smart contracts, and transactions are automatically executed without third party intervention and can be extended to various transaction conditions. In the transaction, the energy is transferred using the Energy Storage System(ESS) which the seller and the buyer belong, and payment is made by transferring the token through a transaction. Based on this information, this paper suggests proposes a peer-to-peer (P2P) system that can freely trade the produced energy.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"96 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":"131970141","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.2019.8668999
Shigeaki Onoda, K. Okuhara
The web includes enormous data such as patents. The purpose of this research finds the rule of textual patent data and creates new model. Hence, we suggest new weighted method using DEA to handle unstructured data like patent. Our proposed method is advantageous because this considers the value of the patent compared with TF-IDF and other weighted methods. Using suggested method, we probe new text-mining in the field of patent.
{"title":"Selection of Core Words from Textual Patent Data with DEA based on Citation","authors":"Shigeaki Onoda, K. Okuhara","doi":"10.1109/ICAIIC.2019.8668999","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668999","url":null,"abstract":"The web includes enormous data such as patents. The purpose of this research finds the rule of textual patent data and creates new model. Hence, we suggest new weighted method using DEA to handle unstructured data like patent. Our proposed method is advantageous because this considers the value of the patent compared with TF-IDF and other weighted methods. Using suggested method, we probe new text-mining in the field of patent.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"10 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":"133961011","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.2019.8669042
JiSeong Han, Gwangsun Kim, ChanSeo Lee, YeongKwang Han, Ung Hwang, Sunghwan Kim
Predictive models on fire have been increasingly popular in computer image analysis. Due to late strides of deep learning techniques, we are now unprecedently benefited from its flexible applicability. In most cases, however, the conventional algorithms are limited to only single-framed images unlike sequence data that inevitably entails heavy computational time and memory. In this paper, we propose an effective algorithm exploiting the combination of CNNs (convolution neural networks) and RNNs (recurrent neural networks) in a consecutive way so that sequence data can be allowed for the model. The LSTM (long short-term memory) is well-known to be superior to other RNNtype algorithms in accuracy, especially when applying to sequence data. In our extensive experiments, where fire videos (e.g. indoor fire, forest fire) and non-fire videos collected from a range of scenarios are taken into accounts, it is confirmed that our propose methods are found outstanding in predictive power.
{"title":"Predictive Models of Fire via Deep learning Exploiting Colorific Variation","authors":"JiSeong Han, Gwangsun Kim, ChanSeo Lee, YeongKwang Han, Ung Hwang, Sunghwan Kim","doi":"10.1109/ICAIIC.2019.8669042","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669042","url":null,"abstract":"Predictive models on fire have been increasingly popular in computer image analysis. Due to late strides of deep learning techniques, we are now unprecedently benefited from its flexible applicability. In most cases, however, the conventional algorithms are limited to only single-framed images unlike sequence data that inevitably entails heavy computational time and memory. In this paper, we propose an effective algorithm exploiting the combination of CNNs (convolution neural networks) and RNNs (recurrent neural networks) in a consecutive way so that sequence data can be allowed for the model. The LSTM (long short-term memory) is well-known to be superior to other RNNtype algorithms in accuracy, especially when applying to sequence data. In our extensive experiments, where fire videos (e.g. indoor fire, forest fire) and non-fire videos collected from a range of scenarios are taken into accounts, it is confirmed that our propose methods are found outstanding in predictive power.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"26 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":"131829996","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.2019.8669053
Y. Nozaki, M. Yoshikawa
To obtain the internet of things (IoT) security, physical unclonable functions (PUFs) have attracted attention. Regarding hardware security, in recent years, the risk of side-channel attacks (SCAs) for PUF is pointed out. Therefore, countermeasures against SCAs have been proposed, and field programmable gate array (FPGA) implementation evaluations have also been reported. On the other hand, the evaluation of PUFs with countermeasures needs actual modeling attacks using many side-channel information; therefore, costs increase. This study proposes a new PUF security evaluation method, which does not need actual modeling attacks. The proposed method verifies the existence of side-channel leakages by applying statistical tests to measured power consumption waveforms during PUF operations. In experiments using an FPGA, by using the proposed method, it was confirmed that there were significant differences in the PUF without countermeasure. Experiments also showed that significant differences did not appear in the PUF with countermeasure and the proposed method could evaluate the PUF security easily without actual modeling attacks.
{"title":"Side-Channel Resistance Evaluation Method using Statistical Tests for Physical Unclonable Function","authors":"Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICAIIC.2019.8669053","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669053","url":null,"abstract":"To obtain the internet of things (IoT) security, physical unclonable functions (PUFs) have attracted attention. Regarding hardware security, in recent years, the risk of side-channel attacks (SCAs) for PUF is pointed out. Therefore, countermeasures against SCAs have been proposed, and field programmable gate array (FPGA) implementation evaluations have also been reported. On the other hand, the evaluation of PUFs with countermeasures needs actual modeling attacks using many side-channel information; therefore, costs increase. This study proposes a new PUF security evaluation method, which does not need actual modeling attacks. The proposed method verifies the existence of side-channel leakages by applying statistical tests to measured power consumption waveforms during PUF operations. In experiments using an FPGA, by using the proposed method, it was confirmed that there were significant differences in the PUF without countermeasure. Experiments also showed that significant differences did not appear in the PUF with countermeasure and the proposed method could evaluate the PUF security easily without actual modeling attacks.","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":"130879808","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.2019.8669004
N. Kim
To supply the optimum amount of required power to a load device even when the environmental situations including variation of transfer distance, automated adaptive frequency with power-level tracking systems are proposed based on direct monitoring of the power transfer efficiency (PTE) with received power-level and minimum reflection level observed in the transmitter output via bidirectional out-of-band signalling for efficient and stable mid-range magnetic resonance wireless power transfer (WPT) operations. The effectiveness of the proposed schemes has been successfully demonstrated using a normal digital LED TV, as one of representative exemplary cases. Thus, it is anticipated that the proposed solutions can be commonly useful for any WPT application including mobile devices, electric vehicles, biomedical devices, and many more.
{"title":"Adaptive Magnetic Resonance Wireless Power Transfer System with Optimum frequency and Power-Leve Tracking for maintaining highly efficient","authors":"N. Kim","doi":"10.1109/ICAIIC.2019.8669004","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669004","url":null,"abstract":"To supply the optimum amount of required power to a load device even when the environmental situations including variation of transfer distance, automated adaptive frequency with power-level tracking systems are proposed based on direct monitoring of the power transfer efficiency (PTE) with received power-level and minimum reflection level observed in the transmitter output via bidirectional out-of-band signalling for efficient and stable mid-range magnetic resonance wireless power transfer (WPT) operations. The effectiveness of the proposed schemes has been successfully demonstrated using a normal digital LED TV, as one of representative exemplary cases. Thus, it is anticipated that the proposed solutions can be commonly useful for any WPT application including mobile devices, electric vehicles, biomedical devices, and many more.","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":"131245934","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.2019.8669002
Jung Ho Lee, Kwang-Yul Kim, Y. Shin
In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.
{"title":"Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm","authors":"Jung Ho Lee, Kwang-Yul Kim, Y. Shin","doi":"10.1109/ICAIIC.2019.8669002","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669002","url":null,"abstract":"In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"93 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":"132826009","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.2019.8669068
J. Park, D. Yun, Joo-Pyoung Choi, W. Lee
In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.
{"title":"A Novel Case-Based Reasoning Method for Cognitive Frequency Allocation","authors":"J. Park, D. Yun, Joo-Pyoung Choi, W. Lee","doi":"10.1109/ICAIIC.2019.8669068","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669068","url":null,"abstract":"In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"165 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":"132045857","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.2019.8668984
Yanjun Hu, Jiayu Wu, Yi Wang
The iterative precoding scheme is a common algorithm for downlink massive MIMO systems. Due to the large number of system antennas, traditional linear precoding schemes are usually involve the large-scale matrix inversion and leads to high computational complexity. The complexity of the linear precoding algorithm greatly reduced when the iterative algorithm is proposed, but it caused a decline in Bit Error Rate (BER) performance. Improving the BER performance of the iterative algorithm and ensuring the convergence rate has always been the focus of attentions. Currently, there are many iterative methods that only have mathematical theory and not applied to precoding yet. To solve the aforementioned problem, we proposes a precoding scheme based on Symmetric Accelerated Over Relaxation (SAOR) method, which achieve the enhancement BER performance compared to other iterative algorithms. Combined with the actual system, the selection of optimal acceleration factor and relaxation factor are discussed, which is only related to system parameters. The simulation results show that SAOR-based precoding can achieve good BER performance with less iterations and guarantee the convergence rate.
{"title":"SAOR-Based Precoding with Enhanced BER Performance for Massive MIMO Systems","authors":"Yanjun Hu, Jiayu Wu, Yi Wang","doi":"10.1109/ICAIIC.2019.8668984","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668984","url":null,"abstract":"The iterative precoding scheme is a common algorithm for downlink massive MIMO systems. Due to the large number of system antennas, traditional linear precoding schemes are usually involve the large-scale matrix inversion and leads to high computational complexity. The complexity of the linear precoding algorithm greatly reduced when the iterative algorithm is proposed, but it caused a decline in Bit Error Rate (BER) performance. Improving the BER performance of the iterative algorithm and ensuring the convergence rate has always been the focus of attentions. Currently, there are many iterative methods that only have mathematical theory and not applied to precoding yet. To solve the aforementioned problem, we proposes a precoding scheme based on Symmetric Accelerated Over Relaxation (SAOR) method, which achieve the enhancement BER performance compared to other iterative algorithms. Combined with the actual system, the selection of optimal acceleration factor and relaxation factor are discussed, which is only related to system parameters. The simulation results show that SAOR-based precoding can achieve good BER performance with less iterations and guarantee the convergence rate.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"5 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":"124393237","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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