Pub Date : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114948
Mofadal Alymani, Mohsen H. Alhazmi, Alhussain Almarhabi, Hatim Alhazmi, Abdullah Samarkandi, Yu-dong Yao
Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.
{"title":"Rician K-Factor Estimation Using Deep Learning","authors":"Mofadal Alymani, Mohsen H. Alhazmi, Alhussain Almarhabi, Hatim Alhazmi, Abdullah Samarkandi, Yu-dong Yao","doi":"10.1109/WOCC48579.2020.9114948","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114948","url":null,"abstract":"Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129842137","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114937
Lianjun Li, Lingjia Liu, Jianzhong Zhang, J. Ashdown, Y. Yi
In this paper, we introduce a neural network (NN)based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software defined radio (SDR) platform to further provide realistic and meaningful performance comparison against the traditional Wi-Fi receiver. Over the air experiment results show that the introduced RC-based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work.
{"title":"Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots","authors":"Lianjun Li, Lingjia Liu, Jianzhong Zhang, J. Ashdown, Y. Yi","doi":"10.1109/WOCC48579.2020.9114937","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114937","url":null,"abstract":"In this paper, we introduce a neural network (NN)based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software defined radio (SDR) platform to further provide realistic and meaningful performance comparison against the traditional Wi-Fi receiver. Over the air experiment results show that the introduced RC-based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127621774","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}
A steel enterprise is currently trying to avoid the presence of surplus materials since they can greatly increase its operational cost. The complicated production process of steel products makes it difficult to find the causes of surplus materials. In this work, we propose a surplus material prediction problem and solve it based on statistical analysis and machine learning methods. In the concerned problem, we predict whether there are surplus materials under a given group of production parameters. The dataset used in this work is from a real-world three-month steel coil production process. First, data cleaning is conducted to standardize the industrial dataset. Then, the production parameters highly correlated with surplus material prediction results are selected by a series of feature selection methods. Finally, two prediction models based on extreme gradient boosting and logistic regression are presented according to the selected features. The experimental results reveal that the proposed prediction models have similar effectiveness. A visible regression function makes the logistic regression method more suitable for practical application.
{"title":"Data-driven Surplus Material Prediction in Steel Coil Production","authors":"Ziyan Zhao, Xiaoyue Yong, Shixin Liu, Mengchu Zhou","doi":"10.1109/WOCC48579.2020.9114917","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114917","url":null,"abstract":"A steel enterprise is currently trying to avoid the presence of surplus materials since they can greatly increase its operational cost. The complicated production process of steel products makes it difficult to find the causes of surplus materials. In this work, we propose a surplus material prediction problem and solve it based on statistical analysis and machine learning methods. In the concerned problem, we predict whether there are surplus materials under a given group of production parameters. The dataset used in this work is from a real-world three-month steel coil production process. First, data cleaning is conducted to standardize the industrial dataset. Then, the production parameters highly correlated with surplus material prediction results are selected by a series of feature selection methods. Finally, two prediction models based on extreme gradient boosting and logistic regression are presented according to the selected features. The experimental results reveal that the proposed prediction models have similar effectiveness. A visible regression function makes the logistic regression method more suitable for practical application.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116017665","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114923
G. Yue, Xiao-Feng Qi
In this paper, we design efficient methods for the mean and variance estimations of QAM symbols with applications to iterative receivers. The proposed methods for optimal estimations enable scalable hardware implementations for any Gray mapped PAM or QAM with less circuitries. For variance estimations, the proposed method reduces the complexity from $O((log_{2}N)^{2})$ in the existing method to $O(log_{2}N)$ for an N-QAM. Two suboptimal methods are also proposed to avoid the multiplications in the hardware implementations. The presented approximation approaches provide similar or better performance than the existing methods but with simpler implementation and less logical circuitries. In addition, based on the proposed architecture, we present novel unit module designs with disassembled estimation components and the schematics to virtualize the estimation hardware. With efficient design of unit module and control unit, maximized parallelization can be achieved.
{"title":"Efficient Methods and Architectures for Mean and Variance Estimations of QAM Symbols","authors":"G. Yue, Xiao-Feng Qi","doi":"10.1109/WOCC48579.2020.9114923","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114923","url":null,"abstract":"In this paper, we design efficient methods for the mean and variance estimations of QAM symbols with applications to iterative receivers. The proposed methods for optimal estimations enable scalable hardware implementations for any Gray mapped PAM or QAM with less circuitries. For variance estimations, the proposed method reduces the complexity from $O((log_{2}N)^{2})$ in the existing method to $O(log_{2}N)$ for an N-QAM. Two suboptimal methods are also proposed to avoid the multiplications in the hardware implementations. The presented approximation approaches provide similar or better performance than the existing methods but with simpler implementation and less logical circuitries. In addition, based on the proposed architecture, we present novel unit module designs with disassembled estimation components and the schematics to virtualize the estimation hardware. With efficient design of unit module and control unit, maximized parallelization can be achieved.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114907722","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114912
Mohsen H. Alhazmi, Mofadal Alymani, Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Yu-dong Yao
Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.
{"title":"5G Signal Identification Using Deep Learning","authors":"Mohsen H. Alhazmi, Mofadal Alymani, Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Yu-dong Yao","doi":"10.1109/WOCC48579.2020.9114912","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114912","url":null,"abstract":"Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"40 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930105","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114914
Mona Hosney, H. Selmy, K. Elsayed
Visible Light Communication (VLC) is the hope for keeping up the rapid increase of user’s data demands. VLC provides high-speed data connections. However; it suffers from limited optical bandwidth, and performance decline due to either inter-symbol interference (ISI) or co-channel interference (CCI). In this paper, CCI is managed by using Angular Diversity Receiver (ADR) with a limited field of view to reduce the number of interfered signals. After that least-square (LS) channel estimation with maximum-likelihood (ML) equalizer is used to resolve the interfered signals. The bit-error-rate (BER) is calculated at different room positions and receiver’s heights. The simulation results appear that the proposed scheme BER performance has been enhanced at all positions of the ADR.
{"title":"Co-Channel Interference Management in Visible Light Communication","authors":"Mona Hosney, H. Selmy, K. Elsayed","doi":"10.1109/WOCC48579.2020.9114914","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114914","url":null,"abstract":"Visible Light Communication (VLC) is the hope for keeping up the rapid increase of user’s data demands. VLC provides high-speed data connections. However; it suffers from limited optical bandwidth, and performance decline due to either inter-symbol interference (ISI) or co-channel interference (CCI). In this paper, CCI is managed by using Angular Diversity Receiver (ADR) with a limited field of view to reduce the number of interfered signals. After that least-square (LS) channel estimation with maximum-likelihood (ML) equalizer is used to resolve the interfered signals. The bit-error-rate (BER) is calculated at different room positions and receiver’s heights. The simulation results appear that the proposed scheme BER performance has been enhanced at all positions of the ADR.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132649850","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114930
Yu Zhou, Shengliang Peng, Yudong Yao
Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.
{"title":"MAC Protocol Identification Using Convolutional Neural Networks","authors":"Yu Zhou, Shengliang Peng, Yudong Yao","doi":"10.1109/WOCC48579.2020.9114930","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114930","url":null,"abstract":"Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114381060","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114941
Usha Choudhary, V. Janyani, M. A. Khan
This paper presents a modification in conventional asymmetrically clipped optical OFDM (ACO-OFDM) frame for direct detection intensity modulation (DD-IM) system. Proposed dual frame OFDM consists of two similar frames and transmitted with optical phase conjugation for dispersion and non-linearity mitigation in optical fiber. Proposed system is compared with another scheme- phase conjugated sub-carrier coding (PCSC) in OFDM. Authors have compared the proposed system performance with PCSC for single mode fiber (SMF) with length 10 km and multi-mode fiber (MMF) with length 100 meters. Simulation results show that PCSC scheme performs better for SMF but in case of MMF, performance of proposed system is better.
{"title":"Dual Frame OFDM with Optical Phase Conjugation","authors":"Usha Choudhary, V. Janyani, M. A. Khan","doi":"10.1109/WOCC48579.2020.9114941","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114941","url":null,"abstract":"This paper presents a modification in conventional asymmetrically clipped optical OFDM (ACO-OFDM) frame for direct detection intensity modulation (DD-IM) system. Proposed dual frame OFDM consists of two similar frames and transmitted with optical phase conjugation for dispersion and non-linearity mitigation in optical fiber. Proposed system is compared with another scheme- phase conjugated sub-carrier coding (PCSC) in OFDM. Authors have compared the proposed system performance with PCSC for single mode fiber (SMF) with length 10 km and multi-mode fiber (MMF) with length 100 meters. Simulation results show that PCSC scheme performs better for SMF but in case of MMF, performance of proposed system is better.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"54 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131882745","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114945
Chong Shen, Kun Zhang, Keliu Long
The trusted infrastructure based on blockchain technology can be intelligently integrated with emerging technologies such as cloud computing, big data, the Internet of Things, artificial intelligence, etc., and achieve the realization of machine trust, data trust, and autonomous trust in a trusted digital infrastructure environment. Use blockchain technology to build a trusted infrastructure and promote the development and application of the integration of diverse high-tech. Together, we will enhance the capabilities of information acquisition, real-time feedback, and intelligent service anywhere, anytime for this complex adaptive system in cities. Then the decision-making ability of intelligent convergence emerges quickly. With the continuous development of blockchain technology, it is possible to build a set of credible infrastructure environment based on blockchain technology. The article conducts in-depth research in the areas of performance, scalability, privacy and security, with a view to helping to build a trusted infrastructure environment, and then realizing the construction of a new type of smart city.
{"title":"Research on Hainan Trusted Digital Infrastructure Construction Framework","authors":"Chong Shen, Kun Zhang, Keliu Long","doi":"10.1109/WOCC48579.2020.9114945","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114945","url":null,"abstract":"The trusted infrastructure based on blockchain technology can be intelligently integrated with emerging technologies such as cloud computing, big data, the Internet of Things, artificial intelligence, etc., and achieve the realization of machine trust, data trust, and autonomous trust in a trusted digital infrastructure environment. Use blockchain technology to build a trusted infrastructure and promote the development and application of the integration of diverse high-tech. Together, we will enhance the capabilities of information acquisition, real-time feedback, and intelligent service anywhere, anytime for this complex adaptive system in cities. Then the decision-making ability of intelligent convergence emerges quickly. With the continuous development of blockchain technology, it is possible to build a set of credible infrastructure environment based on blockchain technology. The article conducts in-depth research in the areas of performance, scalability, privacy and security, with a view to helping to build a trusted infrastructure environment, and then realizing the construction of a new type of smart city.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124911957","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：