Pub Date : 2021-12-31DOI: 10.1063/9780735423077_003
B. Elliott
. We investigate the behavior of a greedy sequence on the sphere S d defined so that at each step the point that minimizes the Riesz s -energy is added to the existing set of points. We show that for 0 < s < d , the greedy sequence achieves optimal second-order behavior for the Riesz s -energy (up to constants). In order to obtain this result, we prove that the second-order term of the maximal polarization with Riesz s -kernels is of order N s/d in the same range 0 < s < d . Furthermore, using the Stolarsky principle relating the L 2 -discrepancy of a point set with the pairwise sum of distances (Riesz energy with s = − 1), we also obtain a simple upper bound on the L 2 -spherical cap discrepancy of the greedy sequence and give numerical examples that indicate that the true discrepancy is much lower.
． 我们研究了一个贪心序列在球面上的行为，该贪心序列定义为在每一步中将Riesz S -能量最小的点添加到现有的点集中。我们证明了当0 < s < d时，贪心序列对于Riesz s -能量(不超过常数)达到最优的二阶行为。为了得到这个结果，我们证明了具有Riesz s -核的最大偏振的二阶项在0 < s < d范围内为N s/d阶。此外，利用将点集的l2 -差异与距离的两两和(s = - 1的Riesz能量)联系起来的Stolarsky原理，我们也得到了贪心序列的l2 -球帽差异的一个简单上界，并给出了数值例子，表明真正的差异要小得多。
{"title":"Polarization","authors":"B. Elliott","doi":"10.1063/9780735423077_003","DOIUrl":"https://doi.org/10.1063/9780735423077_003","url":null,"abstract":". We investigate the behavior of a greedy sequence on the sphere S d defined so that at each step the point that minimizes the Riesz s -energy is added to the existing set of points. We show that for 0 < s < d , the greedy sequence achieves optimal second-order behavior for the Riesz s -energy (up to constants). In order to obtain this result, we prove that the second-order term of the maximal polarization with Riesz s -kernels is of order N s/d in the same range 0 < s < d . Furthermore, using the Stolarsky principle relating the L 2 -discrepancy of a point set with the pairwise sum of distances (Riesz energy with s = − 1), we also obtain a simple upper bound on the L 2 -spherical cap discrepancy of the greedy sequence and give numerical examples that indicate that the true discrepancy is much lower.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89660537","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928982
A. F. Hussein, H. Elgala, B. Fahs, M. Hella
In this paper, an experimental investigation of adaptive bit-loading for DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is presented. The carried out measurements demonstrate performance enhancement with DCO-OFDM in a signal-to-noise (SNR) limited environment due to the use of silicon PN photodiode (PD). A PN photodetector usage sets additional bandwidth and SNR constraints that have not been tackled through the majority of published works on visible light communications (VLC). Most of the prior reported works employ commercial-off-the-shelf (COTS) PIN or Avalanche-photodiodes (APD) offering by default higher SNR and larger bandwidth than PN-type PDs. This approach targets at taking one step towards fully integrated low cost fabrication VLC systems. The link utilizes a 650-nm red LED source and a CMOS-compatible reverse-biased PN photodetector receiver. A separation of 4 meters makes the link compatible with indoor VLC applications. A data-rate performance of 172 Mb/s is measured with a bit-error-rate (BER) of 1.9×10−3 which is below the forward error correction (FEC) VLC limit. The effective DC power consumption of the link is about 345 mW. This presents, to the authors' knowledge, a record energy-per-bit performance of around 2 nJ/bit at 172 Mb/s over all reported VLC links to date.
{"title":"Experimental investigation of DCO-OFDM adaptive loading using Si PN-based receiver","authors":"A. F. Hussein, H. Elgala, B. Fahs, M. Hella","doi":"10.1109/WOCC.2017.7928982","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928982","url":null,"abstract":"In this paper, an experimental investigation of adaptive bit-loading for DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is presented. The carried out measurements demonstrate performance enhancement with DCO-OFDM in a signal-to-noise (SNR) limited environment due to the use of silicon PN photodiode (PD). A PN photodetector usage sets additional bandwidth and SNR constraints that have not been tackled through the majority of published works on visible light communications (VLC). Most of the prior reported works employ commercial-off-the-shelf (COTS) PIN or Avalanche-photodiodes (APD) offering by default higher SNR and larger bandwidth than PN-type PDs. This approach targets at taking one step towards fully integrated low cost fabrication VLC systems. The link utilizes a 650-nm red LED source and a CMOS-compatible reverse-biased PN photodetector receiver. A separation of 4 meters makes the link compatible with indoor VLC applications. A data-rate performance of 172 Mb/s is measured with a bit-error-rate (BER) of 1.9×10−3 which is below the forward error correction (FEC) VLC limit. The effective DC power consumption of the link is about 345 mW. This presents, to the authors' knowledge, a record energy-per-bit performance of around 2 nJ/bit at 172 Mb/s over all reported VLC links to date.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"194 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72859529","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7929010
T. Mekkawy, Rugui Yao, Fei Xu, Ling Wang
The objective of this paper is to maximize the secrecy rate in an untrusted relay network with imperfect channel state information (CSI). Considering bounded channel estimation error in cooperative jamming network, we optimize the power allocation for confidential and jamming signals to maximize the instantaneous secrecy rate. The upper and lower bounds of achievable secrecy rate are analyzed. The ergodic secrecy rate (ESR) is further derived to evaluate the maximum average secrecy rate. And the optimal power allocation is also studied based on ESR. Numerical results show optimal power allocation can increase both instantaneous secrecy rate and ESR, and at higher SNR, the impact of channel estimation error can be neglected for the maximum ESR, which is asymptotically approaches to the upper bound.
{"title":"Optimal power allocation for achievable secrecy rate in an untrusted relay network with bounded channel estimation error","authors":"T. Mekkawy, Rugui Yao, Fei Xu, Ling Wang","doi":"10.1109/WOCC.2017.7929010","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7929010","url":null,"abstract":"The objective of this paper is to maximize the secrecy rate in an untrusted relay network with imperfect channel state information (CSI). Considering bounded channel estimation error in cooperative jamming network, we optimize the power allocation for confidential and jamming signals to maximize the instantaneous secrecy rate. The upper and lower bounds of achievable secrecy rate are analyzed. The ergodic secrecy rate (ESR) is further derived to evaluate the maximum average secrecy rate. And the optimal power allocation is also studied based on ESR. Numerical results show optimal power allocation can increase both instantaneous secrecy rate and ESR, and at higher SNR, the impact of channel estimation error can be neglected for the maximum ESR, which is asymptotically approaches to the upper bound.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"2014 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86738420","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928971
Umar Ahsan, Abdul Bais
Smart grid is a technological advancement to the traditional power system that provides efficient and reliable utilization of energy resources. Large number of sensors are becoming part of the power network to improve its efficiency. These sensors enable communication between home appliances and power generators to enhance home appliance automation, monitoring and remote control capabilities. As smart power grid incorporates a large number of data-generating embedded sensors; key questions are where in the network to process and analyze the data, and how to perform the analysis. In this paper a test bed is discussed to highlight advantages of distributed smart grid architecture by comparing central and local processing of data. Furthermore, it discusses the advantages of local processing of sensors' generated data to manage big data and introduces machine learning algorithms for data processing. In addition to that, we present results for our test bed that prototypes distributed smart grid architecture. Finally, it concludes with the discussion of our results and future up-gradation of power system.
{"title":"Distributed big data management in smart grid","authors":"Umar Ahsan, Abdul Bais","doi":"10.1109/WOCC.2017.7928971","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928971","url":null,"abstract":"Smart grid is a technological advancement to the traditional power system that provides efficient and reliable utilization of energy resources. Large number of sensors are becoming part of the power network to improve its efficiency. These sensors enable communication between home appliances and power generators to enhance home appliance automation, monitoring and remote control capabilities. As smart power grid incorporates a large number of data-generating embedded sensors; key questions are where in the network to process and analyze the data, and how to perform the analysis. In this paper a test bed is discussed to highlight advantages of distributed smart grid architecture by comparing central and local processing of data. Furthermore, it discusses the advantages of local processing of sensors' generated data to manage big data and introduces machine learning algorithms for data processing. In addition to that, we present results for our test bed that prototypes distributed smart grid architecture. Finally, it concludes with the discussion of our results and future up-gradation of power system.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89020356","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928992
Zaichen Zhang, Liang Wu, J. Dang, Guanghao Zhu, Jiashun Hu, Hao Jiang, X. You
Optical wireless communication (OWC) is position sensitive and deemed not suitable for ultra-high throughput mobile communications. In this paper, we propose an optical mobile communication (OMC) system, which dynamically allocates optical beams to mobile users and tracks the users adaptively, thus extends OWC to mobile scenarios. Principles of OMC is introduced and several key design problems are discussed.
{"title":"Optical mobile communications: Principles and challenges","authors":"Zaichen Zhang, Liang Wu, J. Dang, Guanghao Zhu, Jiashun Hu, Hao Jiang, X. You","doi":"10.1109/WOCC.2017.7928992","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928992","url":null,"abstract":"Optical wireless communication (OWC) is position sensitive and deemed not suitable for ultra-high throughput mobile communications. In this paper, we propose an optical mobile communication (OMC) system, which dynamically allocates optical beams to mobile users and tracks the users adaptively, thus extends OWC to mobile scenarios. Principles of OMC is introduced and several key design problems are discussed.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82986220","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928986
Carlos Mateo, Jesús Clemente, P. G. Ducar, P. L. Carro, J. D. Mingo, Iñigo Salinas
The main disadvantage of Radio over Fiber (RoF) links digital predistortion (DPD) is that it is necessary to feedback the output signals at the Remote Radio Head (RRH) in a real scenario. Along the literature digital predistortion on RoF mobile fronthaul architectures considers an ideal feedback system, so it cannot be deployed in real systems. We propose the use of a RoF link to feedback these signals. This RoF link can be accomplished by an independent RoF link, in which the feedback signals are sent from the RRH to the Baseband Unit (BBU). Experiments are carried out with a 20MHz-LTE downlink signal (16QAM) at 2.65 GHz within the Band 7 of the LTE standard. The DPD performance has been tested in an intensity modulation/direct detection (IM/DD) RoF mobile fronthaul system. Experimental results reveal that with a properly postdistorter modeling of the feedback RoF link it is possible to achieve Adjacent Channel Power Ratio (ACPR) and Error Vector Magnitude (EVM) values close to the results with an ideal feedback link.
{"title":"Linearization of a Radio-over-Fiber mobile fronthaul with feedback loop","authors":"Carlos Mateo, Jesús Clemente, P. G. Ducar, P. L. Carro, J. D. Mingo, Iñigo Salinas","doi":"10.1109/WOCC.2017.7928986","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928986","url":null,"abstract":"The main disadvantage of Radio over Fiber (RoF) links digital predistortion (DPD) is that it is necessary to feedback the output signals at the Remote Radio Head (RRH) in a real scenario. Along the literature digital predistortion on RoF mobile fronthaul architectures considers an ideal feedback system, so it cannot be deployed in real systems. We propose the use of a RoF link to feedback these signals. This RoF link can be accomplished by an independent RoF link, in which the feedback signals are sent from the RRH to the Baseband Unit (BBU). Experiments are carried out with a 20MHz-LTE downlink signal (16QAM) at 2.65 GHz within the Band 7 of the LTE standard. The DPD performance has been tested in an intensity modulation/direct detection (IM/DD) RoF mobile fronthaul system. Experimental results reveal that with a properly postdistorter modeling of the feedback RoF link it is possible to achieve Adjacent Channel Power Ratio (ACPR) and Error Vector Magnitude (EVM) values close to the results with an ideal feedback link.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73305167","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928969
Poyuan Jeng, Li-Chun Wang
Sleeping is one of the most important activities in our daily lives. However, very few people really understand their sleeping habits, which affect sleep-related diseases such as sleep apnea, back problems or even snoring. Most current techniques that monitor, predict and quantify sleep postures are limited to use in hospitals and/or need the intervention of caregivers. In this paper, we describe a system to automatically monitor, predict and quantify sleep postures that may be self-applied by the general public even in a non-hospital environment such as at a persons home. A Random Forest approach is adopted during training to predict and quantify sleep postures. After going through training procedures, a person needs only one sensor placed on the wrist to recognize the persons sleep postures. Our preliminary experiments using a set of testing data show about 90 percent accuracy, indicating that this design has a promising future to accurately analyze, predict and quantify human sleep postures.
{"title":"Stream data analysis of body sensors for sleep posture monitoring: An automatic labelling approach","authors":"Poyuan Jeng, Li-Chun Wang","doi":"10.1109/WOCC.2017.7928969","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928969","url":null,"abstract":"Sleeping is one of the most important activities in our daily lives. However, very few people really understand their sleeping habits, which affect sleep-related diseases such as sleep apnea, back problems or even snoring. Most current techniques that monitor, predict and quantify sleep postures are limited to use in hospitals and/or need the intervention of caregivers. In this paper, we describe a system to automatically monitor, predict and quantify sleep postures that may be self-applied by the general public even in a non-hospital environment such as at a persons home. A Random Forest approach is adopted during training to predict and quantify sleep postures. After going through training procedures, a person needs only one sensor placed on the wrist to recognize the persons sleep postures. Our preliminary experiments using a set of testing data show about 90 percent accuracy, indicating that this design has a promising future to accurately analyze, predict and quantify human sleep postures.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"7 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84798163","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7929000
Shengliang Peng, Hanyu Jiang, Huaxia Wang, H. Alwageed, Yu-dong Yao
Deep learning (DL) is a powerful classification technique that has great success in many application domains. However, its usage in communication systems has not been well explored. In this paper, we address the issue of using DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is utilized to complete the classification task. We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training. Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison. Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection.
{"title":"Modulation classification using convolutional Neural Network based deep learning model","authors":"Shengliang Peng, Hanyu Jiang, Huaxia Wang, H. Alwageed, Yu-dong Yao","doi":"10.1109/WOCC.2017.7929000","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7929000","url":null,"abstract":"Deep learning (DL) is a powerful classification technique that has great success in many application domains. However, its usage in communication systems has not been well explored. In this paper, we address the issue of using DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is utilized to complete the classification task. We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training. Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison. Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"61 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89822281","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 : 2017-04-07DOI: 10.1109/WOCC.2017.7928973
Haoyue Liu, Mengchu Zhou
A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale dataset from Santander Bank. The results show that our approach can achieve higher Area Under the Curve (AUC) and less computational time. We also compare it with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that it outperforms them but needs slightly more computational efforts.
{"title":"Decision tree rule-based feature selection for large-scale imbalanced data","authors":"Haoyue Liu, Mengchu Zhou","doi":"10.1109/WOCC.2017.7928973","DOIUrl":"https://doi.org/10.1109/WOCC.2017.7928973","url":null,"abstract":"A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale dataset from Santander Bank. The results show that our approach can achieve higher Area Under the Curve (AUC) and less computational time. We also compare it with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that it outperforms them but needs slightly more computational efforts.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"48 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75750667","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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