Pub Date : 2020-08-20DOI: 10.36227/techrxiv.16434693
Ching-Lun Tai, D. Wu
With higher frequencies and broader spectrum than conventional frequency bands, the millimeter-wave (mmWave) band is suitable for next-generation wireless networks featuring short-distance high-rate communications. As a newcomer, mmWaves are expected to have the backward compatibility with existing services and collaborate with other technologies in order to enhance system performances. Therefore, the coexistence issues become an essential topic for next-generation wireless communications. In this paper, we systematically review the coexistence issues of broadband mmWave communications and their corresponding solutions proposed in the literature, helping shed light on the insights of the mmWave design. Particularly, the works surveyed in this paper can be classified into four categories: coexistence with microwave communications, coexistence with fixed services, coexistence with non-orthogonal multiple access (NOMA), and other coexistence issues. Results of numerical evaluations inspired by the literature are presented for a deeper analysis. We also point out some challenges and future directions for each category as a roadmap to further investigate the coexistence issues of broadband mmWave communications.
{"title":"A Review on Coexistence Issues of Broadband Millimeter-Wave Communications","authors":"Ching-Lun Tai, D. Wu","doi":"10.36227/techrxiv.16434693","DOIUrl":"https://doi.org/10.36227/techrxiv.16434693","url":null,"abstract":"With higher frequencies and broader spectrum than conventional frequency bands, the millimeter-wave (mmWave) band is suitable for next-generation wireless networks featuring short-distance high-rate communications. As a newcomer, mmWaves are expected to have the backward compatibility with existing services and collaborate with other technologies in order to enhance system performances. Therefore, the coexistence issues become an essential topic for next-generation wireless communications. In this paper, we systematically review the coexistence issues of broadband mmWave communications and their corresponding solutions proposed in the literature, helping shed light on the insights of the mmWave design. Particularly, the works surveyed in this paper can be classified into four categories: coexistence with microwave communications, coexistence with fixed services, coexistence with non-orthogonal multiple access (NOMA), and other coexistence issues. Results of numerical evaluations inspired by the literature are presented for a deeper analysis. We also point out some challenges and future directions for each category as a roadmap to further investigate the coexistence issues of broadband mmWave communications.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79027761","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 : 2020-08-05DOI: 10.1007/978-3-030-72777-2_10
O. Z. Alsulami, T. El-Gorashi, J. Elmirghani
{"title":"Resource Allocation in 6G Optical Wireless Systems","authors":"O. Z. Alsulami, T. El-Gorashi, J. Elmirghani","doi":"10.1007/978-3-030-72777-2_10","DOIUrl":"https://doi.org/10.1007/978-3-030-72777-2_10","url":null,"abstract":"","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88913950","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 : 2020-08-05DOI: 10.1587/transinf.2018EDP7435
T. Sakamoto
This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.
{"title":"Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network","authors":"T. Sakamoto","doi":"10.1587/transinf.2018EDP7435","DOIUrl":"https://doi.org/10.1587/transinf.2018EDP7435","url":null,"abstract":"This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73135223","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 : 2020-07-21DOI: 10.32603/1993-8985-2020-23-3-6-24
D. Nicolsky, G. Tipenko
We present a new approach to solve the exponential retrieval problem. We derive a stable technique, based on the singular value decomposition (SVD) of lag-covariance and crosscovariance matrices consisting of covariance coefficients computed for index translated copies of an initial time series. For these matrices a generalized eigenvalue problem is solved. The initial signal is mapped into the basis of the generalized eigenvectors and phase portraits are consequently analyzed. Pattern recognition techniques could be applied to distinguish phase portraits related to the exponentials and noise. Each frequency is evaluated by unwrapping phases of the corresponding portrait, detecting potential wrapping events and estimation of the phase slope. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise.
{"title":"Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem","authors":"D. Nicolsky, G. Tipenko","doi":"10.32603/1993-8985-2020-23-3-6-24","DOIUrl":"https://doi.org/10.32603/1993-8985-2020-23-3-6-24","url":null,"abstract":"We present a new approach to solve the exponential retrieval problem. We derive a stable technique, based on the singular value decomposition (SVD) of lag-covariance and crosscovariance matrices consisting of covariance coefficients computed for index translated copies of an initial time series. For these matrices a generalized eigenvalue problem is solved. The initial signal is mapped into the basis of the generalized eigenvectors and phase portraits are consequently analyzed. Pattern recognition techniques could be applied to distinguish phase portraits related to the exponentials and noise. Each frequency is evaluated by unwrapping phases of the corresponding portrait, detecting potential wrapping events and estimation of the phase slope. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77615080","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 : 2020-06-08DOI: 10.1364/SPPCOM.2018.SpTh4F.3
A. Dochhan, Nicklas Eiselt, J. Zou, H. Griesser, M. Eiselt, J. Elbers
We report on 112 Gbit/s real-time DMT transmission over up to 60 km, targeted at DCI applications. Chromatic dispersion mitigation by vestigial sideband filtering is compared to the use of dispersion compensating fiber.
{"title":"Real-time 112 Gbit/s DMT for Data Center Interconnects","authors":"A. Dochhan, Nicklas Eiselt, J. Zou, H. Griesser, M. Eiselt, J. Elbers","doi":"10.1364/SPPCOM.2018.SpTh4F.3","DOIUrl":"https://doi.org/10.1364/SPPCOM.2018.SpTh4F.3","url":null,"abstract":"We report on 112 Gbit/s real-time DMT transmission over up to 60 km, targeted at DCI applications. Chromatic dispersion mitigation by vestigial sideband filtering is compared to the use of dispersion compensating fiber.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86546087","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 : 2020-05-26DOI: 10.36227/techrxiv.12369152
Gaofeng Pan, Jia Ye, Jianping An, Mohamed-Slim Alouini
Full-duplex (FD) transmission has already been regarded and developed as a promising method to improve the utilization efficiency of the limited spectrum resource, as transmitting and receiving are allowed to simultaneously occur on the same frequency band. Nowadays, benefiting from the recent development of intelligent reflecting surface (IRS), some unique electromagnetic (EM) functionalities, like wavefront shaping, focusing, anomalous reflection, absorption, frequency shifting, and nonreciprocity can be realized by soft-controlled elements at the IRS, showing the capability of reconfiguring the wireless propagation environment with no hardware cost and nearly zero energy consumption. To jointly exploit the virtues of both FD transmission and IRS, in this article we first introduce several EM functionalities of IRS that are profitable for FD transmission; then, some designs of FD-enabled IRS systems are proposed and discussed, followed by numerical results to demonstrate the obtained benefits. Finally, the challenges and open problems of realizing FD-enabled IRS systems are outlined and elaborated upon.
{"title":"When Full-Duplex Transmission Meets Intelligent Reflecting Surface: Opportunities and Challenges","authors":"Gaofeng Pan, Jia Ye, Jianping An, Mohamed-Slim Alouini","doi":"10.36227/techrxiv.12369152","DOIUrl":"https://doi.org/10.36227/techrxiv.12369152","url":null,"abstract":"Full-duplex (FD) transmission has already been regarded and developed as a promising method to improve the utilization efficiency of the limited spectrum resource, as transmitting and receiving are allowed to simultaneously occur on the same frequency band. Nowadays, benefiting from the recent development of intelligent reflecting surface (IRS), some unique electromagnetic (EM) functionalities, like wavefront shaping, focusing, anomalous reflection, absorption, frequency shifting, and nonreciprocity can be realized by soft-controlled elements at the IRS, showing the capability of reconfiguring the wireless propagation environment with no hardware cost and nearly zero energy consumption. To jointly exploit the virtues of both FD transmission and IRS, in this article we first introduce several EM functionalities of IRS that are profitable for FD transmission; then, some designs of FD-enabled IRS systems are proposed and discussed, followed by numerical results to demonstrate the obtained benefits. Finally, the challenges and open problems of realizing FD-enabled IRS systems are outlined and elaborated upon.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88493390","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}
Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.
{"title":"Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment","authors":"Seonwoo Lee, HyeonTak Yu, HoJun Yang, Inseo Song, JaeHeung Yang, Gang-Min Lim, Kyusung Kim, Byeong-Keun Choi, Jangwoo Kwon","doi":"10.20944/preprints202012.0646.v1","DOIUrl":"https://doi.org/10.20944/preprints202012.0646.v1","url":null,"abstract":"Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90120195","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}
Markus Schartel, Ralf Burr, Rik Bahnemann, W. Mayer, C. Waldschmidt
Many countries in the world are contaminated with landmines. Several thousand casualties occur every year. Although there are certain types of mines that can be detected from a safe stand-off position with tools, humanitarian demining is still mostly done by hand. As a new approach, an unmanned aerial system (UAS) equipped with a ground penetrating synthetic aperture radar (GPSAR) was developed, which is used to detect landmines, cluster munition, grenades, and improvised explosive devices (IEDs). The measurement system consists of a multicopter, a total station, an inertial measurement unit (IMU), and a frequency-modulated continuous-wave (FMCW) radar operating from 1 GHz to 4 GHz. The highly accurate localization of the measurement system and the full flexibility of the UAS are used to generate 3D-repeat-pass circular SAR images of buried antipersonnel landmines. In order to demonstrate the functionality of the system, 15 different dummy landmines were buried in a sandbox. The measurement results show the high potential of circular SAR for the detection of minimum metal mines. 11 out of 15 different test objects could be detected unambiguously with cm-level accuracy by examining depth profiles showing the amplitude of the targets response over the processing depth.
{"title":"An experimental study on airborne landmine detection using a circular synthetic aperture radar","authors":"Markus Schartel, Ralf Burr, Rik Bahnemann, W. Mayer, C. Waldschmidt","doi":"10.18725/OPARU-31981","DOIUrl":"https://doi.org/10.18725/OPARU-31981","url":null,"abstract":"Many countries in the world are contaminated with landmines. Several thousand casualties occur every year. Although there are certain types of mines that can be detected from a safe stand-off position with tools, humanitarian demining is still mostly done by hand. As a new approach, an unmanned aerial system (UAS) equipped with a ground penetrating synthetic aperture radar (GPSAR) was developed, which is used to detect landmines, cluster munition, grenades, and improvised explosive devices (IEDs). The measurement system consists of a multicopter, a total station, an inertial measurement unit (IMU), and a frequency-modulated continuous-wave (FMCW) radar operating from 1 GHz to 4 GHz. The highly accurate localization of the measurement system and the full flexibility of the UAS are used to generate 3D-repeat-pass circular SAR images of buried antipersonnel landmines. In order to demonstrate the functionality of the system, 15 different dummy landmines were buried in a sandbox. The measurement results show the high potential of circular SAR for the detection of minimum metal mines. 11 out of 15 different test objects could be detected unambiguously with cm-level accuracy by examining depth profiles showing the amplitude of the targets response over the processing depth.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90729746","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 : 2020-04-25DOI: 10.1061/9780784482865.094
B. Sherafat, Abbas Rashidi, Siyuan Song
Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations, directions, and locations working simultaneously. The current state-of-research for recognizing activities of multiple machines on a job site is hardware-oriented, on the basis of using microphone arrays (i.e., several single microphones installed on a board under specific geometric layout) and beamforming principles for classifying sound directions for each machine. While effective, the common hardware-approach has limitations and using microphone arrays is not always a feasible option at ordinary job sites. In this paper, the authors proposed a software-oriented approach using Deep Neural Networks (DNNs) and Time-Frequency Masks (TFMs) to address this issue. The proposed method requires using single microphones, as the sound sources could be differentiated by training a DNN. The presented approach has been tested and validated under simulated job site conditions where two machines operated simultaneously. Results show that the average accuracy for soft TFM is 38% higher than binary TFM.
{"title":"A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines","authors":"B. Sherafat, Abbas Rashidi, Siyuan Song","doi":"10.1061/9780784482865.094","DOIUrl":"https://doi.org/10.1061/9780784482865.094","url":null,"abstract":"Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations, directions, and locations working simultaneously. The current state-of-research for recognizing activities of multiple machines on a job site is hardware-oriented, on the basis of using microphone arrays (i.e., several single microphones installed on a board under specific geometric layout) and beamforming principles for classifying sound directions for each machine. While effective, the common hardware-approach has limitations and using microphone arrays is not always a feasible option at ordinary job sites. In this paper, the authors proposed a software-oriented approach using Deep Neural Networks (DNNs) and Time-Frequency Masks (TFMs) to address this issue. The proposed method requires using single microphones, as the sound sources could be differentiated by training a DNN. The presented approach has been tested and validated under simulated job site conditions where two machines operated simultaneously. Results show that the average accuracy for soft TFM is 38% higher than binary TFM.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80021349","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}
Here we present an algorithm to procedurally remap spectral contents of natural signals. The algorithm takes in two inputs: a signal whose spectral component needs to be remapped and a warping or remapping function. The algorithm generates one output, which is a remapped version of the original signal. The input signal is remapped into the output signal in two steps. In the analysis step, the algorithm performs a series of operations to modify the spectral content, i.e., compute the warped phase of the signal according to the given remapping function. In the synthesis step, the modified spectral content is combined with the envelope information of the input signal to reconstruct the warped or remapped output signal.
{"title":"Spectral mapping of natural signals","authors":"Rahman Ms","doi":"10.31234/OSF.IO/8VQ3G","DOIUrl":"https://doi.org/10.31234/OSF.IO/8VQ3G","url":null,"abstract":"Here we present an algorithm to procedurally remap spectral contents of natural signals. The algorithm takes in two inputs: a signal whose spectral component needs to be remapped and a warping or remapping function. The algorithm generates one output, which is a remapped version of the original signal. The input signal is remapped into the output signal in two steps. In the analysis step, the algorithm performs a series of operations to modify the spectral content, i.e., compute the warped phase of the signal according to the given remapping function. In the synthesis step, the modified spectral content is combined with the envelope information of the input signal to reconstruct the warped or remapped output signal.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90937325","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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