Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646650
Guobao Lu, Qilong Zhang, Xin Zhang, Fei Shen, F. Qin
Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects, especially the non-stationary temporal fading effect. Received Signal Strength Indicator (RSSI) will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power, while Rician K factor consisted by both the specular and scattered power can be treated as a reliable metric. The traditional estimation approaches of K factor from modulated wireless signals have to be data aided. In this paper, we attempt to formalize the estimation of K factor as a problem of non-linear feature extraction directly from modulated I/Q samples, which can be achieved through a simple convolutional neural network with morphological pre-processing. The experiments over field measurements have demonstrated the possibility of this methodology.
无线网络吸引了各行各业越来越多的兴趣。然而，由于严重的多径衰落效应，特别是非平稳的时间衰落效应，无线工业网络的广泛应用仍然面临着业务不可靠的挑战。接收信号强度指标(Received Signal Strength Indicator, RSSI)仅是对反射功率的噪声估计，在没有散射功率的情况下无法准确描述链路质量，而由反射功率和散射功率共同组成的rick因子可以作为可靠的度量。传统的无线调制信号K因子估计方法需要数据辅助。在本文中，我们试图将K因子的估计形式化为直接从调制I/Q样本中提取非线性特征的问题，这可以通过一个简单的卷积神经网络和形态学预处理来实现。现场测量实验证明了这种方法的可行性。
{"title":"CNN BASED RICIAN K FACTOR ESTIMATION FOR NON-STATIONARY INDUSTRIAL FADING CHANNEL","authors":"Guobao Lu, Qilong Zhang, Xin Zhang, Fei Shen, F. Qin","doi":"10.1109/GlobalSIP.2018.8646650","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646650","url":null,"abstract":"Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects, especially the non-stationary temporal fading effect. Received Signal Strength Indicator (RSSI) will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power, while Rician K factor consisted by both the specular and scattered power can be treated as a reliable metric. The traditional estimation approaches of K factor from modulated wireless signals have to be data aided. In this paper, we attempt to formalize the estimation of K factor as a problem of non-linear feature extraction directly from modulated I/Q samples, which can be achieved through a simple convolutional neural network with morphological pre-processing. The experiments over field measurements have demonstrated the possibility of this methodology.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165737","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646330
Trang C. Mai, H. Ngo, T. Duong
In this paper, we investigate the impact of multiple-antenna deployment at access points (APs) and users on the performance of cell-free massive multiple-input multiple-output (MIMO). The transmission is done via time-division duplex (TDD) protocol. With this protocol, the channels are first estimated at each AP based on the received pilot signals in the training phase. Then these channel information will be used to decode the symbols before sending to all users. The simple and distributed conjugate beamforming technique is deployed. We derive a closed-form expression for the downlink spectral efficiency taking into account the imperfect channel state information (CSI), non-orthogonal pilots, and power control. This spectral efficiency can be achieved without the knowledge of instantaneous CSI at the users. In addition, the effects of the number antennas per APs and per users are analyzed in the case of using mutual orthogonal pilot sequences and data power control.
{"title":"CELL-FREE MASSIVE MIMO SYSTEMS WITH MULTI-ANTENNA USERS","authors":"Trang C. Mai, H. Ngo, T. Duong","doi":"10.1109/GlobalSIP.2018.8646330","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646330","url":null,"abstract":"In this paper, we investigate the impact of multiple-antenna deployment at access points (APs) and users on the performance of cell-free massive multiple-input multiple-output (MIMO). The transmission is done via time-division duplex (TDD) protocol. With this protocol, the channels are first estimated at each AP based on the received pilot signals in the training phase. Then these channel information will be used to decode the symbols before sending to all users. The simple and distributed conjugate beamforming technique is deployed. We derive a closed-form expression for the downlink spectral efficiency taking into account the imperfect channel state information (CSI), non-orthogonal pilots, and power control. This spectral efficiency can be achieved without the knowledge of instantaneous CSI at the users. In addition, the effects of the number antennas per APs and per users are analyzed in the case of using mutual orthogonal pilot sequences and data power control.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121036824","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646651
Pu Zhao, Kaidi Xu, Tianyun Zhang, M. Fardad, Yanzhi Wang, X. Lin
As deep learning penetrates into wide application domains, it is essential to evaluate the robustness of deep neural networks (DNNs) under adversarial attacks, especially for some security-critical applications. To better understand the security properties of DNNs, we propose a general framework for constructing adversarial examples, based on ADMM (Alternating Direction Method of Multipliers). This general framework can be adapted to implement L2 and L0 attacks with minor changes. Our ADMM attacks require less distortion for incorrect classification compared with C&W attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.
{"title":"Reinforced Adversarial Attacks on Deep Neural Networks Using ADMM","authors":"Pu Zhao, Kaidi Xu, Tianyun Zhang, M. Fardad, Yanzhi Wang, X. Lin","doi":"10.1109/GLOBALSIP.2018.8646651","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646651","url":null,"abstract":"As deep learning penetrates into wide application domains, it is essential to evaluate the robustness of deep neural networks (DNNs) under adversarial attacks, especially for some security-critical applications. To better understand the security properties of DNNs, we propose a general framework for constructing adversarial examples, based on ADMM (Alternating Direction Method of Multipliers). This general framework can be adapted to implement L2 and L0 attacks with minor changes. Our ADMM attacks require less distortion for incorrect classification compared with C&W attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of 3D sensors induced 3D computer vision research for many application areas including virtual reality, autonomous navigation and surveillance. Recently, different methods have been proposed for 3D object classification. Many of the existing 2D and 3D classification methods rely on convolutional neural networks (CNNs), which are very successful in extracting features from the data. However, CNNs cannot sufficiently address the spatial relationship between features due to the max-pooling layers, and they require vast amount of training data. In this paper, we propose a model architecture for 3D object classification, which is an extension of Capsule Networks (CapsNets) to 3D data. Our proposed architecture called 3D CapsNet, takes advantage of the fact that a CapsNet preserves the orientation and spatial relationship of the extracted features, and thus requires less data to train the network. We compare our approach with ShapeNet on the ModelNet database, and show that our method provides performance improvement especially when training data size gets smaller.
{"title":"Object Classification from 3D Volumetric Data with 3D Capsule Networks","authors":"Burak Kakillioglu, Ayesha Ahmad, Senem Velipasalar","doi":"10.1109/GlobalSIP.2018.8646333","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646333","url":null,"abstract":"The proliferation of 3D sensors induced 3D computer vision research for many application areas including virtual reality, autonomous navigation and surveillance. Recently, different methods have been proposed for 3D object classification. Many of the existing 2D and 3D classification methods rely on convolutional neural networks (CNNs), which are very successful in extracting features from the data. However, CNNs cannot sufficiently address the spatial relationship between features due to the max-pooling layers, and they require vast amount of training data. In this paper, we propose a model architecture for 3D object classification, which is an extension of Capsule Networks (CapsNets) to 3D data. Our proposed architecture called 3D CapsNet, takes advantage of the fact that a CapsNet preserves the orientation and spatial relationship of the extracted features, and thus requires less data to train the network. We compare our approach with ShapeNet on the ModelNet database, and show that our method provides performance improvement especially when training data size gets smaller.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116226960","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646567
Ding Xiang, Ermin Wei
As opposed to the traditional supply-follow-demand approach, demand response is seen as an effective solution to improve efficiency of electricity system. In demand response, dynamic pricing schemes are believed to have significant potential to fully exploit the flexibility of shiftable energy consumptions. Most existing work on dynamic pricing schemes, however, falls short on consideration of price discrimination over different types of consumer groups. In this work, we propose a bilevel game theoretical Stackelberg model between a price-making utility company (a leader) and price-taking consumer groups (followers) in a discriminated dynamic pricing environment. We show under price discrimination producer surplus is monotonically increasing as energy consumption capacity of consumer groups increases. Numerical simulation validates our theoretical analysis and also shows that without price discrimination the social welfare may decrease against the energy consumption capacity of consumer groups. Moreover, social welfare can be higher under price discrimination.
{"title":"DYNAMIC PRICE DISCRIMINATION IN DEMAND RESPONSE MARKET: A BILEVEL GAME THEORETICAL MODEL","authors":"Ding Xiang, Ermin Wei","doi":"10.1109/GlobalSIP.2018.8646567","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646567","url":null,"abstract":"As opposed to the traditional supply-follow-demand approach, demand response is seen as an effective solution to improve efficiency of electricity system. In demand response, dynamic pricing schemes are believed to have significant potential to fully exploit the flexibility of shiftable energy consumptions. Most existing work on dynamic pricing schemes, however, falls short on consideration of price discrimination over different types of consumer groups. In this work, we propose a bilevel game theoretical Stackelberg model between a price-making utility company (a leader) and price-taking consumer groups (followers) in a discriminated dynamic pricing environment. We show under price discrimination producer surplus is monotonically increasing as energy consumption capacity of consumer groups increases. Numerical simulation validates our theoretical analysis and also shows that without price discrimination the social welfare may decrease against the energy consumption capacity of consumer groups. Moreover, social welfare can be higher under price discrimination.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116907595","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646489
Daniyal Amir Awan, R. Cavalcante, Z. Utkovski, S. Stańczak
Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.
{"title":"SET-THEORETIC LEARNING FOR DETECTION IN CELL-LESS C-RAN SYSTEMS","authors":"Daniyal Amir Awan, R. Cavalcante, Z. Utkovski, S. Stańczak","doi":"10.1109/GLOBALSIP.2018.8646489","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646489","url":null,"abstract":"Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091200","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646634
Hanwook Chung, É. Plourde, B. Champagne
In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a Bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabilistic generative model (PGM) of MNMF, specified by Poisson-distributed latent variables and gamma-distributed priors. In the training stage, the MNMF parameters of the speech and noise sources are estimated via the variational Bayesian expectation-maximization (VBEM) algorithm. In the enhancement stage, the clean speech signal is estimated via the MNMF-based minimum variance distortionless response (MVDR) beamformer. To further improve the enhanced speech quality, we efficiently combine the MNMF-based beamforming technique with a classical unsupervised single-channel enhancement method. Experiments show that the proposed method can provide better enhancement performance than the selected benchmarks.
{"title":"A SUPERVISED MULTI-CHANNEL SPEECH ENHANCEMENT ALGORITHM BASED ON BAYESIAN NMF MODEL","authors":"Hanwook Chung, É. Plourde, B. Champagne","doi":"10.1109/GlobalSIP.2018.8646634","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646634","url":null,"abstract":"In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a Bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabilistic generative model (PGM) of MNMF, specified by Poisson-distributed latent variables and gamma-distributed priors. In the training stage, the MNMF parameters of the speech and noise sources are estimated via the variational Bayesian expectation-maximization (VBEM) algorithm. In the enhancement stage, the clean speech signal is estimated via the MNMF-based minimum variance distortionless response (MVDR) beamformer. To further improve the enhanced speech quality, we efficiently combine the MNMF-based beamforming technique with a classical unsupervised single-channel enhancement method. Experiments show that the proposed method can provide better enhancement performance than the selected benchmarks.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120833232","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646443
Sifat Shahriar Khan, Jin Wei
With the omnipresence of big data, social sensing has become a valuable technique for information retrieval and event detection. In recent years, extensive research has been conducted on using social sensing as a platform to detect critical events and emergency situations such as natural disasters, criminal activities, and power outages. In this paper, we focus on detecting real-time power outages using social sensing by investigating different predictive models, preprocessing techniques and feature extraction methods. The investigation shows that multi-layer perception neural network outperforms other popular predictive models. The paper proposes a real-time situational-awareness mechanism to detect the ongoing power outages and extract useful information for power outage management. In the proposed framework, for temporal analysis, a modified approach of Kleinberg’s burst detection algorithm is proposed to ensure the prompt detection of power outages. This study paves the way for future investigation and innovation in efficient real-time event detection using social sensing.
{"title":"Real-Time Power Outage Detection System using Social Sensing and Neural Networks","authors":"Sifat Shahriar Khan, Jin Wei","doi":"10.1109/GLOBALSIP.2018.8646443","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646443","url":null,"abstract":"With the omnipresence of big data, social sensing has become a valuable technique for information retrieval and event detection. In recent years, extensive research has been conducted on using social sensing as a platform to detect critical events and emergency situations such as natural disasters, criminal activities, and power outages. In this paper, we focus on detecting real-time power outages using social sensing by investigating different predictive models, preprocessing techniques and feature extraction methods. The investigation shows that multi-layer perception neural network outperforms other popular predictive models. The paper proposes a real-time situational-awareness mechanism to detect the ongoing power outages and extract useful information for power outage management. In the proposed framework, for temporal analysis, a modified approach of Kleinberg’s burst detection algorithm is proposed to ensure the prompt detection of power outages. This study paves the way for future investigation and innovation in efficient real-time event detection using social sensing.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296141","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646358
N. Cassiau, L. Maret, Jean-Baptiste Doré, V. Savin, D. Kténas
The performance, error rate and synchronization, of recently released 5G New Radio (NR) physical layer (PHY) with typical satellite scenarios are assessed in this paper. Four propagation channels in Ka band are considered and implementation constraints are modeled. The conclusions highly depend on the channel type. For open rural and high speed train (300 km/h) scenarios, 5G NR PHY may be used as is. Higher speed scenarios (aero 1000 km/h) can benefit from the 5G NR mode that allows very short symbols (although this mode is only allowed for large band). Finally, we demonstrate that amendments should be considered in the standard for supporting 2-state channels (suburban for example), due to the long fading periods.
对最新发布的5G新空口物理层(PHY)在典型卫星场景下的性能、错误率和同步性进行了评估。考虑了Ka波段的四种传播通道，并对实现约束进行了建模。结论高度依赖于通道类型。对于开放的农村和高速列车(300公里/小时)场景，5G NR PHY可以原样使用。更高速度的场景(航空1000公里/小时)可以从5G NR模式中受益，该模式允许非常短的符号(尽管该模式只允许大频段)。最后，我们证明，由于长衰落周期，应该考虑在支持两状态信道(例如郊区)的标准中进行修订。
{"title":"Assessment of 5G NR Physical Layer for Future Satellite Networks","authors":"N. Cassiau, L. Maret, Jean-Baptiste Doré, V. Savin, D. Kténas","doi":"10.1109/GlobalSIP.2018.8646358","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646358","url":null,"abstract":"The performance, error rate and synchronization, of recently released 5G New Radio (NR) physical layer (PHY) with typical satellite scenarios are assessed in this paper. Four propagation channels in Ka band are considered and implementation constraints are modeled. The conclusions highly depend on the channel type. For open rural and high speed train (300 km/h) scenarios, 5G NR PHY may be used as is. Higher speed scenarios (aero 1000 km/h) can benefit from the 5G NR mode that allows very short symbols (although this mode is only allowed for large band). Finally, we demonstrate that amendments should be considered in the standard for supporting 2-state channels (suburban for example), due to the long fading periods.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128348029","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646532
M. Khatun, H. Mehrpouyan, D. Matolak
This paper presents a large scale fading channel model at the 60 GHz band. This model is based on the measurement campaign that our team conducted at Boise Airport and Boise State University. The close-in reference path loss and floating-intercept path loss models with both statistical and stochastic approaches are investigated for these environments. The measurements were collected at several different locations in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios using high gain directional antenna. The path loss exponent and shadowing factor are determined based on the measurement results and compared with recent work at this frequency. Both the stochastic gradient descent algorithm and the statistical least-square technique are used to analyze the floating-intercept path loss model. The results show that the path loss exponents in the outdoor scenarios are higher than the indoor environment due the RF noise caused by the sunny and dry climate in the Boise area. Finally, a good agreement is found between the measurement results and the prior work results in presented in the literature.
{"title":"60-GHz Millimeter-Wave Pathloss Measurements in Boise Airport","authors":"M. Khatun, H. Mehrpouyan, D. Matolak","doi":"10.1109/GlobalSIP.2018.8646532","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646532","url":null,"abstract":"This paper presents a large scale fading channel model at the 60 GHz band. This model is based on the measurement campaign that our team conducted at Boise Airport and Boise State University. The close-in reference path loss and floating-intercept path loss models with both statistical and stochastic approaches are investigated for these environments. The measurements were collected at several different locations in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios using high gain directional antenna. The path loss exponent and shadowing factor are determined based on the measurement results and compared with recent work at this frequency. Both the stochastic gradient descent algorithm and the statistical least-square technique are used to analyze the floating-intercept path loss model. The results show that the path loss exponents in the outdoor scenarios are higher than the indoor environment due the RF noise caused by the sunny and dry climate in the Boise area. Finally, a good agreement is found between the measurement results and the prior work results in presented in the literature.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129023889","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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