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Geert Leus - One of the best experts on this subject based on the ideXlab platform.
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Directional maximum likelihood self-estimation of the Path-Loss Exponent
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramér-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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directional maximum likelihood self estimation of the Path Loss Exponent
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramer-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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ICASSP - Directional maximum likelihood self-estimation of the Path-Loss Exponent
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramer-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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Self-Estimation of Path-Loss Exponent in Wireless Networks and Applications
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is one of the most crucial parameters in wireless communications to characterize the propagation of fading channels. It is currently adopted for many different kinds of wireless network problems such as power consumption issues, modeling the communication environment, and received signal strength (RSS)-based localization. PLE estimation is thus of great use to assist in wireless networking. However, a majority of methods to estimate the PLE require either some particular information of the wireless network, which might be unknown, or some external auxiliary devices, such as anchor nodes or the Global Positioning System. Moreover, this external information might sometimes be unreliable, spoofed, or difficult to obtain. Therefore, a self-estimator for the PLE, which is able to work independently, becomes an urgent demand to robustly and securely get a grip on the PLE for various wireless network applications.
Thomas Kaiser - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A hybrid SS-TOA wireless geolocation based on Path attenuation under imperfect Path Loss Exponent
2010Co-Authors: Maria Isabel Valera Martínez, Bamrung Tau Sieskul, Feng Zheng, Thomas KaiserAbstract:We consider the wireless geolocation using the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the Path Loss Exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator under the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numerical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the imperfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML estimator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outperform the ML estimator under the imperfect PLE.
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A hybrid SS-TOA wireless geolocation based on Path attenuation under imperfect Path Loss Exponent
2010 18th European Signal Processing Conference, 2010Co-Authors: Maria Isabel Valera Martínez, Bamrung Tau Sieskul, Feng Zheng, Thomas KaiserAbstract:We consider the wireless geolocation using the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the Path Loss Exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator under the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numerical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the imperfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML estimator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outperform the ML estimator under the imperfect PLE.
A. C. M. Fong - One of the best experts on this subject based on the ideXlab platform.
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variable elasticity spring relaxation improving the accuracy of localization for wsns with unknown Path Loss Exponent
Ubiquitous Computing, 2012Co-Authors: Qing Zhang, Boon-chong Seet, A. C. M. FongAbstract:Wireless sensor network is a key enabling technology for Ambient Intelligence, where location information is crucial for many applications. RSS-based ranging localization takes advantage of its low cost and low complexity, but it has an infeasible assumption of an accurate Path Loss Exponent of the physical environment. In this paper, we study the impact of Path Loss Exponent accuracy on the localization accuracy. We formulate the relationship between the Path Loss Exponent estimate and localization error, and found the localization error of Exponential order which we call the error magnification effect. By our in-depth investigation, we propose a passive and an active measures to suppress the error magnification effect, where the passive measure stabilizes the localization error of the spring-relaxation algorithm (SR), and the active measure introduces variable elasticity into the SR algorithm to cancel off the Exponential ranging error. The combination of both measures forms our localization solution called variable elasticity spring-relaxation (VE-SR) localization. We conduct extensive simulation experiments to show the effectiveness of VE-SR in suppressing the error magnification effect in various experiment setup. For a wide variety of physical environments, VE-SR offers location estimation with an average accuracy of no more than 10% of transmission range.
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GLOBECOM - RSS Ranging Based Wi-Fi Localization for Unknown Path Loss Exponent
2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, 2011Co-Authors: Qing Zhang, Boon-chong Seet, A. C. M. FongAbstract:Localization of mobile phones is important to location-based mobile services, but achieving good location estimation of mobile phones is difficult especially in environment whose Path Loss Exponent is unknown. In this paper, we present a Wi-Fi localization solution specifically designed for dense WLANs with unknown Path Loss Exponent. In order to leverage between the computational cost and localization accuracy, our solution establishes a neighbor selection scheme based on the Voronoi diagram to identify a subset of Access Points (APs) to participate in localization. It considers the identified subset of APs and a mobile phone to be located as a mass-spring system. Provided with information of known coordinates of APs, the solution estimates the Path Loss Exponent of the physical environment, infers inter-distances between APs and the mobile phone from Wi-Fi signals received, and implements spring relaxation algorithm to approximate the geographical location of the mobile phone, where this location estimation is fed back to refine the estimated Exponent iteratively. Extensive simulation results confirm that our solution is able to provide location estimation with an attractive average accuracy of below 2 m in a typical Wi-Fi setup.
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RSS Ranging Based Wi-Fi Localization for Unknown Path Loss Exponent
2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, 2011Co-Authors: Qing Zhang, Boon-chong Seet, A. C. M. FongAbstract:Localization of mobile phones is important to location-based mobile services, but achieving good location estimation of mobile phones is difficult especially in environment whose Path Loss Exponent is unknown. In this paper, we present a Wi-Fi localization solution specifically designed for dense WLANs with unknown Path Loss Exponent. In order to leverage between the computational cost and localization accuracy, our solution establishes a neighbor selection scheme based on the Voronoi diagram to identify a subset of Access Points (APs) to participate in localization. It considers the identified subset of APs and a mobile phone to be located as a mass-spring system. Provided with information of known coordinates of APs, the solution estimates the Path Loss Exponent of the physical environment, infers inter-distances between APs and the mobile phone from Wi-Fi signals received, and implements spring relaxation algorithm to approximate the geographical location of the mobile phone, where this location estimation is fed back to refine the estimated Exponent iteratively. Extensive simulation results confirm that our solution is able to provide location estimation with an attractive average accuracy of below 2 m in a typical Wi-Fi setup.
S. W. Ting - One of the best experts on this subject based on the ideXlab platform.
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The physiscs of mobile wireless communication explained through an electromagnetic macro model
Proceedings of 2014 3rd Asia-Pacific Conference on Antennas and Propagation, 2014Co-Authors: T. K. Sarkar, W. Dyab, M. V. S. N. Prasad, Mohammad N. Abdallah, Magdalena Salazar-palma, S. W. TingAbstract:The objective of this paper is to illustrate the physics associated with the propagation of mobile wireless communication signals. It is shown that an electromagnetic macro model can predict the nature of the Path Loss Exponent in a mobile cellular wireless communication without the use of a statistical model which is devoid of basic physics. Hence this paper makes it possible to analyze propagation of wireless signals in any environment without introducing an adhoc reference distance in the model. Invariably, the reference distance chosen for the model is incorrect as the cell is located in the near field as opposed to be in the far field. In addition, the use of a two ray model provides a Path Loss Exponent of 4 and not 3 which is the actual value within a cell as first described by Okumura in his classic paper. Specifically, we illustrate that the Path Loss Exponent in a cellular wireless communication is three preceded by a slow fading region and followed by the fringe region where the Path Loss Exponent is four. The size of these regions is determined on the heights of the base station transmitting antennas and the receiving antennas. These principles are illustrated by using the analysis of radiation from a vertical electric dipole situated over a horizontal imperfect ground plane which was first presented by Sommerfeld in 1909. When the Sommerfeld integrals are evaluated using a modified saddle point method for field points moderate to far distances away from the source point, through moderate to large values of the numerical distance, the correct Path Loss Exponents can be obtained. Okumura's experimental data are analyzed using the Sommerfeld formulation.
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Dynamic electromagnetic macro modeling of environment to deal with propagation in cellular wireless communication: Theory and experiment
2013Co-Authors: T. K. Sarkar, W. Dyab, M. V. S. N. Prasad, M. Salazar Palma, S. W. TingAbstract:The objective of this paper is to illustrate that an electromagnetic macro modeling can properly predict the Path Loss Exponent in a mobile cellular wireless communication.
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Electromagnetic macro modeling of propagation in mobile wireless communication: theory and experiment
2012 IEEE International Conference on Wireless Information Technology and Systems (ICWITS), 2012Co-Authors: T. K. Sarkar, W. Dyab, Salazar M. Palma, M. V. S. N. Prasad, S. W. TingAbstract:The objective of this paper is to illustrate that an electromagnetic macro modeling can properly predict the Path Loss Exponent in a mobile cellular wireless communication [1]. Specifically, we illustrate that the Path Loss Exponent in a cellular wireless communication is three preceded by a slow fading region and followed by the fringe region where the Path Loss Exponent is four. Theoretically this will be illustrated through the analysis of radiation from a vertical electric dipole situated over a horizontal imperfect ground plane as first considered by Sommerfeld in 1909 [2,3]. To start with, the exact analysis of radiation from the dipole is made using the Sommerfeld formulation. The semi-infinite integrals encountered in this formulation are evaluated using a modified saddle point method for field points moderate to far distances away from the source point to predict the appropriate Path Loss Exponents. The reflection coefficient method can also be derived by applying a saddle point method to the semi-infinite integrals and it is shown not to provide the correct Path Loss Exponent. The various approximations used to evaluate the Sommerfeld integrals are described for different regions [3]. It is also important to note that Sommerfeld's original 1909 paper had no error in sign [1]. However, Sommerfeld overlooked the properties associated with the pole. Both accurate numerical analyses along with experimental data are provided to illustrate the above statements. Both Okumura's experimental data [4,5] and experimental data taken from different base stations in urban environments [6-8] at two different frequencies will validate the theory. Experimental data reveal that a macro modeling of the environment using an appropriate electromagnetic analysis can accurately predict the Path Loss Exponent for the propagation of radio waves in a cellular wireless communication scenario.
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Electromagnetic Macro Modeling of Propagation in Mobile Wireless Communication: Theory and Experiment
IEEE Antennas and Propagation Magazine, 2012Co-Authors: T. K. Sarkar, W. Dyab, M. V. S. N. Prasad, S. W. Ting, Mohammad N. Abdallah, Magdalena Salazar-palma, Silvio BarbinAbstract:The objective of this paper is to illustrate that electromagnetic macro modeling can properly predict the Path-Loss Exponent in mobile cellular wireless communication. This represents the variation of the Path Loss with distance from the base-station antenna. Specifically, we illustrate that the Path-Loss Exponent in cellular wireless communication is three, preceded by a slow-fading region, and followed by the fringe region, where the Path-Loss Exponent is four. The sizes of these regions are determined by the heights of the base-station transmitting antennas and the receiving antennas. Theoretically, this is illustrated through the analysis of radiation from a vertical electric dipole situated over a horizontal imperfect ground plane, as first considered by Sommerfeld in 1909. To start with, the exact analysis of radiation from the dipole is made using the Sommerfeld formulation. The semi-infinite integrals encountered in this formulation are evaluated using a modified saddle-point method for field points moderate to far distances away from the source point, to predict the appropriate Path-Loss Exponents. The reflection-coefficient method is also derived by applying a saddle-point method to the semi-infinite integrals, and this is shown to not provide the correct Path-Loss Exponent that matches measurements. The various approximations used to evaluate the Sommerfeld integrals are described for different regions. It is also important to note that Sommerfeld's original 1909 paper had no error in sign. However, Sommerfeld overlooked the properties associated with the so-called “surface-wave pole.” Both accurate numerical analyses, along with experimental data, are provided to illustrate the above statements. In addition, Okumura's experimental data, and extensive data taken from seven different base stations in urban environments at two different frequencies, validate the theory. Experimental data revealed that a macro modeling of the environment, using an appropriate electromagnetic analysis, can accurately predict the Path-Loss Exponent for the propagation of radio waves in a cellular wireless communication scenario.
Yongchang Hu - One of the best experts on this subject based on the ideXlab platform.
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Directional maximum likelihood self-estimation of the Path-Loss Exponent
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramér-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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directional maximum likelihood self estimation of the Path Loss Exponent
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramer-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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ICASSP - Directional maximum likelihood self-estimation of the Path-Loss Exponent
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramer-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric Path Loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.
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Self-Estimation of Path-Loss Exponent in Wireless Networks and Applications
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Yongchang Hu, Geert LeusAbstract:The Path-Loss Exponent (PLE) is one of the most crucial parameters in wireless communications to characterize the propagation of fading channels. It is currently adopted for many different kinds of wireless network problems such as power consumption issues, modeling the communication environment, and received signal strength (RSS)-based localization. PLE estimation is thus of great use to assist in wireless networking. However, a majority of methods to estimate the PLE require either some particular information of the wireless network, which might be unknown, or some external auxiliary devices, such as anchor nodes or the Global Positioning System. Moreover, this external information might sometimes be unreliable, spoofed, or difficult to obtain. Therefore, a self-estimator for the PLE, which is able to work independently, becomes an urgent demand to robustly and securely get a grip on the PLE for various wireless network applications.