Cram&amp;#x00E9;r-Rao lower bound for breakpoint distance estimation in a path-loss model

Castro-Arvizu, J. Manuel; Closas, Pau; Fernandez–Rubio, Juan A.

doi:10.1109/iccw.2014.6881192

Cited by 10 publications

(15 citation statements)

References 11 publications

(12 reference statements)

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“…As a result of these measurements, we obtained the curves shown in Figure 16, where the one labeled as "Real" denotes the empirical measurements taken at different distances. We selected the path-loss exponent ( ) to be 2.649 for distances less than the breakpoint ( bp ) [28,29] whose value was equal to 55 m, and, for distances greater than bp , the path-loss exponent was 4 dB. We set the maximum communication range to = 63 m. For these experiments, the maximum speed (i.e., max ) was set to 2 m/s.…”

Section: Testbed Experimentsmentioning

confidence: 99%

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

García¹,

Gómez²,

Lopez‐Guerrero³

et al. 2017

Mobile Information Systems

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Location privacy in wireless networks is nowadays a major concern. This is due to the fact that the mere fact of transmitting may allow a network to pinpoint a mobile node. We consider that a first step to protect a mobile node in this situation is to provide it with the means to quantify how accurately a network establishes its position. To achieve this end, we introduce the location-exposure algorithm (LEA), which runs on the mobile terminal only and whose operation consists of two steps. In the first step, LEA discovers the positions of nearby network nodes and uses this information to emulate how they estimate the position of the mobile node. In the second step, it quantifies the level of exposure by computing the distance between the position estimated in the first step and its true position. We refer to these steps as alocation-exposureproblem. We tested our proposal with simulations and testbed experiments. These results show the ability of LEA to reproduce the location of the mobile node, as seen by the network, and to quantify the level of exposure. This knowledge can help the mobile user decide which actions should be performed before transmitting.

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Section: Testbed Experimentsmentioning

confidence: 99%

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

García¹,

Gómez²,

Lopez‐Guerrero³

et al. 2017

Mobile Information Systems

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“…The Euclidean distance which can be calculated as shown in the below equation is used to measure the distance between an online RSS value and the offline training database RSS records [12]. The smallest distance is used to decide which offline fingerprint is selected to determine the location of the user.…”

Section: Rssmentioning

confidence: 99%

Survey of Indoor Positioning Measurements, Methods and Techniques

Doiphode¹,

Bakal²,

Gedam³

2016

IJCA

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The continuous improvement in the development in the field of communication networks and mobile computing, location based service (LBS) has become very popular in recent years. For outdoor positioning service the Global Positioning Systems (GPS) are the earliest widely used modern systems. In GPS technology Satellite signals cannot penetrate in indoor environment since they are blocked by building obstructions thus satellite signal cannot provide good accuracy in indoor environments due to lack of LoS(Line Of Sight). On doing research it is being observed that in the near future, indoor positioning will gain more demand as people spend more than 70% of their lives in indoor environment. A large variety of technologies have been designed for dealing with the problem since the indoor environments are very difficult to track .The paper also provide brief description on various indoor wireless tracking measurements, methodologies and technologies. The paper illustrates the theoretical points, the main tools, and the most promising technologies for indoor tracking infrastructure.

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“…In addition of TOA, LE can also be achieved by using RSS or power of arrival (POA) [12], [13]. By using the measured power, we can estimate the distance between transmitter and receiver with the assistance of path-loss model.…”

Section: Introductionmentioning

confidence: 99%

“…However, our results show that such LE method will perform poorly when there are only 2 reliable BSs and the UE is located near the straight line that connects the 2 BSs. Meanwhile, in the case of an RSS-based LE [12], [13], the method may perform poorly due to signal attenuation, noise, interference and multipath channels [3]. Hence, in this paper, we take advantage of both of the LE methods by designing an algorithm that can detect the reliability of angle-based LE and switch to power-based LE under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

Localization Using Joint AOD and RSS Method in Massive MIMO System

Mahyiddin¹,

Mazuki²,

Dimyati³

et al. 2019

RADIOENGINEERING

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In this research, we propose to estimate location of mobile users by using joint angle of departure (AOD) from base station (BS) and received signal strength (RSS) at user equipment (UE) method. The location estimation (LE) is performed at each UE by using massive multipleinput multiple-output (MIMO) system at the BS, which transmits specially design MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) beamforming signals. Since the estimation is done independently at each UE, the number of UEs in the area does not affect the performance and calculation complexity of the LE. To improve the practicality of the proposed method, we also design the beamforming signals with reduced peak to average power ratio (PAPR) by using random subcarrier-beamforming angle allocation method. The results show that the proposed method generally has lower estimation error than that of the AOD-only and the RSS-only LE methods across various UEs' locations. The proposed beamforming signal method can also significantly reduce PAPR of the transmitted signal.

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Cramér-Rao lower bound for breakpoint distance estimation in a path-loss model

Cited by 10 publications

References 11 publications

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

Survey of Indoor Positioning Measurements, Methods and Techniques

Localization Using Joint AOD and RSS Method in Massive MIMO System

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