A Deterministic approach to evaluate path loss exponents in large-scale outdoor 802.11 WLANs

Zhou, Ting; Sharif, Hamid; Hempel, Michael; Mahasukhon, Puttipong; Wang, Wei; Ma, Tao

doi:10.1109/lcn.2009.5355111

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…From this point of view, we supposed that signal conditions such as the orientation of the antenna and transmission power do not change and only environmental conditions constitute a challenge for distance estimation. In fact, we also assumed that we could reduce the possible variations to fewer than four for each type of field (cold/warm and wet/dry, similar to proposed in [19]), and for this purpose the first three experiments were carried out for static distances, along with two experiments on a moving target through a known path.…”

Section: Discussionmentioning

confidence: 99%

“…Fitting parameters change depending on the season and the scenery [19]. To fit both parameters, a linear approximation of both parameters can be done, converting Equation (1) into a strike line equation such as y = ax + b with the changes [20] expressed in Equation (2).…”

Section: Distance Estimation Modelmentioning

confidence: 99%

“…Given that the environment conditions (humidity, temperature, type of soil, and vegetation) is a key factor [22], some authors opted for featuring terrain depending on some of variables described above (e.g., Reference [19]). Other authors opted for building auto-corrective models in order to adapt distance calculation to environmental conditions (for example, in [4], variance is used for auto-adjusting), and others states that it is difficult to model a distance estimator using RSSI due to the intrinsic lack of distance information contained in it [13].…”

Section: Considering Uncertaintymentioning

confidence: 99%

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Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

et al. 2019

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In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana's National Park. As a supporting feature for that project, this work was implemented for locating animal's collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.Finite-difference time-domain [12], or Artificial Networks [13] are much better when estimating position in terms of accuracy than other models, but, as mentioned above, at the expense of a high computation cost [14]. Other models (e.g., Reference [15]) are well accepted for network deployment of cellular devices (≤1.5 GHz) but not suitable for higher frequencies.The log-normal model, together with the free-space model [16] are some of the most used models operating at 2.5 GHz for being the calculation procedure simple [4] (at distances greater than the length of the antenna and the wavelength) but at the expense of a lower accuracy. One alternative is 2RM (2-Ray Model) [4], which includes this length as one of the parameters to calculate two possible signal paths. An improved version of this model can be found at [17] but the improvements over the log-normal model are only present at d ≥ 4πH rx H tx , which in our case is more than 250 m (when setting the maximum height of the antennas that were used in this work at 160 cm). Further details about the position of the antenna can be found in Section 2.5.This work presents a relative distance estimator for Wireless Sensor Network devices operating at 2.5 GHz using the 802.15.4 standard [18], on which the target device is a wildlife tracking and behavior classifying device that is attached to an animal by means of a collar. The distance estimator is used to locate collars belonging to animals that are not moving, or collars that have been abandoned by the animals to which they belonged, or even collars that have gone outside of the coverage area of the WSN. On the other hand, the locating device (that uses the distance estimator algorithm) is used by an operator that moves overland with a WSN node that acts as a 802.15.4 wireless packet sniffer. To justify this development, the propagation model and filters used are shown together with two ways to deal with signal measurement variability and uncertainty. In Section 3, we present the data obtained fro...

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Section: Discussionmentioning

confidence: 99%

Section: Distance Estimation Modelmentioning

confidence: 99%

Section: Considering Uncertaintymentioning

confidence: 99%

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Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

et al. 2019

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“…The path loss exponent which shows the lossy nature of a particular propagation environment was computed from the measurement data for each of the areas considered. [23] presents an approach to finding the path loss exponent as shown in (21) = ∑ ( − ) * 10log ( ) =1 ∑ ( =1 10log ( )) 2…”

Section: Path Loss Exponentmentioning

confidence: 99%

“…Pl(dB) = EIRP(dBm)-Pr(dBm) The path loss is obtained by substituting the calculated value of EIRP (dBm) and the mean received power Pr (dBm) into equation (23).…”

Section: Path Loss Of Measured Datamentioning

confidence: 99%

Improved Propagation Models for LTE Path Loss Prediction in Urban & Suburban Ghana

Gadze¹,

Agyekum²,

Nuagah³

et al. 2019

IJWMN

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To maximize the benefits of LTE cellular networks, careful and proper planning is needed. This requires the use of accurate propagation models to quantify the path loss required for base station deployment. Deployed LTE networks in Ghana can barely meet the desired 100Mbps throughput leading to customer dissatisfaction. Network operators rely on transmission planning tools designed for generalized environments that come with already embedded propagation models suited to other environments. A challenge therefore to Ghanaian transmission Network planners will be choosing an accurate and precise propagation model that best suits the Ghanaian environment. Given this, extensive LTE path loss measurements at 800MHz and 2600MHz were taken in selected urban and suburban environments in Ghana and compared with 6 commonly used propagation models. Improved versions of the Ericson, SUI, and ECC-33 developed in this study predict more precisely the path loss in Ghanaian environments compared with commonly used propagation models.

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Rate Control for Multi-link and Multi-relay Wireless LANs Supporting Real-Time Mobile Data Transmissions

Ikeda

Omoto

Yomo

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Deterministic approach to evaluate path loss exponents in large-scale outdoor 802.11 WLANs

Cited by 14 publications

References 11 publications

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

Improved Propagation Models for LTE Path Loss Prediction in Urban & Suburban Ghana

Rate Control for Multi-link and Multi-relay Wireless LANs Supporting Real-Time Mobile Data Transmissions

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