Computer prediction of field strength in the planning of radio systems

Dadson, C. E.; Durkin, John; Martin, R.E.

doi:10.1109/t-vt.1975.23590

Cited by 24 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The probability of error q(z R ) when the transmission factor is z R is given by: (15) where u(z R ) and w(z R ) are defined via equation (12) with ρ taken to be the last component of z R and SNR taken to be the average SNR under the set of transmission factor z R . The likelihood ratio, the negative log likelihood ratio, and the combined objective function which is a combination of negative likelihood and RSSI estimation error are extended in analogy to equations (4), (7) and (8), respectively.…”

Section: Data-driven Approach With Rician Fadingmentioning

confidence: 99%

See 1 more Smart Citation

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Rathod

Subramanian²,

Sundaresan

2020

IEEE Access

View full text Add to dashboard Cite

This paper is on a coverage estimation procedure for the deployment of outdoor Internet of Things (IoT). In the first part of the paper, a data-driven coverage estimation technique is proposed. The estimation technique combines multiple machine-learning-based regression ideas. The proposed technique achieves two purposes. The first purpose is to reduce the bias in the estimated received signal strength arising from estimations performed only on the successfully received packets. The second purpose is to exploit commonality of physical parameters, e.g. antenna-gain, in measurements that are made across multiple propagation environments. It also provides the correct link function for performing a nonlinear regression in our communication systems context. In the second part of the paper, a method to use readily available geographic information system (GIS) data (for classifying geographic areas into various propagation environments) followed by an algorithm for estimating received signal strength (which is motivated by the first part of the paper) is proposed. Together they enable quick and automated estimation of coverage in outdoor environments. It is anticipated that these will lead to faster and more efficient deployment of outdoor Internet of Things. INDEX TERMS Coverage, geographic information system (GIS), heterogeneous propagation environment, Internet of Things (IoT) deployment. NIHESH RATHOD received the B.E. degree in electronics and communication from Dharmsinh Desai University, Nadiad, in 2012, and the M.E. degree in telecommunication from the Indian Institute of Science, Bengaluru, in 2015, where he is currently pursuing the Ph.D. degree in communication and networks. Since 2015, he has been a Cisco Research Scholar with the Department of Electrical Communication Engineering, Indian Institute of Science. His interest includes the areas in design and implementation of the Internet of Things (IoT). RENU SUBRAMANIAN received the B.Tech. degree in electrical and electronics from the Rajiv

show abstract

Section: Data-driven Approach With Rician Fadingmentioning

confidence: 99%

“…There are many classical outdoor propagation models, for example the Longley-Rice model [3]- [5] and the Edwards-Durkin model [6], [7]. These involve sophisticated knife-edge diffraction techniques to estimate path loss and require very detailed topography information.…”

Section: Introductionmentioning

confidence: 99%

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Rathod

Subramanian²,

Sundaresan

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When the target is located off the link, the RSSI node r receives not only from node t , but also from the target (the second transmitter), as shown in Figure 7. In this case, the RSSI measurement Rt,r,o sent by node t and received by node r can be calculated as where Pt, dt,r, and γt are the same as defined above, and Ss,r,o is the scattering gain defined in [22]: which is supplementary to Rt,r,o. The distance information can be calculated by (1) and (10) with the RSSI measurement.…”

Section: Link Sensing-adaptive Polmentioning

confidence: 99%

Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

Tong

Wang

Chang

et al. 2014

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The passive object localization (POL) problem in wireless sensor networks aims to determine the location of a target without any device attached for receiving or transmitting signal. This problem is challenging as there is very limited information available for deriving the target location. By combining the diffraction and scattering models, we propose a link sensing adaptive approach to POL, which first decides the target position attribute based on the signal strength and then localizes the target in different modes. We conduct rigorous localizability analyses and design a unit localization area scheme to achieve a higher level of localization accuracy. The efficacy of the proposed method is evaluated through comprehensive experiments in real life network environments.

show abstract

“…The median transmission loss predicted using the path geometry of the terrain profile is the goal of Edwards and Durkin, 8 as well as Dadson. 27 Our implementation of this propagation model in the NS-2 working environment closely follows their concepts. The execution of the path-loss estimation consists of two parts.…”

Section: Durkin's Modelmentioning

confidence: 99%

Terrain-aware three-dimensional radio-propagation model extension for NS-2

Filiposka¹,

Trajanov

2010

SIMULATION

View full text Add to dashboard Cite

One of the weakest points of modeling wireless systems is radio-signal propagation in an irregular space. For that reason, it is essential that, when analyzing the performances of wireless networks, we observe the network in a natural threedimensional terrain and we use an appropriate propagation model. However, care must be taken since such simulations demand extensive processing power, especially for mobile scenarios. In this paper, we present an extension for the NS-2 simulator with our optimized Durkin's propagation model based on digital elevation model data. Going one step further in creating a set of realistic simulation environments, we present a case study for modeling the behavior of a wireless ad hoc network via the social network of users grouped into scale-free communities. The case study presents a blend of topography responsive simulations with realistic traffic and movement pattern, while showing the numerous simulation possibilities of the presented extension.

show abstract

Computer prediction of field strength in the planning of radio systems

Cited by 24 publications

References 9 publications

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

Terrain-aware three-dimensional radio-propagation model extension for NS-2

Contact Info

Product

Resources

About