Comparison between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities

Castro, Bruno; Pinheiro, Márcio Rodrigues; Cavalcante, Gervásio P. S.; Gomes, Igor dos Santos; Carneiro, Oziel de Oliveira

doi:10.1590/s2179-10742011000100011

Cited by 15 publications

(8 citation statements)

References 4 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An efficient method for estimating the accuracy of path loss model is the RMS error, which is the difference in dB between the measured path loss and estimated path loss [13][14] [15].…”

Section: Root Mean Square Error (Rms Error)mentioning

confidence: 99%

Path Loss Propagation Model Prediction for GSM Network Planning

Mawjoud¹

2013

IJCA

View full text Add to dashboard Cite

This paper deals with the outdoor path loss behavior. The study has been conducted in Mosul city Iraq to fit a suitable propagation model to measured data at 900 MHz and 1800 MHz in urban and suburban environment. The empirical models dealt which are Hata, Cost-231 Hata, international Telecommunication Union (ITU-R), Ericsson and Stanford University Interm (SUI). Path loss accuracy of the fitting of the empirical models to the measured data is estimated using Root Mean Square (RMS) error.

show abstract

“…An efficient method for estimating the accuracy of path loss model is the RMS error, which is the difference in dB between the measured path loss and estimated path loss [13][14] [15].…”

Section: Root Mean Square Error (Rms Error)mentioning

confidence: 99%

Path Loss Propagation Model Prediction for GSM Network Planning

Mawjoud¹

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Similar results were presented by Faruk et al (2013b), who concluded that tuning of Hata-Davidson's model, which is one of the models that show better fits, at least along some selected routes, is necessary to minimise the RMSE values within the acceptable ranges. Fernández et al (2012) (2010), Castro et al (2011), and Medeisis and Kajackas (2007). In all these, measurements were conducted in given areas and optimisation techniques were employed to tune the model parameters to decrease the prediction errors.…”

Section: Related Workmentioning

confidence: 98%

Improved path-loss model for predicting TV coverage for secondary access

Фарук

Ayeni

Adediran

et al. 2014

IJWMC

View full text Add to dashboard Cite

Path-loss model is essential for proper planning of emerging technologies in the broadcast frequency band. Characterisation of the radio channel is paramount for accurate prediction of TV coverage, and achievement of peaceful coexistence between the TV (primary users) and other new infrastructures (secondary users). In this paper, field strength measurements are conducted along six routes that spanned through urban, suburban and rural areas of Kwara State, Nigeria. The measurement results are compared with path-loss prediction of eight widely used empirical models. Least squares and linear iterative methods are employed to optimise Hata-Davidson's model, as it showed best fit compared with other models. The predictions of the tuned model are compared with other models in terms of relative error, mean error, skewness, root mean square errors and normalised error probability density function. Results of the simulations indicate that the optimised model gives better values for the metrics considered.

show abstract

“…In other words, the actual wireless IoT performance differs when used in environments other than where they were developed. Furthermore, the ongoing expansion of wireless networks needs further signal propagation studies to assure an effective postplanning phase service coverage and efficiency [2].…”

Section: A Problem Formulation and Backgroundmentioning

confidence: 99%

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

et al. 2022

View full text Add to dashboard Cite

The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.

show abstract

Comparison between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities

Cited by 15 publications

References 4 publications

Path Loss Propagation Model Prediction for GSM Network Planning

Path Loss Propagation Model Prediction for GSM Network Planning

Improved path-loss model for predicting TV coverage for secondary access

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Contact Info

Product

Resources

About