Propagation Path Loss Estimation Using Nonlinear Multi-Regression Approach

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

doi:10.1109/icc.2010.5502808

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…In addition, based on the path loss information, the coverage limit of a transmitter can be estimated [13]. Therefore, utilising other parameters such as bandwidth, delay and throughput may not be as effective, since they provide less information to indicate whether the link is unidirectional.…”

Section: The Proposed Schemementioning

confidence: 99%

On-demand routing with unidirectional link using path loss estimation technique

Zuhairi

Zafar

Harle

2012

Wireless Telecommunications Symposium 2012

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Most on-demand Mobile Ad Hoc etwork (MA ET) routing schemes typically assume operation over symmetrical and bidirectional routing paths, both of which are not feasible due to the inherent heterogeneous properties of wireless devices. Radio links between a set of wireless devices may be asymmetrical and unidirectional as a result of factors such as high mobility, packet collisions due to interference and differences in terms of transmission power assigned to the devices. In this paper, an efficient routing scheme based on the path loss estimation technique is proposed. The proposed scheme effectively reduces the probability of selecting routing paths with unidirectional links by predicting the symmetrical properties of the link. In addition, nodes choose the link with the lowest path loss and estimate the received power in the reverse direction. As such, the number of unreliable links formed along the routing path can be minimized. The proposed scheme is compared against AODV-EUDA, AODV, and AODV-Blacklist, which provides support for unidirectional link detection and avoidance. Simulation results shows that the proposed scheme improves the number of packet transmitted by as much as 53%.

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Section: The Proposed Schemementioning

confidence: 99%

On-demand routing with unidirectional link using path loss estimation technique

Zuhairi

Zafar

Harle

2012

Wireless Telecommunications Symposium 2012

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“…However, as signals propagate through the wireless channel, the strength diminishes in free space due to expansion and presence of obstacles, resulting in reflection, refraction, diffraction, and absorption of the signals, and hence, multipath [2][3][4]. The reduction in signal strength is termed path loss and its computation/prediction is important, particularly during network design, maintenance, or interference analysis, as it helps in setting up network parameters such as transmitter/base station antenna height, cell radius, and transmitting power [5,6]. Prediction of path loss is done using either empirical models, deterministic models, or Machine Learning models (ML) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

2022

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Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the model accuracy for a particular scenario is low due to data imbalance. This is because model accuracy varies at certain regions of the dataset and such variations are more intense when the dataset is generated from a fusion of datasets of different sizes. A Dynamic Regressor/Ensemble selection technique is proposed to address this problem. In the proposed method, a regressor/ensemble is selected to predict a sample point based on the sample’s proximity to a cluster assigned to the regressor/ensemble. K Means Clustering was used to form the clusters and the regressors considered are K Nearest Neighbor (KNN), Extreme Learning Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The ensembles are any combinations of two, three or four of the regressors. The sample points belonging to each cluster were selected from a validation set based on the regressor that made prediction with lowest absolute error per individual sample point. Implementation of the proposed technique resulted in accuracy improvements in a scenario described by a few sample points in the training data. Improvements in accuracy were also observed on datasets in other works compared to the accuracy reported in the works. The study also shows that using features extracted from satellite images to describe the environment was more appropriate than using a categorical clutter height value.

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“…The most common methods used for estimation path-loss exponent are the linear regression with the Least Square Method [ 21 ], Multivariate Linear Regression [ 22 ] or Generalised Additive Model described in Reference [ 23 ]. In the case of a non-linear relationship, a non-linear regressions are used [ 24 ]. Statistical methods are also used to estimate path-loss exponent.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Path-Loss Exponent by Bayesian Filtering Method

Wójcicki

Zientarski

Charytanowicz

et al. 2021

Sensors

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Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.

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Propagation Path Loss Estimation Using Nonlinear Multi-Regression Approach

Cited by 8 publications

References 7 publications

On-demand routing with unidirectional link using path loss estimation technique

On-demand routing with unidirectional link using path loss estimation technique

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

Estimation of the Path-Loss Exponent by Bayesian Filtering Method

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