Empirical Path Loss Models for Wireless Sensor Network Deployment in Snowy Environments

It has been observed that the existing path loss models such as Hata, Okumura, and COST-231-Walfisch-Ikegami do not help in accurately predicting path loss for Indian terrain conditions. Since path loss values impacts the cost of operation along with Quality of Services (QoS) significantly, a new path loss model is desired. Modelling an accurate path loss model for Indian terrain requires terrain/city planning along with the building data, extensive drive tests for radio signal characterization, wireless telecom operator data related to Base Station (BTS) location, transmission power, height, tilt, etc. All these factors make the path loss modeling difficult and challenging. In this direction, we have proposed Tata Dense Urban Path loss (T-DUP) model which is a non-deterministic statistical model applicable for Indian cities. The proposed path loss model is validated through extensive drive tests at Bangalore with the help of a major Indian Telecom Operator operating at 1800 MHz. We have observed that the T-DUP model is statistically robust and is closely matching to the drive test results. We have also conducted statistical analysis and have illustrated the impact of various city planning parameters such as building heights, building separation, road widths, etc., on path loss model. We believe that T-DUP model can be extended to other frequency bands operating in India with appropriate tuning of parameters.

Purpose The purpose of this paper is to investigate the effect of snow on the radio link performance of wireless sensor nodes in Indian Himalayan conditions and to propose empirical path loss models for radio wave propagation. Design/methodology/approach At the remote test site, one source and three listening wireless sensor nodes were deployed at frequency of 433 MHz. The path loss models are derived from experimental data collected during the period of snowfall and clear weather conditions. Linear, exponential, second and third-order polynomials path loss models have been investigated along with experimental data. Findings With the help of curve fitting and goodness-of-fit tests, it is found that path loss can be modelled through third-order polynomial equation during the snowfall period. However, if sensor is buried, the acceptable path loss model is exponential. Similarly, for unified modelling requirement, exponential path loss model over linear can be a preferred choice. Originality/value Results show that path loss can be estimated priori for deciding optimum transmission energy in wireless sensor network. Presented work is usable in extending the lifetime of health monitoring devices buried in snowy environment.

For beyond the fifth-generation (5G) and future wireless communications, spatial consistency that represents the correlation between propagation channel characteristics in close proximity has become one of the major issues in channel modeling to describe channels more realistically in emerging scenarios, such as device-to-device (D2D). In this study, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that provides spatial consistency to channels in propagation environment by predicting local shadow fading while fitting large-scale path loss from measured data. The proposed model has a special structure consisting of a radial mean function and local shadow fading term, which are modeled by independent Gaussian Processes. In contrast to the log-distance path loss model and other regression-based models, the proposed model trains two functions simultaneously; thus, predicts path loss well by capturing both global tendency and local correlation. Moreover, because the proposed model is based on GPR, it provides uncertainty of the predicted path loss. We validated the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments showed that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation becomes significant. The proposed model can be used to simulate path loss in a general environment after training the measurement data.

Internet of Things (IoT) holds a great promise in providing autonomous and ubiquitous connectivity between devices in future communication systems. Due to the spectrum scarcity, very high frequency (VHF) and ultra high frequency (UHF) bands are viewed as valuable resources for IoT communications, especially to connect to distant locations that are hard to reach using higher frequencies. Existing propagation models in the VHF/UHF frequency bands are mainly for broadcasting and cellular systems with high transmit antenna heights, and hence, they are not suitable for IoT communications characterized by low antenna heights at both the transmitter and receiver. In this paper, we present new statistical path loss and delay spread models for IoT communications based on quasi-simultaneous wideband channel measurements conducted in the VHF/UHF frequency bands (from 37.8 to 370 MHz) at the city of Halifax, Canada. In particular, we present two log-distance path loss models (frequency-independent path loss exponent and frequency- dependent path loss exponent), as well as, a new statistical distribution of the delay spread.

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.

We study wireless sensor network (WSN) node placement in an environment where RF signal losses vary with position. This reflects real–world outdoor environments where vegetation and topography cause nonuniform path loss. Many techniques that solve for a variety of objective functions subject to various constraints have previously been proposed for node placement. However, many of these methods make simplifying assumptions such as all nodes having the same transmission range. Our goal is to take the insights and approaches of this previous ork and extend it to real–world environments. The present work assumes we have a map that quantifies the path loss behaviour of the real environment. Based on this map, and a path loss model that accounts for spatial variations in the path loss exponent, we propose a node placement algorithm for two–tiered WSNs that maximises the area covered by a specified number of relay nodes and sensor nodes.

In mobile radio systems, path loss models are necessary for proper planning, interference estimations, frequencies assignments and cell parameters which are basic for network planning process. Empirical models are the most adjustable models that can be suited to different types of environments. In this paper, data collected in Batna, Algeria is used to calculate the path loss for GSM (908-957 MHz). The measured path loss is compared with theoretical path loss estimated by the most widely empirical models "Cost123", "Hata", "SUI" and "Egli". The best model to estimate the measured path loss is optimized using Nelder-Mead algorithm to predict path loss for suburban and rural area. The RMSE between the actual and predicted data is calculated for various path loss models. It turned out that the adjusted COST 231 model outperforms the other studied models. The investigated results can help telecommunication engineers improve their planning and design of microcellular system.

New empirical path loss model for wireless sensor networks in mango greenhouses

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

This paper presents an empirical path loss model through forest for measuring sea wave energy using 2.4 GHz wireless sensor network (WSN). The empirical path loss model was determined from measurement campaign by using 18 dBm transmitter and the receivers with a low noise amplify. The conventional path loss models for forest environments were carried out such as Weissberger, ITU-R, COST 235 and Torrico models. From the results it is found that the proposed model provides a good agreement and is used for planning WSN.

An on- and off- combined body channel path loss (PL) model, along with its link realistic antennas effects, is developed in this paper. Based on a heterogeneous human body phantom and the verified antenna structures, the PL parameters of this model are extracted through the SEMCAD simulated results. This PL model is finally verified experimentally on a volunteer body.

Little research has been done to reliably model millimeter wave (mmWave) path loss in rural macrocell settings, yet, models have been hastily adopted without substantial empirical evidence. This paper studies past rural macrocell (RMa) path loss models and exposes concerns with the current 3rd Generation Partnership Project (3GPP) TR 38.900 (Release 14) RMa path loss models adopted from the International Telecommunications Union — Radiocommunications (ITU-R) Sector. This paper shows how the 3GPP RMa large-scale path loss models were derived for frequencies below 6 GHz, yet they are being asserted for use up to 30 GHz, even though there has not been sufficient work or published data to support their validity at frequencies above 6 GHz or in the mmWave bands. We present the background of the 3GPP RMa path loss models and their use of odd correction factors not suitable for rural scenarios, and show that the multi-frequency close-in free space reference distance (CI) path loss model is more accurate and reliable than current 3GPP and ITU-R RMa models. Using field data and simulations, we introduce a new close-in free space reference distance with height dependent path loss exponent model (CIH), that predicts rural macrocell path loss using an effective path loss exponent that is a function of base station antenna height. This work shows the CI and CIH models can be used from 500 MHz to 100 GHz for rural mmWave coverage and interference analysis, without any discontinuity at 6 GHz as exists in today's 3GPP and ITU-R RMa models.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

Propagation modeling is the most important part of mobile wireless network planning. Wireless network planning requires an accurate calculation of the path, which depends on different environmental conditions. It requires accurate path loss modeling of the characteristics of a specific region. The study aimed to obtain a path loss propagation model by modifying the ECC model and using linear, logarithmic regression in lowland areas. The measurement used drive test method, located in the Jakabaring area that represented the lowland area. This research used four existing path loss models, namely Okumura-Hatta, COST-Hatta, Ericsson Model, and ECC Model. It was found that the Okumura-Hatta model had the largest RMSE value, 34.90, followed by the Ericsson model, 27.07, while the ECC model had the smallest RMSE value, 8.43. The ECC model required to be modified using logarithmic, linear regression to obtain the proposed model. The results of the evaluation showed that the proposed model improved with RMSE 4.93, MAPE 2.71, and MAD 3.91, whereas the values of the existing ECC Model before modification were 8.43 for RMSE, 4.72 for MAPE and 7.09 for MAD. The proposed model provided an accurate prediction of the path loss propagation in a lowland environment. The results of the study can be used for planning engineers to plan, design, and implement the wireless communication networks in lowland area conditions.

The empirical propagation path loss models have important influence in mobile communication systems design. They become important tools in both research and industrial communities owing to their speed of implementation and their limited dependence on detailed knowledge of the terrain. The mobile services are increasing rapidly and hence the mobile stations are required to design with best quality of service. Path loss models are considered frequently to estimate power budgets and cell coverages. Four empirical models namely, the Lee model, the Stanford University Interim (SUI) model, the COST-231 Hata model and Egli model have been investigated in this work. The measurements were carried out at University Putra Malaysia (UPM) campus and Gombak area. The measured data is used to validate the applicability of the four models mentioned previously in suburban environments. The results show that Egli model, COST-231 Hata model and SUI model over-predict the path loss in all coverage areas. The Lee model shows the best performance to the measured path loss.