Search Results (10)

The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article

Women entrepreneurs in small to medium enterprises (SMEs) in emerging countries play an essential role in the economy of developing countries such as Indonesia. Drawing on the resource-based view and entrepreneurship effectuation theory, this study examines how women’s entrepreneurial effectuation (WEE) modeled as a higher-order construct (HOC) comprising its four dimensions (LOCs)—namely, flexibility, experimentation, affordable loss, and pre-commitment—can influence employee performance (EMPRF) mediated by structural (STREM) and psychological empowerment (PSYEM). Using a disjointed two-stage PLS-SEM approach with data from 218 female SME employees, our results confirm that flexibility is the most salient effectuation dimension. WEE strongly predicts both STREM and PSYEM but shows no direct impact on EMPRF, highlighting that effectuation must be activated via empowerment mechanisms. PSYEM emerges as the strongest mediator of WEE on EMPRF, with STREM also contributing significantly and being amplified by gender equality practices; market orientation, by contrast, fails to moderate any paths. Theoretically, these findings enrich resource-based view (RBV) theory by integrating entrepreneurial effectuation dimensions and empowerment as human resource capabilities that generate inimitable performance gains. Practically, they suggest that women-led SMEs should integrate effectuation heuristics with targeted empowerment programs to realize the full potential of their human capital. Full article

For efficient radio network planning, empirical path loss (PL) prediction models are utilized to predict signal attenuation in different environments. Alternatively, machine learning (ML) models are proposed to predict path loss. While empirical models are transparent and require less computational capacity, their predictions are not able to generate accurate forecasting in complex environments. While ML models are precise and can cope with complex terrains, their opaque nature hampers building trust and relying assertively on their predictions. To fill the gap between transparency and accuracy, in this paper, we utilize glass box ML using Microsoft research’s explainable boosting machines (EBM) together with the PL data measured for a university campus environment. Moreover, polar coordinate transformation is applied in our paper, which unravels the superior explanation capacity of the feature transmitting angle beyond the feature distance. PL predictions of glass box ML are compared with predictions of black box ML models as well as those generated by empirical models. The glass box EBM exhibits the highest performance. The glass box ML, furthermore, sheds light on the important explanatory features and the magnitude of their effects on signal attenuation in the underlying propagation environment. Full article

Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal. Full article

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models. Full article

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods. Full article

LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R² of 0.917. Full article

Terahertz (THz) communication is a key candidate for the upcoming age of beyond-fifth-generation mobile networks (B5G) or sixth-generation mobile networks (6G) in the next decade and can achieve ultra-high data rates of dozens of gigabits or even terabits per second. As the carrier frequency increases from radio frequency (RF) to the THz band, the impact of meteorological factors on the wireless link is expected to become more pronounced. In this work, we first provide an overview of the attenuation caused by atmospheric gases, fog, and rain on terrestrial THz wireless communications using the recommendations of the International Telecommunication Union-Radiocommunication (ITU-R). Measured data from the literature are used to predict the attenuation caused by snow. Because unfavorable weather conditions may harm sensitive measurement equipment, ray-tracing (RT) simulations are sometimes used as an alternative to extend sparse empirical data. In this study, the terrestrial channel in an urban scenario at 300 GHz, with a bandwidth of 8 GHz, is characterized using RT simulations under different meteorological factors. The key performance parameters are explored, including path loss (PL), Rician K-factor (KF), root-mean-square (RMS) delay spread (DS), and four angular spreads. The channel characteristics under different meteorological conditions studied in this work are expected to aid the design of future outdoor terrestrial THz communications. Full article

Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others. Full article

This article introduces an efficient analysis of indoor 4.5 GHz radio wave propagation by using a proposed three-dimensional (3-D) ray-tracing (RT) modeling and measurement. The attractive facilities of this frequency band have significantly increased in indoor radio wave communication systems. Radio propagation predictions by simulation method based on a site-specific model, such as RT is widely used to categorize radio wave channels. Although practical measurement provides accurate results, it still needs a considerable amount of resources. Hence, a computerized simulation tool would be a good solution to categorize the wireless channels. The simulation has been performed with an in-house developed software tool. Here, the 3-D shooting bouncing ray tracing (SBRT) and the proposed 3-D ray tracing simulation have been performed separately on a specific layout where the measurement is done. Several comparisons have been performed on the results of the measurement: the proposed method, and the existing SBRT method simulation with respect to received signal strength indication (RSSI) and path loss (PL). The comparative results demonstrate that the RSSI and the PL of proposed RT have better agreements with measurement than with those from the conventional SBRT outputs. Full article