Path Loss Prediction Research Articles

Performance analysis of artificial neural networks for predicting propagation losses in suburban environments for 4G LTE and 5G networks

<p>This study analyzes two distinct approaches for predicting path loss at frequencies of 800 MHz, 1800 MHz, and 2600 MHz in suburban areas. These frequencies are commonly utilized in broadcasting, 4G LTE, and 5G networks. Two models of Artificial Neural Networks (ANN) were implemented: an Error-Based Neural Network (EBNN), which incorporates error correction by combining empirical propagation models with an ANN, and a Terrain Parameters Based Neural Network (TBNN), which uses input parameters commonly applied in related studies, such as the distance from the transmitter to the receiver, receiver altitude, average terrain level, and azimuth angle between the transmitter and receiver. The performance of these models was evaluated using root mean square error (RMSE) and the Wilcoxon rank-sum test, comparing them with empirical propagation models such as SUI, ECC-33, Ericsson, and TR 25.942. The results were then compared with data obtained from a measurement campaign conducted along three routes in the city of Natal, Brazil. The findings from both simulations and actual measurements showed good metric alignment, particularly highlighting the performance of the error-based model. The primary contribution of this study is demonstrating that these techniques enable more accurate prediction of signal information, thereby reducing errors in the planning and implementation of wireless networks.</p>

Indoor Multi-Different-Wall Path Loss Prediction Model Using Adaptive Neuro-Fuzzy Inference System

The efficiency of wireless systems is heavily dependent on the precision of the propagation channel model. Accurate prediction of signal path loss is an essential process in planning and optimizing radio networks. In this paper, a new indoor path loss prediction model - utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach - is proposed. The proposed model takes into consideration the effect of different indoor barriers or wall types, in addition to other essential factors, on the propagation channel to obtain precise prediction in buildings with hybrid walls or partitions. On-site recorded WiFi data is used to build and verify the proposed model. To minimize modeling errors, a hybrid training method is employed, and the input membership functions of the model are optimized. The proposed ANFIS model is compared with the Cost-231 propagation model after it has been calibrated using a multilinear regression approach. The comparison indicates the superiority of the proposed model in terms of precision and accuracy. The obtained results show that the new model has a lower root mean square error and standard deviation with a higher correlation factor of 98%. The proposed ANFIS model produces precise indoor WiFi prediction and promotes accurate forecasting of other frequency bands - such as UMTS and GSM - in buildings with different interior structures.

Path Loss Prediction on Earth-Space Link Using Statistical and Time Series Approach at Ka-Band in Abuja, North Central Nigeria

Path Loss Prediction on Earth-Space Link Using Statistical and Time Series Approach at Ka-Band in Abuja, North Central Nigeria

Modelling and Optimization of Wireless Signal Propagation Path-loss in the Forcados- Ogulagha Maritime Environment of Delta State, Nigeria

The deployment of Long-Term Evolution (LTE) networks in marine environments involves unique challenges owing to the influence of environmental conditions on signal propagation. LTE networks are meant to provide high-speed internet and communication services, which rely largely on precise pathloss modelling. This study model and optimize wireless signal propagation path-loss in the Forcados- Ogulagha maritime environment. A total of eighty base transmission stations were surveyed to measure the signal strength and four maritime locations were selected to represent a typical mixed-path environment.The traditional model was optimized using Decision Tree-Particle Swarm (DT-PSO), Particle Swarm Optimization (PSO), and Random Forest-Particle Swarm (RF-PSO) methods. The results show that certain features, such as measurement distance and temperature, play a crucial role in determining path loss, providing valuable guidance for refining path loss prediction models and optimizing the performance of wireless communication systems. The research further revealed that conventional path loss models showed significant discrepancies compared to actual measurements due to the impact of terrain and topography variations on the model's ability to capture nonlinear and non-stationary path loss factors.

A Deep Probabilistic Machine Learning Approach to Ray Tracing Path Loss Prediction at 900 MHz

A Deep Probabilistic Machine Learning Approach to Ray Tracing Path Loss Prediction at 900 MHz

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Path Loss Prediction in Urban Environments With Sionna-RT Based on Accurate Propagation Scene Models at 2.8 GHz

Path Loss Prediction in Urban Environments With Sionna-RT Based on Accurate Propagation Scene Models at 2.8 GHz

Path loss prediction for air-to-ground communication links via scenario transfer technology

Path loss (PL) is a significant channel parameter for the link budget in unmanned aerial vehicle-aided communications. This study introduces an innovative neural network model to estimate PL for air-to-ground communication links. Utilizing the geometric characteristics of varied physical environments, the model accurately predicts PL in diverse communication scenarios. A back-propagation neural network technique is introduced for extrapolating PL under both line-of-sight and non-line-of-sight conditions. A dataset acquisition strategy, comprising scenario reconstruction and advanced ray-tracing techniques, is employed to foster the model’s training and evaluation. Finally, the proposed model is fully trained in diverse communication scenarios, and then used to predict the PLs in a new communication scenario generated by the International Telecommunication Union standard at 28 GHz. The results demonstrate that the extrapolated PLs of the proposed model are well consistent with the reference results. As existing PL models and standard PL models aim at several specifically defined scenarios, the proposed model can predict the PLs in some undefined and unknown scenarios.

Performance Evaluation of GeoAI-Based Approach for Path Loss Prediction in Cellular Communication Networks

Performance Evaluation of GeoAI-Based Approach for Path Loss Prediction in Cellular Communication Networks

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 MHz, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)-LoRa, etc.

Multi-Modal Sensing Data-Based Real-Time Path Loss Prediction for 6G UAV-to-Ground Communications

Multi-Modal Sensing Data-Based Real-Time Path Loss Prediction for 6G UAV-to-Ground Communications

An Improved Practical Measuring Technique for Wireless Cellular Signal Path Loss Forecast in the African Terrain

Several studies on cellular signal path loss measurements at various locations under varying geographical and environmental circumstances in Africa have been conducted. Most of the techniques used, if not all, do not give consideration to the peculiar Africans’ road networks and building patterns, valleys, and hills that usually prevent the driving of motor cars within them when taking the signal strength measurements. The article presents an improved technique for wireless cellular signal strength measurement for better path loss prediction. The approach adopted for measurement is the regular grid outdoor foot technique. The technique was tested at various locations in the Ilorin metropolis and proved to be a better method of obtaining signal strength measurements. Although some of the signal strength values coincided with respect to distance when both methods were used, the drive test method overvalued the signal strength for study locations that were not in line of sight with the BTSs. These values range from 2 dBm to 8 dBm. If this technique is adopted, measurements will be taken in all vital locations that may not be accessible by means of a motor car drive test method, and more accurate values of the cellular signal strength will be obtained for better path loss prediction.

Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Millimeter wave communication systems design requires accurate pathloss prediction as well as optimization. Traditional pathloss models gives less accuracy therefore pathloss models using deep learning is alternative solution for traditional method. These methods overcome the time consuming pathloss prediction parameter prediction based on large dataset which is available for different frequencies. In this proposed work, for prediction of pathloss, Convolutional Neural Network (CNN) and for optimization of pathloss Particle Swarm Optimization (PSO) is proposed. Towards implementation of the same, outdoor scenario with two roads and one cross section is used and dataset is generated using DeepMIMO generic dataset. The simulation result shows that using optimization technique reduces pathloss and CNN give accurate prediction of 95.7 %. Furthermore, root mean square error value of 6.6 dB has been achieve which is less in comparison to alternative approaches designed for outdoor environments. The forthcoming development of multifrequency path loss models has sparked significant interest due to their forecasting ability to support higher frequency bands and address blockage scenarios within future wireless networks.

Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios

The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid approach depends on the optimal understanding of its impact on network and protocol performance in an actual reference scenario. This work, which has practical implications for the field, focuses on creating a realistic representation of the dual-link-enabled Hybrid Vehicular Network (HVN) in the simulator. Several enhancements were implemented into a network simulator, including multiple Radio Access Technologies (multi-RAT) support, models for path loss predictions, static subscribers, background interference, and SINR throughput mapping within a multi-cell environment. We then presented a parameterized data traffic steering algorithm that enables switching between Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) RAT based on channel congestion and cell load conditions. The simulation result shows the high potential of a dual-link-enabled hybrid approach and its implications on both networks under realistic vehicular networking conditions. The investigations reveal that an intelligent decision to steer traffic can target balanced connectivity and highly reliable vehicular communication at a reduced network load.

Attention-transfer-based path loss prediction in asymmetric massive MIMO IoT systems

The asymmetric massive multiple-input–multiple-output (MIMO) array improves system capacity and provides wide-area coverage for the Internet of Things (IoT). In this paper, we propose a novel attention-based model for path loss (PL) prediction in asymmetric massive MIMO IoT systems. To represent the propagation characteristics, the propagation image that considers the detailed environment, beamwidth pattern, and propagation-statistics feature is designed. Benefiting from the shuffle attention computation, the proposed model, termed a shuffle-attention-based convolutional neural network (SAN), can effectively extract the detailed features of the propagation scenario from the image. Besides, we design the beamwidth-scenario transfer learning (BWSTL) algorithm to assist the SAN model in predicting PL in the new asymmetric massive MIMO IoT systems, where the beamwidth configuration and propagation scenario are different. It is shown that the proposed model outperforms the empirical model and other state-of-the-art artificial intelligence-based models. Aided by the BWSTL algorithm, the SAN model can be transferred to new propagation conditions with limited samples, which is beneficial to the fast deployment in the new asymmetric massive MIMO IoT systems.

A Critical Review of the Propagation Models Employed in LoRa Systems.

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

AbstractElevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct‐height‐based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH‐producing systems. However, producing high‐resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high‐time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense‐Linear convolutional neural network (DLCNN)‐based EleDH refinement technique based on the development of statistical downscaling and super‐resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high‐time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super‐resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High‐resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB.

Ultrahigh frequency path loss prediction based on K-nearest neighbors

Abstract Path loss prediction (PLP) is an important feature of wireless communications because it allows a receiver to anticipate the signal strength that will be received from a transmitter at a given distance. The PLP is done by using machine learning models that take into account numerous aspects such as the frequency of the signal, the surroundings, and the type of antenna. Various machine learning methods are used to anticipate path loss propagation but it is difficult to predict path loss in unknown propagation conditions. In existing models rely on incomplete or outdated data, which can affect the accuracy and reliability of predictions and they do not take into account the effects of environmental factors, such as terrain, foliage, and weather conditions, on path loss. Furthermore, existing models are not robust enough to handle the real-world variability and uncertainty, leading to significant errors in predictions. To tackle this issue, a novel ultrahigh frequency (UHF) PLP based on K-nearest neighbors (KNNs) is developed for predicting and optimizing the path loss for UHF. In this proposed model, a KNN-based PLP has been used to predict the path loss in the UHF. This technique is used for high-accuracy PLP through KNN forecast route loss by determining the K-nearest data points to a particular test point based on a distance metric. Moreover, the existing models were not able to optimize path loss due to complex and large-scale machine learning models. Therefore, the stochastic gradient descent technique has been used to minimize the objective function, which is often a measure of the difference between the model’s predictions and the actual output that will fine-tune the parameters of the KNN model, by measuring the similarity between data points. This model is implemented using Python to make it a lot more convenient.

Machine learning-based methods for MCS prediction in 5G networks

In the ever-evolving landscape of wireless communication systems, including fifth-generation (5G) networks and beyond (B5G), accurate Modulation and Coding Scheme (MCS) prediction is crucial for optimizing data transmission efficiency and quality of service. Traditional MCS selection methods rely on predefined rules and heuristics, offering transparency and control but lacking adaptability in changing wireless conditions. The emergence of Machine Learning (ML) has brought transformative capabilities, particularly in MCS prediction. ML leverages data-driven models, promising improved accuracy and adaptability in dynamic wireless environments. This paper marks a novel endeavor in this domain, as it explores and evaluates a range of machine learning (ML) techniques for predicting MCS in orthogonal frequency-division multiplexing (OFDM) systems, representing the first such investigation in this field. Additionally, it introduces a specialized Deep Neural Network (DNN) architecture with two hidden layers for MCS prediction, guided by performance metrics such as accuracy, precision, recall, and F1-score. The examined ML methods include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), and Bagging with k-NN (B-kNN). These methods undergo thorough training and evaluation using a dataset generated from simulations of non-standalone 5G networks. The study incorporates physical layer measurements and employs a ray-tracing path loss prediction model for comprehensive environmental characterization. Also, advanced data mining techniques preprocess raw data, addressing model underfitting and overfitting challenges. Finally, performance evaluation results reveal that the ANN with two hidden layers achieves the highest accuracy at 98.71%, while RF and B-kNN methods attain the lowest accuracy, below 88.65%. SVM and ANN models, with one and four hidden layers, respectively, demonstrate comparable MCS prediction accuracy, ranging from 97.02 to 97.30%.