Path Loss Model Research Articles

Pathloss Assessment of a Terrestrial Digital UHF Channel over Kano City, Nigeria

Path loss assessment is an important factor in wireless communications to ensure quality of service. This study involves the measurement of Received Signal Strength (RSS) of Digital Terrestrial Television (DTTV) Star Times Station in Kano at 1 km intervals along three routes. Data were gathered during dry and wet season months in the year 2022. The path loss along the routes were computed based on the Okumura-Hata model. It was observed that path losses were higher during wet than dry season months. The result also indicates a higher negative correlation coefficient (R) of -0.745 between Line of Sight (LOS) and RSS for wet and -0.690 for dry season months respectively for route, A. Similarly, R, of -0.739 between LOS and RSS was also observed during wet and -0.589 for dry season months for route, B. In addition, R, of -0.420 was observed between LOS and RSS during wet and -0.328 for dry season months along route C. The average path loss for wet season is 133.90 dB and for dry season is 127.90 dB. Furthermore, this study introduced a Modified Hata Path Loss Model (MHPL) that takes into account the impact of specific tropospheric parameters such as LOS and elevation. Findings further indicated that the path loss predicted by the conventional Hata model exhibited a steady exponential increase with LOS. Similarly, MHPL followed a comparable trajectory, with some exceptions manifesting as peaks and valleys. The overall findings will be useful for power budgeting that will enhance quality of service of DTTV signal over Kano city.

Path Loss Models for 5G Communications System in Corridors Environment

This paper presents the propagation path loss channel models, developed from real-field measurement in Universiti Teknologi Malaysia (UTM) Kuala Lumpur, Malaysia. The purpose of the study is to characterize the channel at 28 GHz for 5G communications system in line-of-sight corridors environment. Measurement campaigns were conducted to measure the wireless signal of received signal strength at three different construction of straight corridors, narrow, wide, and open corridors. The large-scale path loss models are developed using the closed-in reference distance and floating–intercept modeling approaches. Besides contributing path loss models at 28 GHz for different corridors dimension, the result found in this work discovered the breakpoint distance of radio propagation is seen varies differently at those corridors.

60 GHz millimeter-wave indoor propagation path loss models for modified indoor environments

The 60 GHz band has been selected for short-range communication systems to meet consumers’ needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with square loop (SL) frequency selective surfaces (FSSs) wallpaper, thereby increasing its utilization. The SL FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and CST Studio Suite software. ‘Wireless InSite’ is also used to demonstrate enhanced wave propagation in a building modified with SL FSSs wallpaper. The demonstration is applied to multiple input multiple output system to verify the effectiveness of FSSs on such systems’ capacity, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using SL FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation path loss models, the close-in (CI) free space reference distance model and the floating intercept (FI) model. Data obtained from ‘Wireless InSite’ over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model.

A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks

Mobile coverage is crucial for the fifth-generation (5G) network since it affects the network's accessibility and dependability in various locations. With a wider coverage, more people and devices will access the 5G network, allowing them to experience the benefits and capabilities of this important technology. However, the development and construction of the networks need a lot of time and effort to maximize network coverage and service delivery while utilizing the fewest possible infrastructure components. Path loss models are commonly employed to predict network coverage. Therefore, it is expedient to adopt a path loss model that is suitable for the specified geographical area. In this paper, we investigated and analysed two empirical models: the Close-in free space reference distance (CI) model and the alpha-beta-gamma (ABG) model at the low- and mid-spectrum bands. Secondly, the path loss parameters were derived to characterize the two empirical models based on data from the measurement campaign. Finally, Root Mean Square Error (RMSE) was employed for the comparison of the models. It was found that at frequencies 0.9 and 1.8 GHz, the CI model's least RMSE values were 3.6 and 4.1 at location 1 and 4.7 and 4.2 dB at location 2, respectively, outperforming the ABG. However, at 3.4 GHz, the ABG with 3.25 and 1.75 dB outperformed the CI at both locations. For future work, the integration of machine learning techniques to dynamically predict and adapt path loss models in real time based on continuous data collection will be appropriate.

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was proposed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model's performance was optimised using the AHT tuning technique. The AHT-SEML model's efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub-Saharan cities, at mmWaves frequencies. The AHT-SEML model's performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML's t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p-values of 1.91×10−14 and 2.50×10−13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML's performance.

A modified cost-231-hata path loss model for typical semi-urban environments in Nigeria

Outdoor path loss propagation modeling is critical in the planning and design of the coverage area by the Global System for Mobile Communication (GSM). For the best prediction of GSM signal at any location within its coverage region, an accurate forecast based on critical characteristics and a mathematical model is necessary. Multiple studies on path loss propagation model prediction for GSM networks conducted at various semi-urban environments in Nigeria proclaimed that propagation path loss models may provide different results when utilized in environments other than those in which they were initially designed, that car drive-test methodology was used during the data collection, and that COST-231-Hata model provides closet prediction to the practical measure values. This paper created an appropriate path loss model based on the COST-23-Hata model and outdoor measurement at 1800 MHz frequency range for the semi-urban area of Kwara State, Nigeria. The created model was used and validated with the measured data and COST-231-Hata model at other different semi-urban environments in Nigeria. The results analysis shows that the created model performed satisfactorily given the closet path loss prediction to the practical measure path loss values at all the study locations. It also gives the lowest Square Root Means Error (SRME) and Standard Deviation (SD) in all the base stations that were tested in semi-urban environments. The newly created model would therefore be more appropriate for GSM 1800 network design and installation in semi-urban environments in Kwara State, Nigeria, as well as any other semi-urban locations in Nigeria.

Optimal and Efficient Design of WBAN Sensor Antenna System and Voxel-Based Path Loss Modeling for Internet of Wearable Things

Optimal and Efficient Design of WBAN Sensor Antenna System and Voxel-Based Path Loss Modeling for Internet of Wearable Things

Measurement-based V2V propagation modeling in highway, suburban, and urban environments

Vehicle-to-vehicle (V2V) communication is one of the promising communication applications designed to optimize traffic conditions and has played a crucial role in the improvement of intelligent transportation technologies. Since there is still some uncertainty regarding generalized models that provide a more accurate representation of propagation environments, the existing literature emphasizes the need for additional experimental studies in various countries and propagation environments. This study aims to investigate the low-density and high-density characteristics of V2V channels for highway, suburban, and urban propagation environments in Türkiye. Thus, first, channel measurements were conducted for all propagation scenarios. Then, after the estimation of path loss parameters, the best-fitted path loss model was determined for each propagation scenario by comparing log-distance, two-ray, and log-ray models. It was observed that the log-ray model offered remarkably better performance than the two-ray model, especially in the majority of scenarios with two-ray characteristics. In addition, small-scale modeling and shadowing were also examined, and the outcomes were compared to relevant literature. Last, generalized path loss models were developed for six propagation scenarios and compared with previous studies. Providing additional experimental data on the impact of traffic and road environments that vary across countries on the V2V channel, this study not only validated and compared existing propagation models but also improved the representing accuracy and generalizability of the newly proposed propagation models. Here, all findings were presented in detail to support the motivation of the research.

A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring.

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model's precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Lorawan-Based RSSI-Trilateration Model for Node Location: A Simulation Integrating Flora and Omnet++

Abstract This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

Improving Performance of the Typical User in the Indoor Cooperative NOMA Millimeter Wave Networks with Presence of Walls

INTRODUCTION: The beyond 5G millimeter wave cellular network system is expecting to provide the high quality of service in indoor areas. OBJECTIVES: Due to the high density of obstacles, the cooperative communication technique is employed to improve the user's desired signal power by finding more than one appropriate station to serve that user. METHODS: While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable the cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depends on the success of decoding the message from the user' serving BS of the second nearest BS, the typical user can work with and without assistance from the relay whose operation follows the discipline of the power-domain NOMA technique. In the case of with relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals. RESULTS: To examine the performance of the proposed system, the Nakagami-m and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closed-form expressions of this performance metric are derived by Gauss quadrature and Welch-Satterthwaite approximation.

The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq

Abstract The high speeds resulting from the use of millimeter waves (mmWave) in 5G mobile networks are accompanied by high path loss. The issue of generating a reliable propagation model of radio waves is crucial to the development of cellular networks since it reveals essential information regarding the properties of the wireless channel. The received signal strength, the coverage area, and the outage probability in certain places may all be determined through theoretical or empirical radio frequency propagation models, which offer essential valuable information regarding signal path loss and fading. This work analyzes a comprehensive three-dimensional ray-tracing method at 28 GHz for Najaf city, Iraq. The optimum path loss model for the city of Najaf is evaluated using the close-in (CI) model. On average, the values of the main parameters of CI model n n , X σ CI {X}_{\sigma }^{{\rm{CI}}} accomplished, respectively, 3.461866667 and 11.13958333. The lowest achievable path loss exponent was 3.0619 across all analyzed scenarios, while the highest possible value was 4.1253. The results of this work can serve as a baseline for mmWave measurement campaigns conducted in comparable conditions, and they provide a new avenue for future research into mmWave at 28 GHz in Iraq.

Channel Modeling Based on Transformer Symbolic Regression for Inter-Satellite Terahertz Communication

Channel modeling is crucial for inter-satellite terahertz communication system design. The conventional method involves manually constructing a mathematical channel model, which is labor-intensive, and using a neural network directly as a channel model lacks interpretability. This paper introduces a channel modeling approach based on symbolic regression. It is the first time that using transformer neural networks as the implementation tool of symbolic regression to generate the mathematical channel model from the channel data directly. It can save manpower and avoid the interpretability issue of using neural networks as a channel model. The feasibility of the proposed method is verified by generating a free space path loss model from simulation data in the terahertz frequency band.

Research on WSN reliable ranging and positioning algorithm for forest environment

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions.

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura-Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura-Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

Digital twin modeling of open category UAV radio communications: A case study

The modeling of radio links plays a crucial role in achieving mission success of unmanned aerial vehicles (UAVs). By simulating and analyzing communication performance, operators can anticipate and address potential challenges. In this paper, we propose a full-featured UAV software-in-the-loop digital twin (SITL-DT) for a heavy-lifting hexacopter that integrates a radio link module based on an experimental path loss model for ‘Open’ category Visual Line of Sight (VLOS) conditions and drone-antenna radiation diagrams obtained via electromagnetic simulation. The main purpose of integrating and simulating a radio link is to characterize when the communication link can be conflicting due to distance, the attitude of the aircraft relative to the pilot, and other phenomena. The system architecture, including the communications module, is implemented and validated based upon experimental flight data.

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.

An improved path loss model for 5G wireless networks in an enclosed hallway

An improved path loss model for 5G wireless networks in an enclosed hallway