Path Loss Model Research Articles

Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

Wireless Channel Propagation Model for Inland Waterway Bridge Scenario

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

English

English

Omni-directional path loss model for non-line-of-sight ultraviolet communications

Omni-directional path loss model for non-line-of-sight ultraviolet communications

Enabling Technologies and Techniques for Floor Identification

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-date vertical localization approaches. This article provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, fingerprinting utilized by WiFi, radio-frequency identification (RFID), global system for mobile communications (GSM), long-term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The article primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.

Modeling and analysis of propagation characteristics of WBAN in hospital ward scenarios

AbstractTo explore the feasibility of high‐frequency indoor wireless body area network (WBAN) communications, the propagation characteristics of WBAN in simulated hospital ward scenarios at 11 GHz are studied. Based on the extensive measured data, the impact of the patient's lying angle (LA) on channel characteristics has been analyzed, such as statistical characteristics of path loss (PL), shadowing effect, and root mean square delay spread. Moreover, a PL model with LA factor is presented for the hospital ward scene, and the influence of LA on PL in the presence of height difference is also analyzed. The research results indicate that, the PL caused by LA (abbreviated as PLA) follows a power function with respect to the LA in case of a fixed distance between the transmitter and receiver. When the LA is fixed, PLA exhibits a cubic function relationship with the distance between the transmitter and receiver. In addition, the presence of patient's LA can cause more significant multipath effect. However, the impact of LA gradually weakens as the height difference increases when there is a height difference. The research results provide a theoretical basis and practical basis for utilizing the 11 GHz frequency band in indoor WBAN in the future.

An Environment-Adaptive Radio Propagation Path Loss Model With Ray-Based Validation

An Environment-Adaptive Radio Propagation Path Loss Model With Ray-Based Validation

Analyzing the BER of NOMA in Different Channels with Various Path Loss Models and Multipath Fading for Contemporary Cellular Wireless Communications

Analyzing the BER of NOMA in Different Channels with Various Path Loss Models and Multipath Fading for Contemporary Cellular Wireless Communications

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.

Path loss modelling of mmwave outdoor propagation for 5G mobile systems at 28, 38, 60, and 73 GHz in four Nigerian cities

Millimetre-waves (mmWaves), a critical part of 5G, have not been extensively studied in the sub-Saharan tropical environment. This study was conducted to model the outdoor propagation path loss of mmWaves in four Nigerian cities at 28, 38, 60, and 73 GHz for 5G mobile systems: Abuja, Lagos, Ibadan, and Port Harcourt. The study utilised simulation data from a Composite Raytracing-Image-Method propagation model in MATLAB 2023a with OpenStreetMap 3D building maps to formulate the Close-In free-space Reference Distance (CI), Floating Intercept (FI), and Alpha–Beta-Gamma (ABG) large-scale path loss models. This model considered the specific atmospheric conditions, terrain, building materials and structures, and geographical layouts of each site. The study found that the path loss exponent ranged from 2.712 to 3.819 in line-of-sight scenarios, with shadow fading standard deviation (σCI) ranging from 11.778 to 37.199. In non-line-of-sight scenarios, the path loss exponent ranged from 3.375 to 5.033, with σCI ranging from 12.441 to 44.181 across the four frequencies studied. The ABG model consistently provided the most accurate predictions compared to CI and FI, particularly the mean absolute error and root mean square error values. However, performance varied across cities, frequencies, and scenarios, with Port Harcourt exhibiting the highest path loss values across all frequencies and scenarios, followed by Lagos. Furthermore, the omnidirectional large-scale path loss increases marginally as the frequency increases, except at 60 GHz, which had a higher path loss per distance than 73 GHz. Finally, the findings showed that the propagation characteristics of mmWaves for 5G networks in sub-Saharan tropical environments, such as Nigeria, exhibit significant variability based on factors such as atmospheric conditions, terrain, building materials, and geographical layouts. The formulated path loss models can be used to predict the coverage area and signal quality of millimetre-wave communication systems at these frequencies and locations.

An empirical study of ISAC channel characteristics with human target impact at 105 GHz

AbstractLeveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, the MPCs belonging to target channel are extracted within target‐oriented power delay profile to count the power proportion of each bounce MPCs of the target‐Rx link, which supports the necessity of multi‐bounce (indirect) paths modelling in target channel.

Study and Analysis Indoor Path Loss Models for Low-Terahertz mmWaves

Study and Analysis Indoor Path Loss Models for Low-Terahertz mmWaves

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.

Enhanced Indoor Path Loss and RSRP of 5G mmWave Communication System with Multi-objective Genetic Algorithm

The signal strength in 5G mobile communication systems is significantly influenced by the surroundings, with key factors including the path difference, operating frequency, and obstructions at specific locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in different environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor officce, 5GCM InH shopping mall, 3GPP TR 38.91 InH office, mmMAGIC InH office, METIS InH shopping mall, and IEEE 802.11 ad InH office indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36.87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of -12.17dBm,-11.37dBm,-12.17dBm,-5.80dBm,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.17~dBm, -11.37~dBm, -12.17~dBm, -5.80~dBm,$$\\end{document}-12.24dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.24~dBm$$\\end{document} and -8.68dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-8.68~dBm$$\\end{document} in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, -5.80dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-5.80~dBm$$\\end{document}, 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss difference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power difference above 37 dBm. Therefore, the highest path loss difference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios.

Mobile Network Coverage Prediction Using Multi-Modal Model Based on Deep Neural Networks and Semantic Segmentation.

A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction.

Coverage analysis of coded orthogonal frequency division multiplexing (C-OFDM) based high speed railways (HSR) networks under different propagation scenarios

In this research work, investigation of percentage of coverage area is performed for coded OFDM (COFDM) based high-speed railways (HSR) communication networks. Different HSR scenarios such as viaduct, rural, suburban, urban and cutting etc. are considered for this analysis. Path loss models for these HSR scenarios are explored and analysed w.r.t the distance between base station (BS) and the receiver located at the train. Further, link budget of the network is investigated under these HSR scenarios. In addition, bit error rates (BER) of this communication networks are analysed under vehicular A and vehicular B channel models. For these investigation, sub-GHz frequency bands such as 2.5 GHz, 3.5 GHz, and 5.8 GHz are considered. It is observed that path loss in viaduct scenario is less as compared to other scenarios irrespective of frequency bands. It is also found that BER is more when the signal in propagated through vehicular B channel as compared to vehicular A channel. In addition, it is also noted that percentage of coverage area in viaduct scenario is more as compared to other HSR scenarios irrespective of frequency bands.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

Geometric Optimization Algorithm for Path Loss Model of Riparian Zone IoT Networks Based on Federated Learning Framework

Geometric Optimization Algorithm for Path Loss Model of Riparian Zone IoT Networks Based on Federated Learning Framework

Path loss, angular spread and channel sparsity modeling for indoor and outdoor environments at the sub-THz band

In this paper, we present new measurement results to model large-scale path loss, angular spread and channel sparsity at the sub-THz (141–145 GHz) band, for both indoor and outdoor scenarios. Extensive measurement campaigns have been carried out, taking into account both line-of-sight (LoS) and non line-of-sight (NLoS) propagation. For all considered propagation scenarios, omni-directional and directional path loss models have been developed, based on the so-called close-in (CI) free-space reference distance model. A power angular spread analysis is further presented. The sparsity of the wireless channel has also been studied by employing suitable metrics, namely the so-called Gini index (GI), the Ricean K-factor and the root mean square (RMS) delay spread.

Multi‐band millimetre wave indoor directional channel measurements and analysis for future wireless communication systems

AbstractSingle‐input single‐output (SISO) three‐dimensional (3D) wideband indoor directional measurements collected in a factory environment and an office environment at 38 and 70 GHz are presented. 3D single‐input multiple‐output (SIMO) dual polarised measurements with 1 × 2 antenna configurations were also carried out in a meeting room, a conference room, and an office room at the 60 GHz band. The measurements cover both azimuth and elevation by rotating the directional antenna (RDA) at the receiver side. Different statistical channel parameters such as power delay profile, power angle profile, root‐mean‐square delay spread, angular spread, and path loss were estimated for different possible antenna orientations between the transmitter and the receiver, which include line‐of‐sight, obstructed line‐of‐sight, and non‐line‐of‐sight. The polarisation effects on path loss models and the delay and angular spread models based on the surface area of the environment are studied. The results will be valuable for the design of indoor millimetre wave cellular networks.