Path Loss Model Research Articles

Development of a Path Loss Model for Radio Wave Signals for some Selected Routes in Enugu State, Nigeria

Abstract: This research focuses on developing a path loss model for radio wave signals propagating in a wavelength range of 0.37–6.12 m along some selected routes in Enugu state, Nigeria. A cable television signal strength meter was used to measure signal strength at 2 km intervals along the routes. The television signals examined were those from NTA Enugu. Path loss values were derived from the received signal strength for each route. The path loss obtained was plotted against the distance and the result shows that the path loss increases with the distance along each of the routes. A path loss model was developed by analyzing the path loss variation with distance. The path loss values obtained from the developed model were plotted against the distance and compared with the measured path losses for the NTA signals to assess how accurate the model is for the path loss prediction at the study locations. Some statistical tools (RMSE, ME, and SDE) were used to validate the developed model. The model predicted the path loss of NTA-Enugu signals with RMSE very close to zero (0). In addition, the values of the developed path loss model for the NTA signals were compared with the values of other empirical models. The results show that the other empirical path loss models overestimate the path loss of NTA-Enugu signals. Keywords: Nigeria Television Authority (NTA), Path loss, Radio waves, Signal strength, Terrestrial television, Ultra High Frequency (UHF). PACs: 84.40.Ua.

Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage

Visible light communication (VLC) is one of the candidate technologies for the sixth generation (6G) networks. The path loss model is particularly important for link budget estimation and network planning in VLC. Due to the wideband nature and the extremely poor diffraction capacity of light, the path loss of the VLC channel is susceptible to wavelength dependence and blockage effect. In this paper, we propose a novel path loss model which can characterize the impact of wavelength dependence combined with mobile human blockage for both the single-LED (light emitting diode) and the multi-LED scenario. When there is no blockage in the channel, the multi-wavelength path loss under free space propagation is modeled with a small standard deviation of 0.262 in the single-LED scenario and a small root mean square error of 0.009 in the multi-LED scenario which indicates the high accuracy of the model. When considering the mobile human blockage, the blockage probability (BP) is modeled with full consideration of realistic human mobility and human body shadowing. The results indicate that the BP in single-LED scenario can reach 0.08, while the BP in multi-LED scenario is 0.022. This demonstrates that the distributed deployment of transmitters can effectively reduce the occurrence of the blockage state in VLC.

Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks

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.

The Current Progress and Future Prospects of Path Loss Model for Terrestrial Radio Propagation

The radio channel model is a major factor supporting the whole life cycle of the terrestrial radio system, including the demonstration, design, validation, operation, and so on. To improve the spectrum sharing and spectral efficiency in terrestrial radio services, we analyze three types of path loss models in detail: deterministic, empirical, and semi-empirical models, to meet the requirements of path loss modeling for supporting traditional band expansion and reuse. Then, we conduct a comparative analysis based on the characteristics of the current models. Furthermore, a preview of the future terrestrial path loss modeling methods is provided, including intelligent modeling processes and multi-model hybridization methods. Finally, we look forward to the potential technology that can be used in future wireless communication, such as terahertz communication, reconfigurable intelligent surface technology, and integrated communication and sensing technology. The above research can provide a reference for the development of terrestrial radio channel modeling, promoting the technologies of terrestrial channel modeling. We hope this paper will stimulate more interest in modeling terrestrial radio channels.

A novel method for estimating propagation pathloss in millimeter-wave communication systems

In millimeter wave communication systems, the construction of a path loss model that is close to empirical one plays an important role in planning coverage, system capacity, and link budgets. In this paper, we propose a novel approach applied unsupervised machine learning for propagation pathloss estimation in millimeter wave communication systems based on the iterative procedure of cooperative and iterative evaluation exchange (Co-IEE) algorithm. The propagation path loss models of the time city–an urban area in the center of Hanoi, Vietnam in both line-of-sight (LOS) and non line-of-sight (NLOS) are estimated and calculated using the proposed approach and then they are compared to the minimum mean square error (MMSE). The estimated results of the proposed model show the approximation with the optimum one returned by MMSE method. Moreover, the proposed model of estimating path loss can solve the problem of sensitivity with outliers existing in MMSE and give more choices for path loss exponents.

Vegetation Scattering Attenuation Characteristics of Terahertz Wave

A terahertz (THz) wave transmitted through vegetation experiences both absorption and scattering caused by the air molecules and leaves. This paper presents the scattering attenuation characteristics of vegetation in a THz range. The theoretical path loss model near the vegetation yields the average attenuation of THz waves in a mixed channel composed of air and vegetation leaves. Furthermore, a simplified model of the vegetation structure is obtained for generic vegetation types based on a variety of parameters, such as leaf size, distribution, and moisture content. Finally, based on specific vegetation species and different levels of air humidity, the attenuation characteristics under different conditions are calculated, and the influence of different model parameters on the attenuation characteristics is obtained.

Satellite to buoy IoT communications in the Arctic Ocean

Internet of things (IoT), to Arctic Ocean areas using Low Earth Orbit (LEO) satellites for communication to and from sensor units on the sea, has become increasingly important. It is challenging to close the link budget from a buoy on sea to a LEO satellite due to restrictions on the availability of power in such installations. Phenomena as scattering from the sea surface and small-scale fading, ionospheric scintillations, diffraction loss at low elevation angles, and power availability in small LEO satellites and models for analysis are described. It is of great importance to have a radio wave propagation model that accurately predicts path loss between an installation on the sea and a LEO satellite taking all loss mechanisms into account. In this paper, a path-loss model for satellite to buoy communications over the sea is described. In this model, maritime propagation phenomena in the radio link include free space loss, scintillation loss caused by the ionosphere, diffraction loss at low elevation angle, and scattering at the sea surface depending on the wave height and small-scale fading. Furthermore, a buoy on the sea surface with strong angular movement will cause a varying receive signal level depending on the antenna diagram. These phenomena are assessed. A link budget for the frequencies 433, 868, and 3,400 MHz is calculated for a LEO satellite at a height of 800 km.

Propagation channel characterization for mobile communication based on measurement campaign and simulation

Predicting the coverage area of mobile networks across various frequency bands is a critical concern in wireless communication. Each frequency band exhibits distinct propagation characteristics that directly influence the network coverage. Therefore, the use of the appropriate propagation models, whether through simulation or measurement-based approaches, is essential for accurate signal strength prediction in different environments and scenarios. In this study, we focus on the analysis and comparison of propagation channel characterization for mobile communication within the sub-6GHz frequency range. Our research involved outdoor measurement campaigns and ray-tracing simulations conducted at the Botswana International University of Science and Technology, covering distances up to 213 m. We then conducted an in-depth investigation and comparative analysis of the measured and simulated data for the propagation channel. Additionally, we derived a characterization model for the closed-in free space reference distance (CI) model based on measured and simulated data. Finally, we employed the root-mean-square error (RMSE) as a quantitative assessment of the performance of the characterized models. Notably, our findings revealed that the derived Close-in model based on measured data outperformed that of the simulations, exhibiting the lowest RMSE values of 3.56 dB, 4.20 dB, and 7.87 dB for location-1 line-of-sight, location-2 line-of-sight, and non-line-of-sight scenarios, respectively. These results hold significant potential for the development of precise path loss models that can effectively predict and optimize the coverage area of mobile communication systems across various real-world scenarios and environments.

Economic evaluation for 5G planning of distribution network considering transmission loss and vulnerability

Aiming at the difficulty of existing 4G networks to meet distribution network services, and the unclear economics of 5G in distribution network applications, an evaluation method of 5G communication planning economics for distribution networks considering the path loss model and comprehensive node vulnerability is proposed. Firstly, the exact coverage radius of 5G base stations is solved according to the signal transmission path loss model, and the objective function for solving the number and location of communication base stations in the distribution network is established. Secondly, a comprehensive communication node vulnerability evaluation model is established to judge whether each communication node meets the node vulnerability requirements under different communication modes, and to improve the communication reliability by installing additional fiber optic or micro base stations for nodes with too much vulnerability. Meanwhile, a comprehensive cost model for communication planning is established. Finally, the distribution network communication planning economic evaluation model is established. To verify the effectiveness of the method in this paper, the IEEE-123 topology model is used as an example to compare the economic evaluation of 5G and 4G in three scenarios: main urban areas, county town and township, respectively. According to the simulation results, using 5G communication in the distribution network reduces the total investment, operation and maintenance costs of the main urban area by 1.64 million yuan compared to 4G within 10 years. The total investment and maintenance cost of 5G in the county town for 10 years decreased by 2.16 million yuan compared to 4G. The cost of 5G in the township scenario is reduced by 860,000 yuan compared to 4G. According to the results of the comprehensive economic comparison, the comprehensive economic performance of 5G in the three scenarios is lower than that of 4G in the first few years due to the investment cost. In comparison, the comprehensive economic performance of 5G is better than that of 4G in the subsequent time due to the unique obtainable benefits of 5G, which concludes that 5G is better than that of 4G communication in all cases.

Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

A cost-effective LoRaWAN-based IoT localization method using fixed reference nodes and dual-slope path-loss modeling

Recently, RSSI-based localization has gained popularity for outdoor localization within the IoT ecosystem due to its cost-effectiveness, low-power and low-cost deployment, and ability to operate loT nodes for years on a single battery. However, this approach typically sacrifices accuracy, resulting in location estimates in the tens or hundreds of meters. For example, LoRaWAN communications have been widely used in IoT applications, being an attractive solution due to their long-range coverage, low implementation cost, and higher autonomy. However, RSSI-based LoRaWAN location methods suffer from multipath and fading interference which results in the near-far problem, impacting ranging accuracy for short and long distances, and thus leading to an overall decrease in the localization accuracy. In this article, we address the low accuracy of RSSI based localization challenge for outdoor IoT node localization by computing the Path Loss (PL) parameters of LoRaWAN, for short and long distances separately, thus providing a dual-slope PL model. In addition, some IoT nodes in specific locations are adopted as Reference Nodes (RNs), whose main task is to estimate the interference effect on the transmitted signals dynamically. The proposed model has been evaluated using a publicly available LoRaWAN dataset collected in urban areas in the city of Antwerp, Belgium, which serves as a benchmark for the evaluation of the results. Its effectiveness is assessed by simulation and comparison to the state-of-the-art. In addition, results are compared with the derived Cramer–Rao Lower Bound (CRLB). The localization error achieves a median error of 117 m and a mean error of 236 m.

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.

Coverage Analysis of LoRa and NB-IoT Technologies on LPWAN-Based Agricultural Vehicle Tracking Application

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms.

Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning

Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN). Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wireless channel are still numerous and relevant. These studies are used in the design and planning of wireless networks deployed in various environments, including those with abundant vegetation. This paper analyzes the performance of three vegetation propagation models, ITU-R, FITU-R, and COST-235, and compares them with path loss measurements conducted in a cassava field in Sincelejo, Colombia. Additionally, we applied four machine learning techniques: linear regression (LR), k-nearest neighbors (K-NN), support vector machine (SVM), and random forest (RF), aiming to enhance prediction accuracy levels. The results show that vegetation models based on traditional approaches are not able to adequately characterize attenuation, while models obtained by machine learning using RF, K-NN, and SVM can predict path loss in cassava with RMSE and MAE values below 5 dB.

Agile gravitational search algorithm for cyber-physical path-loss modelling in 5G connected autonomous vehicular network

Based on the characteristics of the 5 G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5 G (or the so-called 6 G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5 G and Beyond 5 G (or 6 G) will operate over millimetre-wave (mmWave) bands. These network standards require suitable path loss (PL) models to guarantee effective communication over the network standards of CAV. The existing PL models suffer heavy signal losses and interferences at mmWave bands and may not be suitable for cyber-physical (CP) signal propagation. This paper develops an Agile Gravitational Search Algorithm (AGSA) that mitigates the PL and signal interference problems in the 5G–NR network for CAV. On top of that, a modified Okumura-Hata model (OHM) suitable for deployment in CP terrestrial mobile networks is derived for the CAV-CPS application. These models are tested on the real-world 5 G infrastructure. Results from the simulated models are compared with measured data for the modified, enhanced model and four other existing models. The comparative evaluation shows that the modified OHM and AGSA performed better than existing OHM, COST, and ECC-33 models by 90%. Also, the modified OHM demonstrated reduced signal interference compared to the existing models. In terms of optimisation validation, the AGSA scheme outperforms the Genetic algorithm, Particle Swarm Optimisation, and OHM models by at least 57.43%. On top of that, the enhanced AGSA outperformed existing PL (i.e., Okumura, Egli, Ericson 999, and ECC-33 models) by at least 67%, thus presenting the potential for efficient service provisioning in 5G-NR driverless car applications.

Application of Particle Filter in Path-loss Modelling

Application of Particle Filter in Path-loss Modelling

BLE beacons for sample position estimation in a life science automation laboratory

AbstractEstimation of the sample position is essential for working process monitoring and management in the life science automation laboratory. Bluetooth low-energy (BLE) beacons have the advantages of low price, small size and low energy consumption, which make them a promising solution for sample position estimation in the automated laboratory. Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength (RSS) data. However, most of the research depends on intensive beacon installation. Proximity estimation, which depends entirely on one beacon, is more suitable for sample position estimation in large automated laboratories. The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model (PLM), which is a widely used radio wave propagation model-based proximity estimation method. In this paper, BLE sensing devices for sample position estimation are proposed. The BLE beacon-based proximity estimation is discussed in the framework of machine learning, in which the support vector regression (SVR) is utilized to model the nonlinear relationship between the RSS data and distance, and the Kalman filter is utilized to decrease the RSS data deviation. The experimental results over different environments indicate that the SVR outperforms the PLM significantly, and provides 1 m absolute errors for more than 95% of the testing samples. The Kalman filter brings benefits to stable distance predictions. Apart from proximity-based sample position estimation, the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

Applying an Adaptive Neuro-Fuzzy Inference System to Path Loss Prediction in a Ruby Mango Plantation

The application of wireless sensor networks (WSNs) in smart agriculture requires accurate path loss prediction to determine the coverage area and system capacity. However, fast fading from environment changes, such as leaf movement, unsymmetrical tree structures and near-ground effects, makes the path loss prediction inaccurate. Artificial intelligence (AI) technologies can be used to facilitate this task for training the real environments. In this study, we performed path loss measurements in a Ruby mango plantation at a frequency of 433 MHz. Then, an adaptive neuro-fuzzy inference system (ANFIS) was applied to path loss prediction. The ANFIS required two inputs for the path loss prediction: the distance and antenna height corresponding to the tree level (i.e., trunk and bottom, middle, and top canopies). We evaluated the performance of the ANFIS by comparing it with empirical path loss models widely used in the literature. The ANFIS demonstrated a superior prediction accuracy with high sensitivity compared to the empirical models, although the performance was affected by the tree level.

Experimental study on low-altitude UAV-to-ground propagation characteristics in campus environment

In recent years, unmanned aerial vehicle (UAV)-to-ground wireless channels have become focused areas. Thus a better understanding of UAV-to-ground wireless channel characterizations is critical. The large-scale fading characterization is widely analyzed in many research works. Nevertheless, the small-scale fading characterization of low-altitude UAV-to-ground propagation has not been fully modeled. In this work, the close-in (CI) free space and floating-intercept (FI) path loss models are implemented to characterize large-scale fading. The large-scale parameters, i.e., path loss exponent (PLE), shadow fading standard deviation (STD), and shadow fading correlation coefficient, are carefully extracted, analyzed, and compared. Regarding small-scale fading, Akaike’s Information Criteria (AIC) is used to find the most suitable fading distribution. In addition, fade depth (FD), level crossing rate (LCR), and average fade duration (AFD) are also calculated. The quantitative results and comparisons show a noticeable difference with ground propagation. The parameterized models are helpful for further understanding and deployment of UAV-to-ground wireless communication systems.

Communication performance optimization of coal mine goaf LoRa AD hoc network sensor system based on tree topology

Real-time accurate monitoring of goaf gas, temperature, and other parameters is of key significance for the prevention and control of spontaneous coal combustion in the goaf. In this paper, a tree network and communication protocol for LoRa technology are proposed to solve the problems of small communication coverage and short endurance of the traditional goaf wireless monitoring system. In this paper, a geometric topology structure is designed for wireless sensor nodes to find relay nodes through the path loss model, and an energy-saving optimization algorithm routing scheme is designed. In addition, LoRa AD hoc network experiment platform and upper computer system are developed and designed. Through numerical analysis and experimental results in coal mines, it is proved that the proposed method significantly reduces the energy consumption of the whole network. The coverage radius is increased by 300% compared with the traditional star wireless sensor system, which makes the endurance time and communication quality have higher reliability.