Radio Propagation Model Research Articles

CONSIDERATION OF MULTIPATH IN MODELING THE FIELD OF SECONDARY ELECTROMAGNETIC RADIATION ON INFORMATIZATION OBJECTS

The paper considers the features of the formation of the resulting field of spurious electromagnetic radiation at informatization objects, taking into account the multipath propagation of radio waves, evaluates the achievable error values for determining the attenuation coefficient using known methods for determining the attenuation of the field of spurious electromagnetic radiation based on a deterministic model of radio wave propagation inside the building. The need for a significant refinement of the method for determining the attenuation coefficient is shown, since, in a known form, it is suitable only for measurements in free space and gives very significant errors in urban conditions and inside buildings, or the use of adaptive reception methods when receiving signals (for example, diversity reception), which allows to significantly reduce the level of interference dips in the resulting electromotive force.

Первый сравнительный анализ метеорного эхо и спорадического рассеяния, идентифицированных самообучившейся нейронной сетью по данным радаров EKB и MAGW ИСЗФ СО РАН

The paper describes the current version (v.1.1) of the algorithm for automatic classification of signals received by ISTP SB RAS decameter coherent scatter radars. The algorithm is a self-learning neural network that determines the type of scattered signals from the results of physical modeling of radio wave propagation, using radar data and international reference models of the ionosphere and geomagnetic field. According to MAGW and EKB ISTP SB RAS radar data for 2021, the algorithm self-learns to classify scattered signals into initially unknown classes based on physically interpreted parameters of radio wave propagation and data measured by the radar, with 15 frequently observed out of 20 possible hidden classes identified, 14 of which can be interpreted from a physical point of view. To demonstrate the operation of the algorithm, we present the first statistical analysis of observations of signals assigned by the algorithm to classes which we interpret as scattering by meteor trails and scattering with the sporadic E layer respectively. Through a statistical analysis of EKB and MAGW radar data during 2021–2022, we demonstrate the range-altitude characteristics of signals of these types. A correlation is shown between the hourly average numbers of observations of both classes, as well as between the hourly average line-of-sight velocities obtained for both classes. The results obtained make it possible to interpret these classes as a meteor echo and sporadic scattering respectively, and to use radar data to study the interaction between the neutral atmosphere (studied from meteor scattering data) and the lower ionosphere (studied from observations of sporadic scattering). Currently, this classification algorithm works in ISTP SB RAS radars in automatic mode.

The first comparative analysis of meteor echo and sporadic scattering identified by a self-learned neural network in EKB and MAGW ISTP SB RAS radar data

The paper describes the current version (v.1.1) of the algorithm for automatic classification of signals received by ISTP SB RAS decameter coherent scatter radars. The algorithm is a self-learning neural network that determines the type of scattered signals from the results of physical modeling of radio wave propagation, using radar data and international reference models of the ionosphere and geomagnetic field. According to MAGW and EKB ISTP SB RAS radar data for 2021, the algorithm self-learns to classify scattered signals into initially unknown classes based on physically interpreted parameters of radio wave propagation and data measured by the radar, with 15 frequently observed out of 20 possible hidden classes identified, 14 of which can be interpreted from a physical point of view. To demonstrate the operation of the algorithm, we present the first statistical analysis of observations of signals assigned by the algorithm to classes which we interpret as scattering by meteor trails and scattering with the sporadic E layer respectively. Through a statistical analysis of EKB and MAGW radar data during 2021–2022, we demonstrate the range-altitude characteristics of signals of these types. A correlation is shown between the hourly average numbers of observations of both classes, as well as between the hourly average line-of-sight velocities obtained for both classes. The results obtained make it possible to interpret these classes as a meteor echo and sporadic scattering respectively, and to use radar data to study the interaction between the neutral atmosphere (studied from meteor scattering data) and the lower ionosphere (studied from observations of sporadic scattering). Currently, this classification algorithm works in ISTP SB RAS radars in automatic mode.

Bayesian Active Learning for Received Signal Strength-Based Visible Light Positioning

Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength (RSS) and fixed LED-based light transmitters. Classical VLP approaches use lateration or angulation based on a wireless propagation model to obtain location estimations. However, previous work has shown that machine learning models such as Gaussian processes (GP) achieve better performance and are more robust in general, particularly in presence of non-ideal environmental conditions. As a downside, Machine Learning (ML) models require a large collection of RSS samples, which can be time-consuming to acquire. In this work, a sampling scheme based on active learning (AL) is proposed to automate the vehicle motion and to accelerate the data collection. The scheme is tested on experimental data from a RSS-based VLP setup and compared with different settings to a simple random sampling.

LTE wireless network coverage optimisation based on corrected propagation model

Abstract With the development of technology, the 5th-generation mobile communication technology is becoming more and more mature, and the LTE wireless network is in the peak of application. Although the current wireless propagation model has good adaptability, the prediction accuracy of signal propagation in various specific environments is low; therefore, the wireless propagation model needs to be corrected. The LTE wireless network propagation model was optimised by using the neural network algorithm and corrected by combining with the actual geomorphic characteristics of the experimental area so as to improve the accuracy of simulation prediction of the signal in the service coverage area by the propagation model and provide reference for wireless network planning.

Comparison of radio propagation models in five LTE coverage cells in Riobamba

This article performs a comparative analysis of the power intensity levels measured with the Network Cell Info Lite application and the performance of the different propagation models: Log-Normal, Okumura Hata, COST 231, Walfish Bertoni, and SUI in the 4G LTE Frequency Band. The study was conducted in five LTE coverage cells located in the southern area of the city of Riobamba. The model that best fits each cell was chosen by means of absolute error analysis, thus obtaining an empirical correction factor for the proposed models. For the analysis of the absolute error, three measurement campaigns were carried out with 50 samples where the mean value was obtained. After applying the aforementioned models, the Log-Normal model yielded the most favorable results, being the one that achieved the best adaptation in Riobamba, since the power levels vary in the range (-80; -106) dBm at a coverage area not exceeding 200m.

Vision-aided self-calibration of a wireless propagation model for crowdsourcing-based indoor localization

Wireless localization method can take advantage of the existing Wi-Fi infrastructures to obtain indoor position information. However, an essential wireless localization bottleneck is the intensive geo-tagging, calibration and updating work of the collected wireless data. In this case, this study develops a vision-aided self-calibration method for wireless localization without manual supervision. This method localizes a pedestrian using a visual coordinate transformation model (V-CTM). The visual location is matched with the simultaneously collected wireless data to automatically generate training data for a wireless propagation model. A wireless self-calibration scheme is proposed to self-train the model using a Bi-LSTM and a principle of separate training. Experiments have demonstrated that the accuracy of the generated training dataset was 0.2 m using the V-CTM. The localization experiment achieved an accuracy of 2 m using the constructed propagation model. Meanwhile, the time and labour consumption for model calibration and updating can be significantly reduced.

Bluetooth low energy indoor localization for large industrial areas and limited infrastructure

Systems using Bluetooth Low Energy (BLE) communication are widely used in various industrial applications, which benefit from the efficient low-power operation and pervasive availability of BLE transceivers in a large number of devices and sensors. It is no different in Smart Factory and Smart Farming settings, where BLE systems are already used for asset monitoring, management, tracking, and localization. The existing BLE-based localization systems aim at high accuracy and precision using radio propagation models and multilateration, or radio fingerprinting. Both methods use the received signal strength indicator (RSSI) measurements and the dependency of RSSI on the distance between the transmitter and the receiver. Because RSSI measurements are highly inaccurate and susceptible to radio propagation phenomena, high localization accuracy is only possible if the anchor devices are densely deployed, and the propagation conditions are stable. Unfortunately, these requirements do not hold in industrial systems where radio propagation is complex, the number of anchors is limited, and propagation conditions change because the environment is dynamic. Therefore, localization methods for industrial applications have to balance system complexity (e.g., anchor density, energy cost) and localization accuracy so that practical benefits can be attained. This paper presents a set of localization algorithms that require limited infrastructure, have low complexity, and can provide valuable location information at low costs. The proposed algorithms were verified in a real-life Smart Farming application where BLE is used to monitor well-being of farm animals. The animal localization was integrated with the already existing system and proved to operate reliably despite system-level constraints and varying propagation conditions — interference, signal attenuation, and unavailability of raw radio signal measurements. The proposed approaches allow localizing animals in the cowshed of 1600m2, using only 10 anchors with an average positioning error below 8m. The algorithms are based on signal strength measurement, so they can be applied to other radio technologies.

Bounds Calculation Method of Electromagnetic Availability Zone of Radio Emission Source

The article deals with the problem of determining the electromagnetic availability zone of the radio transmitter of the radio communication channel in the UHF/VHF range on the terrain map of the area. This problem was solved using well-known mathematical models of radio signal propagation, depending on the characteristics of the specific area. Based on the wave algorithm, a bounds calculation method of electromagnetic availability zone is proposed, using a digitized radiation pattern of radio means. The conducted calculations show the effectiveness of the method in the tasks of assessing the performance of a mobile radio communication channel and its security at local combat operations in the conditions of operation of an enemy radio reconnaissance receiver or a radio interference system.

Site-Specific Radio Propagation Model for Macrocell Coverage at Sub-6 GHz Frequencies

This article presents a radio propagation model for macrocell coverage predictions in built-up areas for sub-6-GHz frequency band. The proposed two-ray model is a combination of various individual propagation models available in the literature, namely the discrete mixed Fourier transform split-step parabolic-equation (DMFT-SSPE) to characterize reflection and diffraction phenomena due to the presence of buildings and terrain irregularities, the building-transmission model (BTM) to account for the signal attenuation through the nearby buildings and the International Telecommunication Union of Radio-communications (ITU-R) model for attenuation in vegetation to weight the excess attenuation caused by the surrounding vegetation. The performance of the proposed modeling approach was assessed against measurement results obtained from an extensive measurement campaign conducted in Rio de Janeiro, Brazil. The receiver (RX) antenna was assembled on an automotive van, and it was made to travel around the city recording the received signal level. Several drive tests were conducted, including different transmitter (TX) locations and scenarios, and at various frequencies foreseen for 5G systems, namely 750 MHz, 2.5 GHz, and 3.5 GHz.

Sub-GHz Two-Way Ranging Based on Phase Detection for Remote Keyless Entry Systems

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle.

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review.

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

Over-the-Horizon Channel of Radio Communication at VHF Band via Artificial Plasma Clouds

Metal cesium (Cs) vapor, released at an altitude in the upper atmosphere, generates an artificial space plasma cloud with high electron density. It can act as a scatter to realize radio communication across an over-the-horizon distance. A full-life model of radio propagation via an artificial plasma cloud is established based on its space–time evolution in this article. In addition, this model is verified by the sounding data of the rocket release experiment carried out in the United States. The multipath effect of plasma cloud scattering is studied by using ray tracing, and then, the coherent bandwidth is estimated for this spatial channel. Also, the theoretical channel model of artificial plasma cloud is established by using its space–time evolution and atmospheric wind field parameters. Considering common modulation modes and receiver noise levels, the potential communication performance is evaluated by using the proposed channel model, such as the transmission rate, the bit error rate (BER), and the available time for building up and maintaining communication links. It is shown that the maximal communication rate is up to 200 kb/s, BER overall maintains below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup> , and the available time for a single release is about 30–60 min; for the case of this article, they are release mass of 2–10 kg, communication distances of 400–1200 km, and radio frequencies of 30–70 MHz at the VHF band. This demonstrates that the artificial channel can propagate radio waves at a much higher frequency, wider bandwidth, and higher rate than those of HF communication. Hopefully, it could be a solution to overcome the intolerable shortcoming and adverse effects on HF communication due to ionospheric disturbances especially in cases of maritime, emergency, and minimum communications

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

Athlete Health Status Assessment Based on Edge Computing Model and Large-Scale Multimedia Health Data Analysis.

This study explores the athlete's health status evaluation system based on an edge computing model and large-scale multimedia health data analysis in order to improve the assessment effect of athletes' current health state. In addition to that, this research builds a wireless signal propagation model in edge areas using bicubic polynomial interpolation, improves the accuracy of DEM data by using bicubic polynomial interpolation, and digitally represents the sports field by utilizing DEM data. In addition to that, the model and DEM data are utilized in this paper to realize the site selection process for base station networking. The experimental analysis demonstrates that the athletes' health status evaluation system suggested in this research, which is based on the edge computing model and the large-scale multimedia health data analysis, has a good effect on the assessment of athletes' health.

Development of Machine Learning-Based Radio Propagation Models and Benchmarking for Mobile Networks

Path loss prediction plays an important role in assisting network operators to build quality mobile networks by indicating optimal coverage and interference performances. Common empirical models such as the Cost 231-Hata model and Cost 231-Walfisch Ikegami model have been used to predict path loss due to their simplicity and robustness; however, these models do not provide reliable accuracy for small coverage infrastructures, especially in LTE and 5G networks. Therefore, as an alternative method, deterministic models (e.g., ray-tracing models) are widely used for small coverage applications–however, these also require precise 3D digital maps, street structures, and building surface reflection indices, etc. which can be costly and need frequent maintenance. In this paper, an alternative model based on machine learning algorithms is proposed and the algorithms' prediction accuracies are evaluated. The machine learning approach trains models using signal strength data upon which they draw generalizations. The performance of these models is compared to that of the empirical models, and the results reveal that the machine learning models perform significantly better, with Random Forest outperforming other algorithms.

EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model

Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined with the design and operation of next-generation wireless networks. Machine-learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of wireless channel characteristics in a computationally efficient way. However, most of the existing research works on the ML-based propagation models focus on outdoor propagation modeling, while indoor data-driven propagation models remain site-specific with limited scalability. In this article, we present an efficient and credible ML-based radio propagation modeling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder–decoder can be trained to replicate the results of a ray tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decoding it as the path loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. In addition, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its preestimate weights, allowing it to make predictions that are consistent with measurement data.

Структурно-многолучевой подход к разработке пространственно-временной модели одномодового декаметрового канала связи с диффузной многолучевостью

A structural-multipath approach has been developed for constructing a spatiotemporal model of a single-mode decameter communication channel, which makes it possible to determine the two-frequency spatial correlation function of a single-mode decameter communication channel and to establish the dependences of frequency and spatial correlation intervals of fading on the operating frequency, diffuse ionosphere parameters, and radio link geometry. The method for constructing this model of a communication channel using the structural multipath approach includes four stages: 1) development of a multibeam spatiotemporal decameter model of a communication channel; 2) development of a radiophysical space-time model of radio wave propagation in a decameter radio link, taking into account the influence of inhomogeneities (diffusion) of the ionosphere based on the method of smooth perturbations; 3) identification of multipath and radiophysical models to determine the two-frequency spatial correlation function of the decameter of the communication channel; 4) determining the dependence of frequency and spatial correlation intervals of fading in a single-mode decameter communication channel on the choice of operating frequency, diffuse ionosphere parameters, and radio link geometry.

Guest Editorial Artificial Intelligence in Radio Propagation for Communications

The era of wireless communications began at the turn of the 20th century, when Guglielmo Marconi used electromagnetic waves to transmit telegraph signals from ships to stations onshore <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . To understand how radio signals propagate is critical for wireless communication system design. With the development of wireless communications, much effort has been made to characterize radio propagation in different frequency bands and physical environments. Radio propagation and wireless channel modeling are essential for communication system simulation, channel emulator design, wireless system planning and optimization, and the development of regulations and standards in wireless communications <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> .

Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps

Numerous indoor positioning technologies and systems have been proposed to localize people and objects in large buildings. Wi-Fi and Bluetooth positioning systems using fingerprinting have gained popularity, due to the wide availability of existing infrastructure. Unfortunately, the implementation of fingerprinting-based methods requires time-consuming radio surveys to prepare databases (RSSI maps) that serve as a reference for the radio signal. These surveys must be conducted for each individual building. Here, we investigate the possibility of using simulated RSSI maps with fingerprinting-based indoor localization systems. We discuss the suitability of the two popular radio wave propagation models for the preparation of RSSI reference data: ray tracing and multiwall. Based on an analysis of several representative indoor scenarios, we evaluated the performance of RSSI distribution maps obtained from simulations versus maps obtained from measurement campaigns. An experimental positioning system developed by the authors was used in the study. Based on Bluetooth Low Energy beacons and mobile devices (smartphones), the system uses fingerprinting followed by a particle filter algorithm to estimate the user’s current position from RSSI measurements and a reference spatial RSSI distribution database for each Bluetooth beacon in the building. The novelty of our contribution is that we evaluate the performance of the positioning system with RSSI maps prepared both from measurements and using the two most representative indoor propagation methods, in three different environments in terms of structure and size. We compared not only the three RSSI maps, but also how they influence the performance of the fingerprint-based positioning algorithm. Our original findings have important implications for the development of indoor localization systems and may reduce deployment times by replacing reference measurements with computer simulations. Replacing the labor-intensive and time-consuming process of building reference maps with computer modeling may significantly increase their usefulness and ease of adaptation in real indoor environments.