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The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors' contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real‐world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM‐ANN) self‐organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100‐m range can be achieved. With this result, the authors conclude that we can use LM‐ANN for the wireless sensor network SON model in the jungle environment. [ABSTRACT FROM AUTHOR]

Abstract The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors’ contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real‐world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM‐ANN) self‐organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100‐m range can be achieved. With this result, the authors conclude that we can use LM‐ANN for the wireless sensor network SON model in the jungle environment.

Abstract Propagation models are essential for the prediction of received signal strength and the planning of wireless systems in a given environment. The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnels. However, carrying out simulations for large‐scale environments is still computationally expensive. A convolutional neural network (CNN)‐based propagation model, which can provide high‐fidelity received signal strength prediction based on results from low‐cost VPE simulations, is proposed. A thorough study of the generalisability, including both interpolation and extrapolation capabilities, of the proposed CNN model is conducted. It is found that the proposed model can achieve significant computational savings while maintaining acceptable accuracy, and its performance is validated in both simulations and actual tunnel cases.

Propagation models are essential for the prediction of received signal strength and the planning of wireless systems in a given environment. The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnels. However, carrying out simulations for large‐scale environments is still computationally expensive. A convolutional neural network (CNN)‐based propagation model, which can provide high‐fidelity received signal strength prediction based on results from low‐cost VPE simulations, is proposed. A thorough study of the generalisability, including both interpolation and extrapolation capabilities, of the proposed CNN model is conducted. It is found that the proposed model can achieve significant computational savings while maintaining acceptable accuracy, and its performance is validated in both simulations and actual tunnel cases. [ABSTRACT FROM AUTHOR]

It is crucial for vehicular communications to optimize the field strength coverage on roads, which can be illustrated by the radio map (RM). In this paper, a deep convolutional neural network‐long short‐term memory (DCNN‐LSTM) model for the construction of the road RM is proposed. First, a multi‐modal dataset is built, including the measured field strength, longitude, latitude and elevation data obtained at various measurement points, as well as the outline maps of the buildings that are close to the measurement points. Second, the DCNN‐LSTM model is designed to extract electromagnetic and geographical features separately from the multi‐modal input data and fuse them to predict the field strength at unmeasured points. Finally, the road RM is constructed using the measured and predicted field strength data. The simulation results demonstrate that the field strength prediction accuracy of the proposed model is better than that of the ordinary kriging and the traditional deep learning models. Moreover, it exhibits robust performance even in noise interference environments. The constructed RM also provides an intuitive visualization of field strength coverage along the target road. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments.

Abstract Radio wave propagation modelling in railway environments is of fundamental importance in designing reliable train communication systems. Parabolic equation (PE) methods have been widely applied to the modelling of wave propagation in tunnels due to their high computational efficiency and fidelity. The finite‐difference parabolic equation (FDPE) and the split‐step parabolic equation (SSPE) methods are two commonly used approaches to solve PE numerically. However, the relevant literature is still missing a comprehensive study of their performance, including the selection of parameters such as discretisation steps and the tradeoffs involved in terms of their accuracy and efficiency, especially as current wireless systems shift to high frequencies. In this study, a systematic analysis of the error and computational complexity of the FDPE and SSPE methods for radio wave propagation modelling in tunnels is provided. Guidelines for the choice of their parameters are provided, and their performance is demonstrated through both numerical examples and experimental measurements in actual tunnel cases.

Narrow-band Internet of Things (NB-IoT) and LTE CAT-M are part of low-power wide-area network (LPWAN) access technologies that provide long-range and energy-efficient network access to IoT devices. In this study, we conducted empirical measurements on NB-IoT and LTE CAT-M networks inside a street manhole. Street manholes are integral to smart city structures, where sensor implementation is essential. Our study demonstrates how to derive the network parameters from system information (SI) messages. More precisely, as we measured three commercial operators, we show the parameters and their influence on enhanced coverage level (ECL) repetitions. We consider signal strength and quality measurements at different locations inside the manhole. Our results indicate that when designing sensor networks for urban infrastructure monitoring, NB-IoT and LTE CAT-M should have different coverage margins in the implementation. Robust and low-cost NB-IoT construction leads to considering up to 2.4 dB higher coverage margins than LTE CAT-M. In this work, we analyze various relevant environmental conditions when evaluating and understanding underground signal propagation.

Wireless technologies suitable for Search and Rescue (SaR) operations are becoming crucial for the success of such missions. In avalanche scenarios, the snow depth and the snowpack profile significantly influence the wireless propagation of technologies used to locate victims, such as ARVA (in French: appareil de recherche de victimes d’avalanche) systems. In this work, we explore the potential of LoRa technology under challenging realistic conditions. For the first time, we collect radiopropagation data and the contextual snow profile when the transmitter is buried over a $50\times 50$ m area resembling a typical human-triggered avalanche. Specifically, we detail the methodology adopted to collect data through three test types: cross, maximum distance, and drone flyover. The data are annotated with accurate ground truth which allows evaluating localization algorithms based on the RSSI (received signal strength indicator) and SNR (signal-to-noise ratio) of LoRa units. We conducted tests under various environmental conditions, ranging from dry to wet snowpacks. Our results demonstrate the high quality of the LoRa channel, even when the target is buried at a depth of 1 meter in snow with a high liquid water content. At the same time, we quantify the effects of two main degrading factors for the LoRa propagation: the amount of the snow and the liquid water content existing in the snowpack profiles.

The map-based hybrid channel model is one of the candidates for managing stochastic components above millimeter waves. This model has been used in technical reports published by several well-known standardization organizations. This paper compares simulation using the map-based hybrid channel model with measurements at 28 GHz urban microcell street canyon line-of-sight environment in terms of delay spread and angular spread. With a power threshold of −50 dB, the simulation results of delay spread and angular spread were in good agreement with the measurement results.

To simulate radio propagation in a dynamic indoor environment such as a factory operating with moving people and automated guided vehicles (AGV), we devised a technique to determine shielding losses by moving objects based on ray tracing. For a moving object environment, conventional ray-tracing methods require simulation of each snapshot of a moving object. This conventional technique requires too much time for real-time predictions. The proposed method first calculates the static environment using the ray-tracing method without the moving object, and then the shielding loss caused by a moving object is included through post-processing. To enhance efficiency, 3D path clustering was employed, which significantly reduced the total number of paths and computation time. For comparison, we measured the radio-propagation environment of an existing Japanese factory. The root-mean-square errors (RMSEs) and correlation coefficients of the proposed and conventional methods were compared based on the experimental results, wherein the indicators of the proposed method were comparable to those of the conventional method even though the number of paths was reduced. Furthermore, the proposed method reduces the computation time to approximately 5% of that of the conventional method.

With the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments. [ABSTRACT FROM AUTHOR]

Abstract In received signal strength (RSS) based radio source localisation, RSS measurements can be converted to the squared distance estimates between the emission source and the sensors to construct a system of pseudolinear equations, allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. The WLLS estimators are widely applied in practice because of their simplicity and computational efficiency. Nevertheless, the major challenge of this approach lies in estimating the squared distance from RSS measurements governed by the log‐normal shadowing effect. A pseudo conditional distribution (PCD) of the squared distance between the emission source and the sensor is introduced first, given the RSS measurement at each sensor. Then, the authors propose a series of new WLLS location estimators, using three typical statistical characteristics, that is, mean, median, and mode, of the PCD. Analysis of their estimation performance are also provided through performance rankings in terms of their mean square errors and covariances. It is found that estimation performance of the PCD‐induced WLLS estimators heavily depends on the choice of the statistical characteristic of the PCD and different choices lead to estimators with better, worse, or equal performance. Numerical examples show that the proposed mode‐WLLS estimator always performs better than the existing WLLS estimators, and also better than the existing convex optimisation based algorithms in most cases but with much less computations.

Abstract Accurate radio wave prediction models are necessary for the innovation of mobile communication technology and the deployment of wireless sensor networks in railways. The parabolic wave equation (PWE) method is one commonly used method to predict the electromagnetic characteristic of long railway tunnels. However, the advent of novel antennas and technologies (e.g., beam steering, beamforming) has posed new challenges to PWE‐based models such as effective means of taking tilted beam patterns into account. This study addresses such a challenge by using tilted Gaussian beams to embed antennas with oriented patterns or tilted beam directions into PWE‐based models. The proposed approach does not reduce the simulation efficiency of PWE itself. The validity and practicability of the technique are demonstrated by comparing against ray‐tracing as well as experimental measurements in various tunnel environments.

Abstract Accurate prediction of rain attenuation is critical for the design of terrestrial line‐of‐sight link systems above 5 GHz, particularly for millimetre‐wave frequency. Anomalous behaviour in the adjustment factor in existing rain attenuation prediction models is significantly greater than 1 at short distances and has been reported. To solve this problem and improve the accuracy, a rain attenuation prediction model for terrestrial line‐of‐sight links is proposed. The total rain attenuation described in the model accounts for the contributions of wet antenna attenuation and path rain attenuation. In addition, a rainfall rate adjustment factor is proposed. The proposed model has a root‐mean‐square error of 21.54% in prediction, which is an improvement compared to other existing models. Because the rainfall rate adjustment factor used in the model approaches 1 at short distances, the model can be applied to rain attenuation prediction for short‐distance links. The results will assist designers to design link margins more accurately.

In received signal strength (RSS) based radio source localisation, RSS measurements can be converted to the squared distance estimates between the emission source and the sensors to construct a system of pseudolinear equations, allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. The WLLS estimators are widely applied in practice because of their simplicity and computational efficiency. Nevertheless, the major challenge of this approach lies in estimating the squared distance from RSS measurements governed by the log‐normal shadowing effect. A pseudo conditional distribution (PCD) of the squared distance between the emission source and the sensor is introduced first, given the RSS measurement at each sensor. Then, the authors propose a series of new WLLS location estimators, using three typical statistical characteristics, that is, mean, median, and mode, of the PCD. Analysis of their estimation performance are also provided through performance rankings in terms of their mean square errors and covariances. It is found that estimation performance of the PCD‐induced WLLS estimators heavily depends on the choice of the statistical characteristic of the PCD and different choices lead to estimators with better, worse, or equal performance. Numerical examples show that the proposed mode‐WLLS estimator always performs better than the existing WLLS estimators, and also better than the existing convex optimisation based algorithms in most cases but with much less computations. [ABSTRACT FROM AUTHOR]

Abstract For 5G beyond and/or 6G mobile services, a new spectrum at frequencies above 100 GHz has received great attention lately. To characterise the similarities and the differences between frequencies below 100 GHz and above 100 GHz, this paper provides analyses of multi‐frequency propagation characteristics based on wideband measurements at 28, 38, 71, 82, and 159 GHz, with an emphasis on the sub‐THz (i.e. the 159 GHz) propagation characteristics. All the frequency measurements were conducted in the same cubicle office room environment by locating the transmitter and the receiver at the same positions. The measurement results show that the path loss and the shadow fading characteristics do not exhibit significant differences depending on the frequency range from 28 to 159 GHz. However, the delay spread shows a generally decreasing trend as the frequency increases. Additionally, angular domain analyses were conducted with the 159 GHz measurements.

Abstract Positioning technology based on received signal strength (RSS) fingerprints has emerged as a vital research topic in indoor localization due to its high accuracy and low cost. Increasing the density of offline fingerprints collected can effectively improve localization accuracy in the online stage, but it will result in astronomical collection costs. RSS reconstruction using generative models is an efficient solution to dramatically reduce fingerprint collection costs while maintaining excellent accuracy. In this paper, the authors propose a pix2pix generative adversarial network based RSS reconstruction model, which can generate a dense RSS fingerprint map based on only the location information of the wireless access point. The accuracy and efficiency of the proposed model are verified in an indoor environment.

Accurate radio wave prediction models are necessary for the innovation of mobile communication technology and the deployment of wireless sensor networks in railways. The parabolic wave equation (PWE) method is one commonly used method to predict the electromagnetic characteristic of long railway tunnels. However, the advent of novel antennas and technologies (e.g., beam steering, beamforming) has posed new challenges to PWE‐based models such as effective means of taking tilted beam patterns into account. This study addresses such a challenge by using tilted Gaussian beams to embed antennas with oriented patterns or tilted beam directions into PWE‐based models. The proposed approach does not reduce the simulation efficiency of PWE itself. The validity and practicability of the technique are demonstrated by comparing against ray‐tracing as well as experimental measurements in various tunnel environments. [ABSTRACT FROM AUTHOR]

Accurate prediction of rain attenuation is critical for the design of terrestrial line‐of‐sight link systems above 5 GHz, particularly for millimetre‐wave frequency. Anomalous behaviour in the adjustment factor in existing rain attenuation prediction models is significantly greater than 1 at short distances and has been reported. To solve this problem and improve the accuracy, a rain attenuation prediction model for terrestrial line‐of‐sight links is proposed. The total rain attenuation described in the model accounts for the contributions of wet antenna attenuation and path rain attenuation. In addition, a rainfall rate adjustment factor is proposed. The proposed model has a root‐mean‐square error of 21.54% in prediction, which is an improvement compared to other existing models. Because the rainfall rate adjustment factor used in the model approaches 1 at short distances, the model can be applied to rain attenuation prediction for short‐distance links. The results will assist designers to design link margins more accurately. [ABSTRACT FROM AUTHOR]

For 5G beyond and/or 6G mobile services, a new spectrum at frequencies above 100 GHz has received great attention lately. To characterise the similarities and the differences between frequencies below 100 GHz and above 100 GHz, this paper provides analyses of multi‐frequency propagation characteristics based on wideband measurements at 28, 38, 71, 82, and 159 GHz, with an emphasis on the sub‐THz (i.e. the 159 GHz) propagation characteristics. All the frequency measurements were conducted in the same cubicle office room environment by locating the transmitter and the receiver at the same positions. The measurement results show that the path loss and the shadow fading characteristics do not exhibit significant differences depending on the frequency range from 28 to 159 GHz. However, the delay spread shows a generally decreasing trend as the frequency increases. Additionally, angular domain analyses were conducted with the 159 GHz measurements. [ABSTRACT FROM AUTHOR]

This paper details the motivation and the status of a new satellite mission, which targets the in-orbit verification of a wide band satellite communication link, operating in the high millimeter-wave regime at 71–76 GHz. Under ideal weather conditions, data rates over 15 Gb/s in complex modulation formats are expected to be demonstrated in-orbit, while data rates over 6 Gb/s are still expected under adverse atmospheric and weather conditions. The scientific payload, hosted by a 6U-CubeSat platform, comprises a high-performance W-band transmitter with GaN-based solid state power amplifiers, an InGaAs-based low-noise receiver and a modern FPGA-based digital processing unit, realizing arbitrary waveform generation functionality and 4k image/video storage. The ground terminal employs a highly directive Cassegrain antenna with coarse GNSS and fine multi-mode RF tracking and a low-noise receiver together with an integrated high-speed digital processing and storage unit. [Display omitted] • Motivation and status of the high throughput novel communication satellite • In-orbit verification and demonstration of data downlink at 71–76 GHz • State-of-the-Art analog frontend payload in InGaAs and GaN technology • Miniaturized computationally powerful FPGA-based payload computer • Evaluation of the wave propagation impairments and signal degradation • Challenges of the RF tracking through multi-mode tracking [ABSTRACT FROM AUTHOR]

Positioning technology based on received signal strength (RSS) fingerprints has emerged as a vital research topic in indoor localization due to its high accuracy and low cost. Increasing the density of offline fingerprints collected can effectively improve localization accuracy in the online stage, but it will result in astronomical collection costs. RSS reconstruction using generative models is an efficient solution to dramatically reduce fingerprint collection costs while maintaining excellent accuracy. In this paper, the authors propose a pix2pix generative adversarial network based RSS reconstruction model, which can generate a dense RSS fingerprint map based on only the location information of the wireless access point. The accuracy and efficiency of the proposed model are verified in an indoor environment. [ABSTRACT FROM AUTHOR]

Abstract This paper discusses an artificial noise‐aided intelligent reflecting surface‐MIMO–OFDM system physical layer secure communication, in which two cases for the intelligent reflecting surface reflection coefficient models are considered separately, that is unit modulus constraint for the reflection coefficients and the more practical situation of amplitude phase‐shift dependence. Then the problem of joint optimisation for the precoding matrix, artificial noise covariance matrix and intelligent reflecting surface reflection coefficient matrix to maximise the sum secrecy rate under the power constraint at the transmitter is formulated, and then an alternate optimisation‐based inexact block coordinate descent algorithm is proposed to tackle the formulated non‐convexity problem. For the problem with unit modulus constraint for the intelligent reflecting surface reflection coefficients, closed‐form solutions of the optimisation variables are obtained by utilising the Lagrange multiplier method and the complex circular manifold method. For the problem with intelligent reflecting surface reflection coefficient amplitude phase‐shift dependence, alternate optimisation‐based penalty method is used to obtain the intelligent reflecting surface optimal reflection matrix. Numerical results indicate that the algorithm for the intelligent reflecting surface reflection coefficient unit modulus constraint achieves the maximum secrecy rate, and the algorithm for the intelligent reflecting surface reflection coefficient of amplitude phase‐shift dependence has the sub‐optimal performance, and the benchmark schemes such as no intelligent reflecting surface and intelligent reflecting surface random phase shift strategies have the worst and similar performance.

Abstract The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnel environments. However, dense spatial discretizations are generally required for VPE simulations to achieve acceptable accuracy. This results in high computational costs for large‐size tunnels and high‐frequency wireless systems. This letter presents an efficient physics‐based recurrent neural network (RNN) model for modeling radio wave propagation in tunnels. The proposed RNN‐based model can leverage coarse‐mesh VPE to generate high‐fidelity received signal strength and achieve significant computational savings. The validity of the proposed approach is demonstrated against fine‐mesh VPE simulations in various tunnel cases.

Abstract To support the design and deployment of emerging high‐frequency wireless systems in various applications such as intelligent transportation, advanced received signal strength (RSS) simulation tools for radio coverage prediction are needed. In railway transportation, the vector parabolic equation (VPE) method is a commonly used approach for radio wave propagation modelling in large guiding structures such as tunnels. However, running VPE simulations at high frequencies in such an environment is computationally expensive, as the discretization parameters are closely related to the wavelength. In this letter, the authors propose a convolutional neural network‐based model that can efficiently provide RSS prediction at high frequencies by leveraging data obtained from VPE simulations at a lower frequency as an anchor. This proposed model can significantly improve the efficiency of VPE for high‐frequency RSS predictions and has been validated against conventional VPE in various tunnel cases.

Abstract Device‐free localisation (DFL) systems infer presence and location of moving users by measuring to which extent they change the received signal power in wireless links. Consequently, users not only induce perturbations to the power of the line of sight but also to the power of reflected and scattered signals which are observed in the received signal as multipath components (MPCs). Since the propagation paths of MPCs differ inherently from the line‐of‐sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath‐enhanced device‐free localisation (MDFL) system. Based on empirical models that relate perturbations in the received power of MPCs to the user location, the localisation problem can be solved by non‐linear Bayesian filtering. In this work, we investigate the point mass filter and the particle filter as possible solutions. We demonstrate the applicability of these solutions using ultra‐wideband measurements and develop and verify a numerical simulation framework that flexibly enables a sound evaluation of MDFL. Based on both measurements and simulations, we show a significant improvement of the localisation performance of MDFL compared to DFL. The overall localisation performance is thereby comparable for both filters. Eventually, we show that complexity and divergence probability, rather than localisation performance, are the decisive factors for the choice of the filter solution.

Abstract In this work, an artificial neural network (ANN) based time‐varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN‐based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high‐voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

Abstract Low latency beamforming using phased antenna arrays is the key for practical deployment of envisioned millimetre wave (mmWave) Gbps mobile networks. This work aims towards reducing the overhead of the exhaustive sector‐level sweep phase of the analog beamforming adopted in the IEEE 802.11ad standard. This work is the first to propose the use of reconfigurable antenna single RF chain in the sub‐6 GHz new radio (NR) band to aid codebook‐based beam selection in the mmWave band of the NR. We exploit the congruence between the spatial propagation signatures of signals at both mmWave and sub‐6 GHz frequencies to reduce the beam search space. The simulation results show a significant reduction in mmWave beam search overhead up to 70% on average and 80% with an average gain loss of 3dB.

Based on ray tracing in a smooth ionosphere described by the IRI‐2012 model we have inferred the seasonal‐diurnal dynamics of radio noise observed by four mid‐latitude high‐frequency (HF) radars. In the calculations, noise is assumed to be homogeneous and stationary, but the main contribution comes from the radar skip zone boundary due to focusing radiowaves effect. Noise absorption along the ray path is simulated from the IRI‐2012 electron density, and from the molecular nitrogen density and electron temperatures obtained from the NRLMSISE‐00 model. Earth magnetic field is not taken into account both in the absorption and ray‐tracing calculations due to insufficient accuracy of the ionospheric model. The model results are compared with experimental radar data, and good agreement between the two is demonstrated. It is shown that experimentally observed seasonal and diurnal dynamics of the noise correlates well with model predictions. We demonstrated saturation effect at low noise levels. The model makes it possible to estimate the amount of absorption in D‐ and E‐layers using noise observations at SuperDARN and SuperDARN‐like poleward‐oriented radars, especially at mid‐latitudes. This is important for the retrieval of long term variations in the electron density in the lower ionosphere, by using wide coverage provided by these radars' network. The model also makes it feasible to interpret vertical absorption by experimental noise observations, thereby significantly expanding the capability of HF radars to monitor the lower ionosphere, and to provide data for joint analysis with other data, obtained by these radars at E‐ and F‐layer heights. Key Points: Physics‐based seasonal‐daily noise level model built for pole‐oriented high‐frequency radarsGood agreement between modeled noise level and experimental observationsModel allows estimating vertical absorption from noise measurements at these radars [ABSTRACT FROM AUTHOR]

Over the past decade, spectrum sharing has evolved into viable commercial systems poised to meet the ever-increasing spectrum demand from service providers and end users. The key to the success of these systems is accurate and reliable propagation models that will simultaneously maximize the number of users able to access the spectrum and minimize the interference to incumbent or protected users. The classical propagation models currently utilized by these systems—the irregular terrain model (ITM) and extended hata (eHata)—do not account for features, such as foliage or endpoint clutter that can have a large impact on propagation loss. This article presents a measurement-based framework for updating these classical models and proposes a new integrated terrain and clutter model using publicly available geographic information system datasets. Over 400 000 path loss measurements were recorded in nine diverse locations across the United States at 1.7 and 3.5 GHz. Our updates improved the rms difference between measurements and model by 3–7 dB for ITM and 3–14 dB for eHata. In addition, we demonstrate several integrated terrain and clutter models that have rms differences ranging from 9.5 to 17.8 dB. Finally, cross validation was used to demonstrate the generalizability of our models to a wide variety of propagation environments. [ABSTRACT FROM AUTHOR]

The knowledge of diurnal cycle of rainfall (DCR), is crucial for meticulous understanding of the rain climatology, and thus quite imperative for devising precise rainfall forecasting models which is important for future satellite communication channel modelling. The location of Kolkata is characterized by stratiform, convective as well as mixed type of rainfall during different seasons of the year. The rain diversity prevailing over Kolkata, located at the verge of the tropics, prompts critical investigation of rain and rain-induced impairments. The present study utilizes 4 years data of ground-based observations over Kolkata. The present study not only highlights significant features of DCR during the pre-monsoon and the monsoon days but also indicates the distinct variability of rain-induced propagation impairments associated with the rain drop size distribution pattern. [ABSTRACT FROM AUTHOR]

In wireless local area networks (WLANs) compliant with the IEEE 802.11n standard, channel state information (CSI) is known to embed information about the surrounding environment, including the positions and movements of people. So far, this information has been inferred by exploiting certain temporal and spectral features of the related WLAN signals. In this letter, their multifractal, i.e. structural properties are examined by means of detrended fluctuation analysis. Preliminary results support the claims that (a) CSI traces exhibit multifractal properties and (b) these are influenced by human presence and activity/posture within the room. [ABSTRACT FROM AUTHOR]

The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnel environments. However, dense spatial discretizations are generally required for VPE simulations to achieve acceptable accuracy. This results in high computational costs for large‐size tunnels and high‐frequency wireless systems. This letter presents an efficient physics‐based recurrent neural network (RNN) model for modeling radio wave propagation in tunnels. The proposed RNN‐based model can leverage coarse‐mesh VPE to generate high‐fidelity received signal strength and achieve significant computational savings. The validity of the proposed approach is demonstrated against fine‐mesh VPE simulations in various tunnel cases. [ABSTRACT FROM AUTHOR]

To support the design and deployment of emerging high‐frequency wireless systems in various applications such as intelligent transportation, advanced received signal strength (RSS) simulation tools for radio coverage prediction are needed. In railway transportation, the vector parabolic equation (VPE) method is a commonly used approach for radio wave propagation modelling in large guiding structures such as tunnels. However, running VPE simulations at high frequencies in such an environment is computationally expensive, as the discretization parameters are closely related to the wavelength. In this letter, the authors propose a convolutional neural network‐based model that can efficiently provide RSS prediction at high frequencies by leveraging data obtained from VPE simulations at a lower frequency as an anchor. This proposed model can significantly improve the efficiency of VPE for high‐frequency RSS predictions and has been validated against conventional VPE in various tunnel cases. [ABSTRACT FROM AUTHOR]

A fundamental challenge for machine learning (ML) models for electromagnetics is their ability to predict output quantities of interest (such as fields and scattering parameters) in geometries that the model has not been trained for. Addressing this challenge is a key to fulfilling one of the most appealing promises of ML for computational electromagnetics: the rapid solution of problems of interest just by processing the geometry and the sources involved. The impact of such models that can “generalize” to new geometries is more profound for large-scale computations, such as those encountered in wireless propagation scenarios. We present generalizable models for indoor propagation that can predict received signal strengths within new geometries, beyond those of the training set of the model, for transmitters and receivers of multiple positions, and for new frequencies. We show that a convolutional neural network can “learn” the physics of indoor radiowave propagation from ray-tracing solutions of a small set of training geometries so that it can eventually deal with substantially different geometries. We emphasize the role of exploiting physical insights in the training of the network, by defining input parameters and cost functions that assist the network to efficiently learn basic and complex propagation mechanisms. [ABSTRACT FROM AUTHOR]

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning (ML) algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with ML-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area. [ABSTRACT FROM AUTHOR]

Agriculture 4.0 represents a considerable increase in the number of sensors, as well as the appearance of new wireless technologies, which will meet the need to efficiently plan radio communication systems in agricultural environments. In this letter, a simplified model for path loss estimation in citrus plantations is proposed. The model assumes that, for long distances, the physical mechanism is a parallel transmission path over the treetops that can be modeled by multiple-knife-edge diffraction. In our scenario, where the height of the transmitter is above the height of the trees, we proposed to estimate the multiple-knife-edge diffraction contribution by the settled field defined by Walfisch and Bertoni. In this way, the propagation losses estimated by the model have been compared with measurements carried out at 3.5 GHz in a lemon plantation before and after the fruit were collected. It has been observed that the slope of the regression line of the measurements yields values of 3.6 (with fruit) and 3.7 (without fruit), which are close to the value estimated by the model (3.8). The standard deviation of the prediction error given by the difference of the observed and estimated values is 4.5 dB (with fruit) and 3.2 dB (without fruit). [ABSTRACT FROM AUTHOR]

Device‐free localisation (DFL) systems infer presence and location of moving users by measuring to which extent they change the received signal power in wireless links. Consequently, users not only induce perturbations to the power of the line of sight but also to the power of reflected and scattered signals which are observed in the received signal as multipath components (MPCs). Since the propagation paths of MPCs differ inherently from the line‐of‐sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath‐enhanced device‐free localisation (MDFL) system. Based on empirical models that relate perturbations in the received power of MPCs to the user location, the localisation problem can be solved by non‐linear Bayesian filtering. In this work, we investigate the point mass filter and the particle filter as possible solutions. We demonstrate the applicability of these solutions using ultra‐wideband measurements and develop and verify a numerical simulation framework that flexibly enables a sound evaluation of MDFL. Based on both measurements and simulations, we show a significant improvement of the localisation performance of MDFL compared to DFL. The overall localisation performance is thereby comparable for both filters. Eventually, we show that complexity and divergence probability, rather than localisation performance, are the decisive factors for the choice of the filter solution. [ABSTRACT FROM AUTHOR]

The propagation of electromagnetic waves in the troposphere is significantly influenced by atmospheric refractivity and atmospheric ducts. Thus, abundant statistics on atmospheric refractivity and ducts are essential for reliable propagation prediction. In this study, we develop novel digital maps of atmospheric refractivity and atmospheric ducts based on the processing of radiosonde observation (RAOB) data and surface observation (SUOB) data collected from 2005 to 2014, including wet term of surface refractivity, refractivity gradients, and atmospheric ducts. The new digital maps, referred to as RADIO maps are defined as RADIOS and RADIOS2 maps. The RADIOS maps, which are world maps of refractivity gradients and atmospheric ducts, are obtained from data collected at radiosonde stations at 892 sites. The RADIOS2 map, which is a world map of the wet term of surface refractivity, is obtained from data collected at radiosonde and surface stations at 3218 sites. To test the consistency between RADIO maps and ITU-R ERAI (International Telecommunication Union Recommendations employing ERA-Interim (European Centre for Medium-Range Weather Forecasts Re-Analysis-interim) reanalysis data for 36 years from 1979 to 2014) maps; a comparison is performed with the cumulative distributions of refractivity gradients at two radiosonde stations. The accuracy of RADIO maps is checked through comparisons with the maximum, mean, and standard deviation of the relative errors, which are obtained from observed values and predicted values, between RADIO maps and ITU-R ERAI maps. Our results show that: 1) RADIOS2 map is consistent with the current ITU-R ERAI map with respect to the wet term of surface refractivity; 2) $\Delta N_{65\,\mathrm {m}}$ (50) and $\beta $ obtained from RADIOS maps are larger than those obtained from ITU-R ERAI maps in low-latitude coastal and marine areas; and 3) comparisons between RADIOS and ITU-R radios_duct maps (i.e., the world digital maps obtained from a 20-year (1977–1996) dataset of 661 radiosonde stations released by ITU-R P.453-14) show that ITU-R radios_duct maps predict lower occurrences of surface and elevated ducts at low latitudes. Overall, this study demonstrated that the new maps based on observations over the past ten years provide more accurate and reliable information than other traditional maps in terms of atmospheric refractivity, refractivity gradients, and atmospheric ducts. [ABSTRACT FROM AUTHOR]