Search

This paper presents a comprehensive survey of state-of-the-art UAV–based antennas and propagation measurements. Unmanned aerial vehicles (UAVs) have emerged as powerful tools for in situ electromagnetic field assessments due to their flexibility, cost-effectiveness, and ability to operate in challenging environments. This paper highlights various UAV applications, from testing large–scale antenna arrays, such as those used in the square kilometer array (SKA), to evaluating channel models for 5G/6G networks. Additionally, the review discusses technical challenges, such as positioning accuracy and antenna alignment, and it provides insights into the latest advancements in portable measurement systems and antenna designs tailored for UAV use. During the UAV–based antenna measurements, key contributors to the relatively small inaccuracies of around 0.5 to 1 dB are identified. In addition to factors such as GPS positioning errors and UAV vibrations, ground reflections can significantly contribute to inaccuracies, leading to variations in the measured radiation patterns of the antenna. By minimizing ground reflections during UAV–based antenna measurements, errors in key measured antenna parameters, such as HPBW, realized gain, and the front-to-back ratio, can be effectively mitigated. To understand the source of propagation losses in a UAV to ground link, simulations were conducted in CST. These simulations identified scattering effects caused by surrounding buildings. Additionally, by simulating a UAV with a horn antenna, potential sources of electromagnetic coupling between the antenna and the UAV body were detected. The survey concludes by identifying key areas for future research and emphasizing the potential of UAVs to revolutionize antenna and propagation measurement practices to avoid the inaccuracies of the antenna parameters measured by the UAV. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems. [ABSTRACT FROM AUTHOR]

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation. Article highlights: Considering the influence of 5G high-frequency electromagnetic wave on the electrical equipment in the substation, the positioning accuracy of 5G base station antenna in the substation is improved. The large area physical optics method can improve the calculation time and accuracy of radiofrequency radiation field intensity Combining the advantages of particle swarm optimization and interior point method, it can effectively avoid the problem of local optimal solution of location selection The simulation results show that the proposed method not only improves the location accuracy of 5G base station antennas in substations, but also applies to 5G base station antennas in more complex engineering environments [ABSTRACT FROM AUTHOR]

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions. [ABSTRACT FROM AUTHOR]

In this work, we determine the physical layer security (PLS) metrics for ground-to-unmanned aerial vehicle (UAV) link in the presence of a suspicious UAV, which intends to overhear confidential transmission. The ground node sends data up to the UAV, which is positioned exactly above the transmitter and is able to spot an aerial eavesdropper according to the predefined horizontal and/or vertical guard distance. However, the attacker tries to intercept the channel transmission outside of marked zone. The main and wiretap channel are both assumed to be subjected to Fisher-Snedecor fading process. Under such system/channel scenario, the average secrecy capacity, lower bound of secrecy outage probability, intercept probability and non-zero secrecy capacity are characterized in terms of mathematical tractable forms. In addition, numerical and simulation results are presented to verify the correctness of theoretical ones. The impact of different positions of UAVs (their heights and mutual distances) and various conditions over channels on the secrecy transmission is analysed and discussed in details. The proposed PLS scenario can be utilized in Internet of Things environments, with UAV as a data collector, to enhance the security of energy-aware or disaster-stricken transmissions. All obtained results can be helpful in prediction of positioning UAV to achive low probability of interception of confidential communication. [ABSTRACT FROM AUTHOR]

In this work, a group of unmanned aerial vehicles (UAVs) is aimed to function as relay nodes that provide a multi-hop wireless link between ground users. The communication route between the sender and receiver is established with awareness of the link path loss between the cooperating UAV relay nodes. Then, Dijkstra’s shortest path algorithm is used to find the optimal route that has the minimum total path loss among all other possible routes. MATLAB simulation results show a high-performance gain when using Dijkstra’s algorithm for establishing the communication route compared with the baseline approach in which the route is formed based on the shortest-distance path through the UAV relay nodes. It is worth mentioning that both shadow fading and nonfading radio environments were taken into account. Moreover, the performance is further improved when the standard deviation of shadow fading increases. The work also investigates the probability of selecting each of the UAV nodes in forming the multi-hop link considering the total simulated realizations. Here, it is shown that more variability is observed in the indexes of the traversed UAV nodes when routeing through a random fading environment compared to a nonfading environment.

Abstract Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Accurate signal path loss models for predictions are crucial in current cellular communication networks. Recently, numerous path loss estimation methods have been presented to improve the efficiency of networks. However, most of these existing models do not include spatial data such as land use/land cover, terrain elevation, building height, and the effect of topography. To address this issue, this study proposes a GeoAI-based technique for path loss estimation in cellular communication networks, addressing existing models' lack of spatial data integration. Support Vector Regression, K-Nearest Neighbor, Random Forest, and multi-layer perceptron (MLP) artificial neural network models are evaluated using field measurements in an urban, suburban area in Van, Turkey, across various frequencies. Among the models, MLP with three hidden layers, nine input variables, hyperbolic tangent activation function, and Adam optimization method performs best. At 900 MHz, MLP has been observed with MSE, RMSE, MAE, and R values of 0.22 dB, 0.47 dB, 0.46 dB, and 0.99 dB, respectively. Lastly, a comparison of the developed model to the Free space, COST 231, Ericsson, and SUI models revealed that the GeoAI-based path loss models outperformed the empirical models regarding prediction accuracy and generalization. This study underscores the significance of integrating spatial data into path loss prediction, particularly in diverse urban and suburban environments, for optimizing cellular communication networks. [ABSTRACT FROM AUTHOR]

Optical Wireless Communication (OWC) technology, owing to its numerous merits including high-speed transmission, large modulation-bandwidth, security, and cost-effectiveness, has been considered for many applications including terrestrial links, indoor wireless transmission, underwater transmission, hybrid radio-frequency/free space optics links, and space applications. Inter-satellite OWC links are one of the most crucial applications of OWC technology, which can be used to transmit data from one part of the globe to other to achieve global connectivity. On the other hand, Orbital Angular Momentum (OAM) multiplexing is an emerging data-transmission technology used to increase the throughput and spectral-efficiency of optical links. In this work, we report, the integration of OAM multiplexing with inter-satellite OWC link to achieve high-speed information transmission for space applications. 4 OAM beams ( LG 0 0 , LG 13 0 , LG 40 0 , and LG 80 0) are used to transmit independent 20 Gbps binary data over an OWC channel between two satellites. A net transmission speed of 80 Gbps is achieved. The performance of the proposed inter-satellite WC link based on OAM transmission is evaluated through range, misalignment pointing errors, additional losses due to solar radiations, and laser transmission power using numerical simulations. The proposed system demonstrates a reliable 80 Gbps transmission over 10000 km inter-satellite link distance at 30 dBm laser power under the influence of 1.6 µrad pointing error and 5 dB additional losses with log(BER) ≤ − 5. Furthermore, as the pointing error is reduced to 1 µrad, the transmission range is increased from 10000 to 12000 km with log(BER) ≤ − 5. [ABSTRACT FROM AUTHOR]

LoRa WAN (Long range wide area network) performance in IoT (Internet of Things) tends to be an effective communication at low power and less cost. It assists all the nodes to interrelate within a range of 5 km if received signal strength is up to − 154 dBm. The calculated link budget of LoRa WAN predict LOS/ NLOS signal in the range of less than − 154 dBm at path loss of maximum 160 dB. Path loss of a signal is calculated with standard model and area specific path loss model is proposed. The real time analysis based on received signal strength resulted a standard deviation of signal in the range of 11.8 dB for a fading environment. Path loss with standard models in deep fading areas varies in between 200 and 360 dB. Area specific path loss model for free space is proposed within the range of LoRa WAN and it varies between 100 and 154 dB. The outage in deep fading area tends to deep almost 80% at 1 km range and data transfer rate is about 17 % at 3 dB noise figure. The received parameters are also compared with LTE and GSM networks which operate at L band frequency. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems. [ABSTRACT FROM AUTHOR]

The rollout of the fifth-generation (5G) mobile network was completed in 2020. Now, the focus shifts towards exploring the new perspectives of Beyond 5G (B5G) and 6G. Despite being a key enabler of 5G, millimeter wave technology has not yet been fully implemented due to various deployment challenges. Over the past decade, numerous efforts have been dedicated to modeling the propagation channel across the spectrum from 0.5 to 100 GHz. Propagation models such as Close-In (CI), Floating Intercept (FI), and single frequency Alpha-Beta-Gamma (ABG) have been extensively utilized. However, a significant deviation between co- and cross-polarization models has been observed in many estimated models in the literature. In this paper, we propose an optimization of the classic CI model by considering both TX and RX antenna heights, along with a correction coefficient. Initially, we estimate the propagation channel using the classic CI model in indoor environments, revealing a notable deviation between co- and cross-polarization models. Subsequently, we introduce a new model aimed at reducing this deviation and optimizing the Path Loss Exponent (PLE). With our proposed model, the estimated channel depolarization in Line-of-Sight (LOS) conditions stands at 2.8 dB, 5.6 dB, and 2.9 dB for room, corridor, and stairwell environments, respectively. In contrast, utilizing the conventional model yields channel depolarization figures of 4.8 dB, 8.2 dB, and 7 dB for the same environments. This disparity in channel depolarization can have significant implications for millimeter wave propagation, particularly during meteorological events such as rainfall. [ABSTRACT FROM AUTHOR]

Communication has been a crucial aspect of information and worldwide connectivity for a long time. The enhancement in communication technology is demanding, making promulgating information so fast and enlarging. The Research in the 6G Channel model is gaining momentum to meet the skyrocketing demand of the present cellular network. In the present scenario, the mm-wave signal can accomplish wide bandwidth requirements and high data rates (up to GBps) for upholding 6G standards. However, channel modelling in mm-wave communication remained challenging because of the stringent Line-of-sight (LOS) requirement and a low Signal to Noise Ratio (SNR). This paper aims to design and survey a channel model of 6G Communication system. Using NYUSIM software, channel modelling simulations have been carried out on Bhubaneswar city as a case study. NYUSIM enables researchers to precisely model and simulate wireless channels, which is necessary for the design and optimization of wireless systems and technologies. It also offers a standardized structure and flexibility for a wide range of research applications. In this paper, we investigate 85GHz, 90GHz, and 95GHz channels in Bhubaneswar to assess performance for Omni-directional and Directional power delay profiles in terms of pathloss, pathloss exponent, and received power. In this study, both the urban microcell (UMi) and the urban macrocell (UMa) scenario are considered at a distance of 10m to 100m in both LOS and NLOS environments. Simulation results are tabulated under different cases for comparison. [ABSTRACT FROM AUTHOR]

In this paper, the path loss performance of an eight-port Multiple-In-Multiple-Out (MIMO) antenna consisting of four circular slots radiator-based antenna elements is investigated in 3.5–3.75 GHz range for utilisation as an RFID tag and reader antenna. First, the S-parameters between the ports of two proposed MIMO antennas are measured in four channels of spatial diversity; one direct line-of-sight (LOS) and three indirect LOS in an anechoic chamber, each for the horizontal and vertical polarisation schemes at two distances, 5 and 25 cm, and the path losses between the ports of MIMO antennas are evaluated. Then, the path losses between the same MIMO antennas are obtained in a laboratory environment, which are higher by about 5–10 dB than those in the anechoic chamber and so, these losses are more dependent on the antenna position and nearby objects. The tag power sensitivity and the tag (read) range for the presented MIMO antenna are found to be more power level dependent than frequency dependent. The reported results confirm the performance of proposed MIMO antennas for RFID applications in addition to 5 G communications. [ABSTRACT FROM AUTHOR]

Path loss is important for the unmanned aerial vehicle (UAV) placement, trajectory optimization, and power allocation in UAV-aided communications. By considering both the factors of path delay and reflection angle (RA) in the non-line-of-sight (NLoS) paths, a new machine learning-based delay–angle-joint path loss prediction method for UAV mmWave channels is proposed. The one-input back propagation based neural network (BPNN) and two-input BPNN are built to predict the path power for line-of-sight (LoS) case and NLoS case, respectively. Meanwhile, a data acquisition method is developed to obtain massive data set for training the BPNN. According to the geometric information of digital map, a calculation method for path delay and RA is also proposed to drive the BPNN. The proposed method is simulated and analyzed based on ray-tracing (RT) simulated data under a typical urban scenario at 28 GHz. The 2D relationship of power–delay for the LoS case and 3D relationship of power–delay–RA for the NLoS case are obtained through the trained BPNN, which are well consistent with the validation set of RT data and outperform the traditional methods, i.e., 3GPP model and exponential model. Moreover, it's found that the path power rapidly decreases when RA is 65 ∘ – 75 ∘ under the simulation scenario, which could be an important reference for transceiver placement and route planning to reduce the impact of NLoS paths in the UAV channel. [ABSTRACT FROM AUTHOR]

Wireless communication in underground environments presents formidable challenges due to the diverse and complex transmission mediums, including rocks, icebergs, soil, and water. These environments exhibit distinct properties such as varying densities, salinities, and numerous obstacles. Conventional wireless communication methods encounter difficulties in these scenarios, primarily stemming from issues like electrical conduction, line-of-sight obstacles, high path loss, dynamic channel conditions, and the requirement for large antennas, multiple scattering, and propagation delays constrained by the speed of light. However, electromagnetic induction (EMI) communication has emerged as a promising alternative solution, offering advantages such as reliable connectivity, consistent channel responses, rapid propagation, frequency offset capabilities, minimal propagation delays, and energy efficiency. Despite these merits in underwater environment ecosystems communications, EMI communication suffers from a limited communication range, typically spanning just a few meters. Recent research endeavors have sought to extend the effective range of EMI communication by exploring various strategies, including manipulations of coil parameters, structural enhancements, material advancements, and configuration improvements. This comprehensive study and evaluation delves into the diverse applications, notable advantages, techniques for extending communication range, and the pressing challenges and open issue facing EMI communication in underwater ecosystem environments, requiring further investigation and new research. [ABSTRACT FROM AUTHOR]

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

This study analyzes two distinct approaches for predicting path loss at frequencies of 800 MHz, 1800 MHz, and 2600 MHz in suburban areas. These frequencies are commonly utilized in broadcasting, 4G LTE, and 5G networks. Two models of Artificial Neural Networks (ANN) were implemented: an Error-Based Neural Network (EBNN), which incorporates error correction by combining empirical propagation models with an ANN, and a Terrain Parameters Based Neural Network (TBNN), which uses input parameters commonly applied in related studies, such as the distance from the transmitter to the receiver, receiver altitude, average terrain level, and azimuth angle between the transmitter and receiver. The performance of these models was evaluated using root mean square error (RMSE) and the Wilcoxon rank-sum test, comparing them with empirical propagation models such as SUI, ECC-33, Ericsson, and TR 25.942. The results were then compared with data obtained from a measurement campaign conducted along three routes in the city of Natal, Brazil. The findings from both simulations and actual measurements showed good metric alignment, particularly highlighting the performance of the error-based model. The primary contribution of this study is demonstrating that these techniques enable more accurate prediction of signal information, thereby reducing errors in the planning and implementation of wireless networks.

This paper studies the maximum reliability of multi-hop relay UAVs, in which UAVs provide wireless services for remote users as a coded cooperative relay without an end-to-end direct communication link. In this paper, the analytical expressions of the total power loss and total bit error rate are derived as reliability measures. First, based on the environmental statistical parameters, a LOS probability model is proposed. Then, the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss and introducing the maximum allowable path loss constraints, transmission power allocation constraints, and UAV height and elevation constraints. At the same time, the total path loss is minimized to achieve maximum ground communication coverage. However, the formulated joint optimization problem is nonconvex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different; thus, using the mobility of UAVs can improve the reliability of communication performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the characterization of radio propagation, and data communication in a marine environment. More specifically, we consider signal propagation when three different sub-gigahertz industrial, scientific, and medical (ISM) bands, i.e., 169 MHz, 434 MHz, and 868 MHz, are used. The main focus of the paper is to evaluate the path loss (PL), i.e., the power loss that a propagation radio wave would experience when communication occurs between a sail boat and a buoy. We describe the measurement results obtained performing three different radio power measurement campaigns, at the three different aforementioned ISM sub-gigahertz bands. We also want to correlate the radio propagation quality with the weather conditions present in the measurement areas. The obtained results show that higher distances are achieved by transmitting at lower frequencies, i.e., 169 MHz, and, on average, the propagation is directly dependent from the dew point index. [ABSTRACT FROM AUTHOR]

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs. [ABSTRACT FROM AUTHOR]

Channel modeling plays a crucial role in measuring wireless communication performance. This paper describes statistical channel modeling for a recorded signal from a ground station in an urban area in Tehran. This signal was recorded from the GRBALPHA satellite at 170–180° elevation and fixed azimuth. The three-state Markov channel modeling is used for this case. Markov models are often used for Land Mobile Satellite Channels (LMSC) because when the user is moving at different speeds between different environments, this causes, Variety in received power. By analyzing the communication channel in the lower elevations, the link budget can be designed with a suitable margin by considering the channel specification and its limitations. In addition to increasing communication time, reducing the bit error rate (BER), lowering costs, etc., this causes a more accurate channel analysis from the beginning to the end of the satellite passage.This work presents a statistical model for channel simulation using the Markov multi-state chain, estimating the Markov channel model between a fixed ground station and a Low Earth Orbit (LEO) Satellite at low elevations. The comparison of simulation data with recorded data demonstrates the proposed approach's effectiveness in modeling the channel.

Considering the rapid time-varying channel conditions and large Doppler frequency fluctuations caused by high-speed movement of unmanned aerial vehicles, this paper proposes a non-stationary channel emulation scheme for Unmanned Aerial Vehicles(UAVs) using a field-programmable gate array platform. The proposed scheme adopts the sum of frequency modulation method to generate non-stationary channel fading and proposes a real-time algorithm for generating channel parameters to improve the real-time performance of channel emulation and ensure the real-time updating of channel status. In addition, to address the issue of power random fluctuation caused by large Doppler frequency fluctuations, this paper designs an adaptive power equalization module that ensures the stability of fading power by limiting the maximum fluctuation to only 1.13%. Finally, the results of hardware resource consumption analysis demonstrate that the proposed approach in this study significantly reduces the utilization of storage resources compared to replay-based and pre-stored solutions, with reductions of 52.44% and 9.31%, respectively. This makes the proposed approach well-suited for simulating long-duration non-stationary channel fading in UAV scenarios. Additionally, the measured analysis results demon-strate that the channel characteristics output by the proposed hardware emulation scheme, such as path loss and Doppler power spectrum density,closely align with theoretical results when compared to the emulation scheme without the equalizer. This research can be applied to the design and optimization of UAVs communication systems.

Implantable wireless bioelectronic devices enable communication and/or power transfer through RF wireless connections with external nodes. These devices encounter notable design challenges due to the lossy nature of the host body, which significantly diminishes the radiation efficiency of the implanted antenna and tightens the wireless link budget. Prior research has yielded closed-form approximate expressions for estimating losses occurring within the lossy host body, known as the in-body path loss. To assess the total path loss between the implanted transmitter and external receiver, this paper focuses on the free-space path loss of the implanted antenna, from the body-air interface to the external node. This is not trivial, as in addition to the inherent radial spreading of spherical electromagnetic waves common to all antennas, implanted antennas confront additional losses arising from electromagnetic scattering at the interface between the host body and air. Employing analytical modeling, we propose closed-form approximate expressions for estimating this free-space path loss. The approximation is formulated as a function of the free-space distance, the curvature radius of the body-air interface, the depth of the implanted antenna, and the permittivity of the lossy medium. This proposed method undergoes thorough validation through numerical calculations, simulations, and measurements for different implanted antenna scenarios. This study contributes to a comprehensive understanding of the path loss in implanted antennas and provides a reliable analytical framework for their efficient design and performance evaluation.

This paper presents wideband channel measurements for indoor Non-Line-of-Sight (NLoS) fixed links operating at sub-Terahertz (sub-THz) frequencies (92.110 GHz) with single scattered reflections. Seemingly “smooth” surfaces generate substantial internal multipath causing frequency selective scattered reflections owing to the short wavelength. Unlike specular reflections, no single well-defined reflection point aligns the incidence angle ( $\theta_{{\mathrm {i}}}$ ) and reflection angle ( $\theta_{{\mathrm {r}}}$ ) to form the lowest path loss. This means that the scattering effect of the reflective surface gives reason to search for the best angular alignment of the Receiver (Rx) antenna given a specific angle from the Transmitter (Tx) antenna. The wideband effective Radar Cross Section (RCS) is derived and computed based on the bi-static radar equation, which specifically accounts for the effect of alignment angle on the defined frequency selective RCS. The measurements reveal alignment-variant angular scattering from large-scale discontinuities such as wall partitions, television screens and metallic reinforcement studs, offering valuable insights for the design and deployment of future wireless communications systems operating in the sub-THz band.

This paper introduces a novel method for estimating the path loss value in indoor scenarios. It uses a combination of electromagnetic calculation and machine learning. Using electromagnetic software, we design the indoor environment and calculate path loss between the receiver and the transmitter antennas, which are situated randomly with respect to the transmitters for better coverage. The transmitter antenna is modeled by employing the 3GPP standards. The indoor NLOS and LOS scenarios are measured over a 47-m separation distance between the RX and TX antenna locations. As it comes to fitting real measured data that has been acquired via measurement campaigns, the suggested models perform better overall. The achieved data is used to predict path loss using empirical models like FI and CI, and the mean absolute error percentages obtained are 5.87 and 6.61, respectively. After that, deep neural network and CatBoosting methods are employed to predict and estimate the PL precisely in the indoor environment. The mean absolute error percentages obtained are 4.4 and 3.3 for deep neural network and CatBoosting, respectively. Predicting PL, deep neural network, and CatBoosting methods outperform CI and FI by modelling complex, non-linear relationships, incorporating a broader range of features, adapting to diverse environments, and generalizing more effectively from data. These benefits are well-matched for modern wireless communication systems, where accurate estimation of signal loss is essential for maximizing network performance.

Accurate estimation of path loss poses a challenge given its strong reliance on complex propagation environments that may change frequently for example due to new building constructions. Furthermore, the propagation channel at millimeter wave (mmWave) frequencies is highly susceptible to blockage, leading to notable variations in path loss values among different links at comparable distances, which renders path loss estimation based on empirical model less representative. In this paper, we tackle the challenge of accurate path loss estimation for sub-6 GHz and mmWave frequencies when working with a limited dataset of satellite images. In contrast to current literature which relies on satellite images in conjunction with supplementary models, features, or algorithms for path loss estimation, our approach involves directly estimating the path loss from satellite images. Specifically, we employ convolutional neural networks (CNNs), based on VGG-16 and ResNet-50 architectures, to fine-tune existing pretrained models, aiming to achieve accurate path loss estimation. By incorporating the appropriate layers to be fine-tuned, we are able to accelerate the training process. Furthermore, the proposed CNN path loss models yields lower prediction errors compared to the 3GPP 38.901 urban macro (UMa) empirical model by up to 5.5 dB in root mean square error (RMSE) and 4.6 dB in mean absolute error (MAE). In many instances involving different fine-tuning variants and frequencies, VGG-16 achieves lower prediction errors compared to ResNet-50. If processing times and resource consumption are a priority, ResNet-50 can be a viable choice, especially if tolerating increases in RMSE and MAE by up to 0.9 dB and 1 dB, respectively, is acceptable.

Surface wave-assisted wireless communication systems have recently emerged as a promising complementary solution for creating a smart radio environment, particularly in the context of beyond-fifth generation (5G) and sixth generation (6G) networks. Unlike traditional approaches that rely solely on space waves or use passive elements on a large surface to reflect space waves, the incorporation of surface waves can effectively address challenges such as blockage and severe path loss, which are prevalent at extremely high frequencies, including millimeter wave (mmWave) and terahertz (THz) bands. Recent advances in metasurface properties have enabled the propagation of electromagnetic waves on a surface without re-radiation. Motivated by these developments, in this paper, we develop a path loss model and two surface impedance models for surface wave-assisted wireless communication systems. We demonstrate that the signal-to-noise ratio (SNR) of this system can be significantly higher than that of traditional wireless communication systems because the path loss for a surface wave is proportional to the propagation distance, whereas the path loss for a space wave is proportional to the square of the propagation distance. Furthermore, we explore how the metasurface should be designed in terms of its materials and structures, considering that the incident space wave must be aligned with Brewster’s angle to excite a surface wave.

With recent advancements in the telecommunication industry and the deployment of 5G networks, radio propagation modeling is considered a fundamental task in planning and optimization. Accurate and efficient models of radio propagation enable the estimation of Path Loss (PL) or Received Signal Strength (RSS), which is used in a variety of practical applications including the construction of radio coverage maps and localization. Traditional PL models use fundamental physics laws and regression-based models, which can be guided with measurements. In general, these methods have small computational complexity and have been highly successful in attaining accurate models for settings with trivial environmental complexity (e.g., clear weather or no clutter). However, attaining high accuracy in radio propagation modeling at complex settings (e.g., an urban setting with many buildings and obstacles) has required ray tracing, which computationally complex. Recently, the wireless community has been studying Machine Learning (ML)-based modeling algorithms to find a middle-ground. ML algorithms have become faster to execute and, more importantly, more radio data measurements have become available with the increased deployment of wireless devices. In this survey, we explore the recent advancements in the use of ML for modeling and predicting radio coverage and PL.

This research provides a detailed quantitative analysis of the challenges encountered in deploying 6G Unmanned Aerial Vehicle Base Stations UAV-BS in arid environments, specifically focusing on Saudi Arabia. The study meticulously quantifies the impact of dust and sand storms on these advanced communication systems, highlighting the substantial increase in signal attenuation and path loss under such conditions. Key findings include a marked dependency of transmitted power requirements on visibility at millimeter range high frequencies, (at 100 GHz). The results demonstrate that at this frequency, a decrease in ground visibility from 15 to 5 meters necessitates a significant increase in transmitted power by approximately 10 dB. Moreover, a striking 50 dB increase in path loss is observed when transitioning from a 3 GHz carrier frequency to 100 GHz under a ground visibility of 5 meters. This increase is attributed to a 30 dB rise in free space loss FSL due to the change in carrier frequency and an additional 20 dB attributed to increased dust attenuation at 100 GHz compared to 3 GHz. Our approach integrates PSO for dynamic UAV-BS positioning, effectively mitigating the adverse effects of dust and sand on signal quality. The research underscores the importance of considering UAV altitude, signal frequency, and ground visibility for optimal signal propagation. Additionally, it highlights the necessity of dynamic power management strategies within the network architecture to adapt to the unique challenges of arid environments. This study not only illuminates the practical implications for the deployment of UAV-BS in future 6G networks but also emphasizes the need for continuous adaptation of UAV-BS positioning in response to changes in visibility and that due to continuous change in the pattern of active users (asking for network services). The insights gained are crucial for ensuring reliable communication during environmental challenges, paving the way for more resilient and efficient wireless communication systems in demanding conditions.

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications.

Wireless channel properties in industrial environments can differ from residential or office settings due to the considerable impact of heavy machinery that triggers intricate multipath propagation effects and strong blockage effects. Previous investigations on wireless propagation in factories often consisted of empirical models, that is simple analytical formulas based on measurement data. Unfortunately, they usually lack in flexibility, since they seldom include geometrical parameters describing the industrial scenario and therefore turn out reliable only in industrial scenarios sharing the same propagation characteristics as those where the measurements were performed. In response to this limitation, this article harnesses the power of Machine Learning to model propagation markers like path loss, shadowing, and delay spread in the industrial environment. By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. Results are in fair overall agreement with previous studies and highlight some trends about the sensitivity of the propagation parameters to the considered input features.

A set of 28 GHz close-to-ground propagation measurements are conducted for different rural outdoor scenarios at the National Spectrum Centre, Aberystwyth. A bespoke correlation based channel sounder and horn antennas are employed in the measurement system. The measurements provide large-scale path loss, time domain impulses and channel measurements that will be beneficial for rural future millimeter wave communication systems. Different scenarios, such as straight and elevated paths, zig-zag curved road and curved road in woods are explored and log-normal models are extracted. Propagation measurements in diverse terrains including hill, grass land, dense forest and buildings are also conducted. The path loss predictions of existing foliage loss models like Weissberger, ITU-R, COST and FITU-R are investigated with frequency and distance. Then, the measured path losses are compared with these models. Predicted pathloss values of these models are overestimated at 28 GHz in dense tree forest after 50 m. Strong reflections are observed in the propagation through buildings at log(d) distance of 1.6 and 1.9. Similar path loss attenuations and fixed offset values (nearly 22 dB more than free space) are observed in the propagation through the dense forest and curved road in woods. The presented close-to-ground propagation research is significant for future military applications and mobile device-to-device communications.

In Wireless Sensor Networks (WSNs), Collaborative Beamforming (CBF) is a crucial instrument for extending the transmission range. Because WSNs are unpredictable and complicated, it is crucial to develop and apply better metaheuristic algorithms in CBF. This paper presents mathematical models as a result of MATLAB simulation of the consumed energy in a wireless sensor network (WSN) using beamforming with least mean square algorithm (LMS). The objective is to model effect of transmitter power (PowerTX), Number of cycles or iterations, and weights on the energy function. The simulation showed that the relationship between energy and transmitter power is an adaptive, power relationship, while the relationship between energy function and cycles, and weights is an exponential relationship. Correlation between the two functions proved to be possible through the developed mathematical model, which also emphasizes effect of number of sensor nodes on the overall energy consumption and weight changing. The presented work requires further development and more analysis, which involves, inclusion of other parameters that affect energy consumption and range coverage, inside and outside buildings and enclosures. The main objectives and contribution of this work is establishing mathematical models based on simulation of real life scenarios. Such models enabled correlation of energy, weights, number of nodes, cycles, and transmitter power in a manner that highlighted the effect of each on energy and power consumption. In addition, the work enabled the focus on the importance of number of cycles and LMS weights on additional energy consumption per antenna transmitter power.

This paper presents an in-depth investigation into path loss modeling for wireless communication systems, emphasizing the critical role of Exploratory Data Analysis (EDA) and accurate field data collection. It reveals the enhanced efficacy of theoretical models when synergized with specific, real-world data, highlighting the necessity for empirical grounding. Central to our research is the demonstration of the indispensability of thorough EDA in path loss modeling. This phase uncovers key trends and anomalies, setting a solid foundation for subsequent Machine Learning (ML)-based predictive modeling. Deep dataset insights allow for the effective tuning of algorithms to reflect the complexities of signal propagation environments accurately. The study further underscores the impact of the quality of field data collection on model accuracy. Meticulous data gathering that accurately mirrors environmental conditions is essential, providing insights into signal behavior and validating theoretical models. Our research also explores the importance of spatial pattern analysis in understanding path loss. Advanced visualization and clustering methods are employed to illustrate how spatial variability significantly influences path loss, shedding light on the effects of environmental factors like obstructions and topography on signal propagation.

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

This paper presents a comprehensive survey of state-of-the-art UAV–based antennas and propagation measurements. Unmanned aerial vehicles (UAVs) have emerged as powerful tools for in situ electromagnetic field assessments due to their flexibility, cost-effectiveness, and ability to operate in challenging environments. This paper highlights various UAV applications, from testing large–scale antenna arrays, such as those used in the square kilometer array (SKA), to evaluating channel models for 5G/6G networks. Additionally, the review discusses technical challenges, such as positioning accuracy and antenna alignment, and it provides insights into the latest advancements in portable measurement systems and antenna designs tailored for UAV use. During the UAV–based antenna measurements, key contributors to the relatively small inaccuracies of around 0.5 to 1 dB are identified. In addition to factors such as GPS positioning errors and UAV vibrations, ground reflections can significantly contribute to inaccuracies, leading to variations in the measured radiation patterns of the antenna. By minimizing ground reflections during UAV–based antenna measurements, errors in key measured antenna parameters, such as HPBW, realized gain, and the front-to-back ratio, can be effectively mitigated. To understand the source of propagation losses in a UAV to ground link, simulations were conducted in CST. These simulations identified scattering effects caused by surrounding buildings. Additionally, by simulating a UAV with a horn antenna, potential sources of electromagnetic coupling between the antenna and the UAV body were detected. The survey concludes by identifying key areas for future research and emphasizing the potential of UAVs to revolutionize antenna and propagation measurement practices to avoid the inaccuracies of the antenna parameters measured by the UAV.

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV’s agility and mobility, ground user’s mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems.

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

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In this paper, the application of AI and machine learning (ML) to the study of wireless propagation channels is investigated in two parts: first, an artificial neural network model is used to improve path loss prediction, and then, a pattern recognition model using multilayer perceptron (MLP) networks is used to identify and remove impulsive noise in power delay profiles (PDP). These studies were conducted based on field measurements in the 2400 MHz band in a public square with vegetation. The results are analyzed and compared with ordinary least squares (OLS) nonlinear regression results and results from similar studies. The Root Mean Square Error (RMSE) values between the experimental results of mean path loss and those provided by each propagation model are presented. The adjustment performed by OLS nonlinear regression and ANN significantly reduced the RMSE. The best results are those presented by artificial neural networks, with RMSE of 0.39 when using four neurons in the hidden layer of the ANN. The ANN used to identify and remove impulsive noise in power delay profiles (PDP) through pattern recognition proved to be more efficient than the CFAR technique. ANN technique found a larger number of valid multipaths compared to the CFAR technique.

Abstract Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions.

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.