Path Loss Modeling and Channel Characterization at 28 GHz 5G Micro-Cell Outdoor Environment using 3D Ray-Tracing

Ravuri, Viswanadham; Subbarao, M. V.; Terlapu, Sudheer Kumar; Ram, Gopi

doi:10.1109/icaect54875.2022.9808027

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional PL predictions are primarily categorized into deterministic methods [7][8][9][10][11][12][13] and empirical methods [14][15][16][17][18][19][20][21][22]. For deterministic methods, PL is typically modeled through the ray tracing (RT) technique combined with environmental information.…”

Section: Introductionmentioning

confidence: 99%

“…For deterministic methods, PL is typically modeled through the ray tracing (RT) technique combined with environmental information. The authors in [7,8] utilized the RT technique to model the PL of urban scenarios, taking into account the effects of transceiver locations and antenna heights, respectively. The authors in [9,10] predicted the PL under urban and forest scenarios, respectively, and both considered the additional impact of vegetation attenuation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Li,

Mao,

et al. 2023

Drones

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Li,

Mao,

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…This type of material has relatively low penetration losses in mmWaves [170], so it will allow very important transmission contributions to be considered. According to these results it is observed that the transmission contributions are very important in this particular scenario, so omitting this information as many scientific studies have done [160][161][162][163][164][165][166][167][168] may lead to inaccurate results, although it also depends on the type of scenario simulated.…”

Section: Calibration Of Ray-tracing Algorithm With Measurementsmentioning

confidence: 97%

“…The Ray-Tracing algorithm used so far has only taken into account the physical phenomena of specular reflection and edge diffraction. These two physical phenomena are the most important to consider according to the literature [160][161][162][163][164][165][166][167][168], because they are the ones that contribute the most energy to the received power, although this statement depends on the materials of the scenario where the mmWaves propagation channel is being evaluated [169][170][171][172][173].…”

Section: Calibration Of Ray-tracing Algorithm With Measurementsmentioning

confidence: 99%

Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond

Peña¹

View full text Add to dashboard Cite

Future mobile communications are expected to experience a technical revolution that goes beyond Gbps data rates and reduces data rate latencies to levels very close to a millisecond. New enabling technologies have been researched to achieve these demanding specifications. The utilization of mmWave bands, where a lot of spectrum is available, is one of them.Due to the numerous technical difficulties associated with using this frequency band, complicated channel models are necessary to anticipate the radio channel characteristics and to accurately evaluate the performance of cellular systems in mmWave. In particular, the most accurate propagation models are those based on deterministic ray tracing techniques. But these techniques have the stigma of being computationally intensive, and this makes it difficult to use them to characterize the radio channel in complex and dynamic indoor scenarios. The complexity of characterizing these scenarios depends largely on the interaction of the human body with the radio environment, which at mmWaves is often destructive and highly unpredictable.On the other hand, in recent years, the video game industry has developed powerful tools for hyper-realistic environments, where most of the progress in this reality emulation has to do with the handling of light. Therefore, the graphic engines of these platforms have become more and more efficient to handle large volumes of information, becoming ideal to emulate the radio wave propagation behavior, as well as to reconstruct a complex interior scenario. Therefore, in this Thesis one has taken advantage of the computational capacity of this type of tools to evaluate the mmWave radio channel in the most efficient way possible. This Thesis offers some guidelines to optimize the signal propagation in mmWaves in a dynamic and complex indoor environment, for which three main objectives are proposed.The first objective has been to evaluate the scattering effects of the human body when it interacts with the propagation channel. Once evaluated, a simplified mathematical and geometrical model has been proposed to calculate this effect in a reliable and fast way. Another objective has been the design of a modular passive reflector in mmWaves, which optimizes the coverage in indoor environments, avoiding human interference in the propagation, in order to avoid its harmful scattering effects. And finally, a real-time predictive beam steering system has been designed for the mmWaves radiation system, in order to avoid propagation losses caused by the human body in dynamic and complex indoor environments.

show abstract

Performance Evaluation of Optical Links: With and Without Forward Error Correcting Codes

Lakshmi,

Sugumaran,

Srinivas

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Path Loss Modeling and Channel Characterization at 28 GHz 5G Micro-Cell Outdoor Environment using 3D Ray-Tracing

Cited by 4 publications

References 7 publications

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

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

Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond

Performance Evaluation of Optical Links: With and Without Forward Error Correcting Codes

Contact Info

Product

Resources

About