[HTML][HTML] Large scale survey for radio propagation in developing machine learning model for path losses in communication systems
Several orthodox approaches, such as empirical methods and deterministic methods, had
earlier been used for the prediction of path loss in wireless communication systems. These …
earlier been used for the prediction of path loss in wireless communication systems. These …
Artificial intelligence enabled radio propagation for communications—Part II: Scenario identification and channel modeling
This two-part paper investigates the application of artificial intelligence (AI) and, in particular,
machine learning (ML) to the study of wireless propagation channels. In Part I of this article …
machine learning (ML) to the study of wireless propagation channels. In Part I of this article …
Machine learning for radio propagation modeling: a comprehensive survey
M Vasudevan, M Yuksel - IEEE Open Journal of the …, 2024 - ieeexplore.ieee.org
With recent advancements in the telecommunication industry and the deployment of 5G
networks, radio propagation modeling is considered a fundamental task in planning and …
networks, radio propagation modeling is considered a fundamental task in planning and …
EM DeepRay: An expedient, generalizable, and realistic data-driven indoor propagation model
Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined
with the design and operation of next-generation wireless networks. Machine-learning (ML) …
with the design and operation of next-generation wireless networks. Machine-learning (ML) …
Enhancing microwave sensor performance with ultrahigh Q features using CycleGAN
In this work, a microwave planar sensor is used for liquid material characterization. Two
identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to …
identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to …
Machine learning-based urban canyon path loss prediction using 28 ghz manhattan measurements
Large bandwidth at millimeter wave (mm-wave) is crucial for fifth generation (5G) and
beyond, but the high path loss (PL) requires highly accurate PL prediction for network …
beyond, but the high path loss (PL) requires highly accurate PL prediction for network …
Rf genesis: Zero-shot generalization of mmwave sensing through simulation-based data synthesis and generative diffusion models
This paper presents RF Genesis (RFGen), a novel and cost-effective method for
synthesizing RF sensing data using cross-modal diffusion models, in order to improve the …
synthesizing RF sensing data using cross-modal diffusion models, in order to improve the …
Toward physics-based generalizable convolutional neural network models for indoor propagation
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 …
ability to predict output quantities of interest (such as fields and scattering parameters) in …
AI-enabled data-driven channel modeling for future communications
Wireless channel modeling plays an essential role in the design of wireless communication
networks. The future integrated network with various applications and extended-spectrum …
networks. The future integrated network with various applications and extended-spectrum …
A comprehensive prediction model for VHF radio wave propagation by integrating entropy weight theory and machine learning methods
J Wang, Y Hao, C Yang - IEEE Transactions on Antennas and …, 2023 - ieeexplore.ieee.org
In order to improve the prediction accuracy and robustness of radio wave propagation in the
very high-frequency (VHF) band, we proposed a combination prediction model (CPM) based …
very high-frequency (VHF) band, we proposed a combination prediction model (CPM) based …