A deep learning-based indoor radio estimation method driven by 2.4 GHz ray-tracing data
C Pyo, H Sawada, T Matsumura - IEEE Access, 2023 - ieeexplore.ieee.org
This paper presents a novel method for estimating received signal strength (RSS) in indoor
radio propagation using a deep learning approach. The proposed method utilizes a training …
radio propagation using a deep learning approach. The proposed method utilizes a training …
DeepRay: Deep learning meets ray-tracing
Efficient and accurate indoor radio propagation modeling tools are essential for the design
and operation of wireless communication systems. Lately, several attempts to combine radio …
and operation of wireless communication systems. Lately, several attempts to combine radio …
Indoor RSSI prediction using machine learning for wireless networks
N Raj - … Conference on COMmunication Systems & NETworkS …, 2021 - ieeexplore.ieee.org
We consider the study of received signal strength indication (RSSI) prediction in an indoor
room environment using a small set of actual measurement data. The RSSI prediction in a …
room environment using a small set of actual measurement data. The RSSI prediction in a …
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) …
Ray-tracing driven ann propagation models for indoor environments at 28 GHz
A Seretis, T Hashimoto, K Zeng… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Ray-tracing is widely used for radio propagation modeling of indoor environments, such as
hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave …
hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave …
Deep transfer learning based radio map estimation for indoor wireless communications
This paper investigates the problem of transfer learning in radio map estimation for indoor
wireless communications, which can be exploited for different applications, such as channel …
wireless communications, which can be exploited for different applications, such as channel …
Wisegrt: Dataset for site-specific indoor radio propagation modeling with 3d segmentation and differentiable ray-tracing
The accurate modeling of indoor radio propagation is crucial for localization, monitoring, and
device coordination, yet remains a formidable challenge, due to the complex nature of …
device coordination, yet remains a formidable challenge, due to the complex nature of …
Site-Specific Propagation Loss Prediction in 4.9 GHz Band Outdoor-to-Indoor Scenario
K Saito, Q Fan, N Keerativoranan, J Takada - Electronics, 2019 - mdpi.com
Owing to the widespread use of smartphones and various cloud services, user traffic in
cellular networks is rapidly increasing. Especially, the traffic congestion is severe in urban …
cellular networks is rapidly increasing. Especially, the traffic congestion is severe in urban …
Radio propagation prediction using neural network and building occupancy estimation
K Inoue, K Ichige, T Nagao… - … Symposium on Antennas …, 2021 - ieeexplore.ieee.org
In this paper, we propose a radio propagation prediction method using machine learning
and building occupancy estimation. There have been learning-based researches using …
and building occupancy estimation. There have been learning-based researches using …
RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation
Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for
capacity planning, placement of access points and base stations, localization, and coverage …
capacity planning, placement of access points and base stations, localization, and coverage …