Sionna RT: Differentiable ray tracing for radio propagation modeling
Sionna™ is a GPU-accelerated open-source library for link-level simulations based on
TensorFlow. Since release v0. 14 it integrates a differentiable ray tracer (RT) for the …
TensorFlow. Since release v0. 14 it integrates a differentiable ray tracer (RT) for the …
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) …
Learning radio environments by differentiable ray tracing
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and
environment-specific channel impulse responses (CIRs). While acquiring accurate scene …
environment-specific channel impulse responses (CIRs). While acquiring accurate scene …
Overview of the First Pathloss Radio Map Prediction Challenge
Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The
attenuation is due to large-scale effects such as free-space propagation loss and …
attenuation is due to large-scale effects such as free-space propagation loss and …
A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System
W Li, Y Sun, W Bai, Q Du, X Wang, D Wang, C Liu, F Li… - Remote Sensing, 2023 - mdpi.com
The global navigation satellite system radio occultation (GNSS-RO) is an important means of
space-based meteorological observation. It is necessary to test the Global Navigation …
space-based meteorological observation. It is necessary to test the Global Navigation …
Geo2SigMap: High-fidelity RF signal mapping using geographic databases
Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the
RF signal strength and distribution across specific areas, is crucial for cellular network …
RF signal strength and distribution across specific areas, is crucial for cellular network …
Stochastic evaluation of indoor wireless network performance with data-driven propagation models
Cell densification through the installation of smallcells and femtocells in indoor
environments is an emerging solution to enhance the operation of wireless networks. The …
environments is an emerging solution to enhance the operation of wireless networks. The …
Site-specific deep learning path loss models based on the method of moments
C Brennan, K McGuinness - 2023 17th European Conference …, 2023 - ieeexplore.ieee.org
This paper describes deep learning models based on convolutional neural networks applied
to the problem of predicting EM wave propagation over rural terrain. A surface integral …
to the problem of predicting EM wave propagation over rural terrain. A surface integral …
Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks
The increasing relevance of resilience in wireless connectivity for Industry 4.0 stems from the
growing complexity and interconnectivity of industrial systems, where a single point of failure …
growing complexity and interconnectivity of industrial systems, where a single point of failure …
A Differentiable Throughput Model for Load-Aware Cellular Network Optimization Through Gradient Descent
The efficient operation of cellular networks requires careful tuning of configuration
parameters, such as the transmit power or antenna tilts, to adequately balance interference …
parameters, such as the transmit power or antenna tilts, to adequately balance interference …