A deep learning network planner: Propagation modeling using real-world measurements and a 3D city model
In urban scenarios, network planning requires awareness of the notoriously complex
propagation environment by accounting for blocking, diffraction, and reflection on buildings …
propagation environment by accounting for blocking, diffraction, and reflection on buildings …
How to tame mobility in federated learning over mobile networks?
Y Peng, X Tang, Y Zhou, Y Hou, J Li… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Federated learning (FL) over mobile networks has attracted intensive attention recently.
User mobility is a fundamental feature of mobile networks, which leads to dynamic network …
User mobility is a fundamental feature of mobile networks, which leads to dynamic network …
TinyDRaGon: Lightweight radio channel estimation for 6G pervasive intelligence
Due to the emerging challenges with future 6G networks such as high data rates and the
need for remarkably low latency, future wireless communication systems must be planned …
need for remarkably low latency, future wireless communication systems must be planned …
Distributed Split Learning for Map-Based Signal Strength Prediction Empowered by Deep Vision Transformer
This article focuses on predicting the received signal strength (RSS) of mobile users, which
is a fundamental problem for improving the coverage of cellular networks. Traditional …
is a fundamental problem for improving the coverage of cellular networks. Traditional …
AI-Enhanced Generalizable Scheme for Path Loss Prediction in LoRaWAN
M Chen, Y Zhang, Z Ji… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Long-range wide-area network (LoRaWAN) is a widely used technology in the Internet of
Things (IoT), which provides long-range (LoRa) communication with low power …
Things (IoT), which provides long-range (LoRa) communication with low power …
Fine-tuning for propagation modeling of different frequencies with few data
T Nagao, T Hayashi - 2022 IEEE 96th Vehicular Technology …, 2022 - ieeexplore.ieee.org
Wireless emulation is an essential technique for efficiently designing and verifying wireless
systems in a virtual space. To accurately emulate the behavior of wireless systems in various …
systems in a virtual space. To accurately emulate the behavior of wireless systems in various …
Multi-Head DNN Based Federated Learning for RSRP Prediction in 6G Wireless Communication
M Yu, X Xiong, Z Li, X Xia - IEEE Access, 2024 - ieeexplore.ieee.org
In the realm of wireless communications, accurate Radio Signal Received Power (RSRP)
prediction serves as the foundation for improving user experience and optimizing network …
prediction serves as the foundation for improving user experience and optimizing network …
Radio Map Estimation--An Open Dataset with Directive Transmitter Antennas and Initial Experiments
Over the last years, several works have explored the application of deep learning algorithms
to determine the large-scale signal fading (also referred to as``path loss'') between …
to determine the large-scale signal fading (also referred to as``path loss'') between …
An Efficient Wireless Propagation Loss Prediction Model Based on 3-D Terrain Features Extracted by Deep Learning
Y Chen, T Xiang, X Zhang - IEEE Antennas and Wireless …, 2022 - ieeexplore.ieee.org
This letter proposes a path loss prediction method based on a convolutional neural network
(CNN) by extracting features, such as terrain obstacles and building distribution. Twenty …
(CNN) by extracting features, such as terrain obstacles and building distribution. Twenty …
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Estimating path loss for a transmitter-receiver location is key to many use-cases including
network planning and handover. Machine learning has become a popular tool to predict …
network planning and handover. Machine learning has become a popular tool to predict …