Transfer learning-based received power prediction with ray-tracing simulation and small amount of measurement data
M Iwasaki, T Nishio, M Morikura… - 2020 IEEE 92nd …, 2020 - ieeexplore.ieee.org
This paper proposes a method to predict received power in urban area deterministically,
which can learn a prediction model from small amount of measurement data by a simulation …
which can learn a prediction model from small amount of measurement data by a simulation …
Received power prediction for suburban environment based on neural network
Accurate received power prediction is important to wireless network planning and
optimization, and appropriate channel modeling approach is highly demanded. The existing …
optimization, and appropriate channel modeling approach is highly demanded. The existing …
Indoor experiments on transfer learning-based received power prediction
M Iwasaki, T Nishio, M Morikura, K Yamamoto… - IEICE Proceedings …, 2020 - ieice.org
This paper proposes a method to predict received power in indoor environments
deterministically, which can learn a prediction model from small amount of measurement …
deterministically, which can learn a prediction model from small amount of measurement …
A formulation-aid transfer learning-based framework in received power prediction
KN Nguyen, K Takizawa - IEICE Communications Express, 2023 - jstage.jst.go.jp
This study is motivated by the demand for an efficient deep learning-based model that helps
us predict the future link quality for intelligent decision-making systems. In this letter, we …
us predict the future link quality for intelligent decision-making systems. In this letter, we …
A study on the variety and size of input data for radio propagation prediction using a deep neural network
T Hayashi, T Nagao, S Ito - 2020 14th European Conference …, 2020 - ieeexplore.ieee.org
Not only has the volume of mobile traffic been increasing exponentially in recent years,
making various services available, such as IoT and connected cars moving at high speed …
making various services available, such as IoT and connected cars moving at high speed …
Highly accurate prediction of radio propagation using model classifier
K Katagiri, K Onose, K Sato, K Inage… - 2019 IEEE 89th …, 2019 - ieeexplore.ieee.org
In this paper, we propose a measurement-based spectrum database using model classifier.
In the radio propagation, path loss is the fundamental factor to recognize the coverage area …
In the radio propagation, path loss is the fundamental factor to recognize the coverage area …
Spatial signal strength prediction using 3D maps and deep learning
Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied
to simulating complex physics by learning physics models thanks to large data. Inspired by …
to simulating complex physics by learning physics models thanks to large data. Inspired by …
Feature extraction in reference signal received power prediction based on convolution neural networks
Z Yi, L Zhiwen, H Rong, W Ji, X Wenwu… - IEEE …, 2021 - ieeexplore.ieee.org
In this letter, an environmental features (EFs) extraction model is proposed for estimating
reference signal received power (RSRP) accurately. Firstly, 18-D measured data is …
reference signal received power (RSRP) accurately. Firstly, 18-D measured data is …
Study on radio propagation prediction by machine learning using urban structure maps
T Nagao, T Hayashi - 2020 14th European Conference on …, 2020 - ieeexplore.ieee.org
In recent years, mobile data traffic has been increasing, and high-quality mobile
communication services are required. Therefore, it is essential to understand the complex …
communication services are required. Therefore, it is essential to understand the complex …
Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System
T Ngenjaroendee, W Phakphisut… - … on Circuits/Systems …, 2022 - ieeexplore.ieee.org
A highly accurate prediction of radio signal power is crucial for planning the coverage of
mobile networks. Currently, a path loss model is most widely used to predict the radio signal …
mobile networks. Currently, a path loss model is most widely used to predict the radio signal …