Physics-informed machine learning models for indoor Wi-Fi access point placement
D Cui, G Yang, S Ji, S Luo, A Seretis… - … on Antennas and …, 2021 - ieeexplore.ieee.org
2021 IEEE International Symposium on Antennas and Propagation and …, 2021•ieeexplore.ieee.org
One of the main challenges in optimally placing indoor Wi-Fi access points in a complex
indoor environment is the estimation of the received signal strength (RSS) given different
access point locations. This paper proposes a deep learning approach, a modification to the
classic Deep Convolutional Generative Adversarial Network (DCGAN), to generate accurate
power maps for a specific indoor geometry. It has been demonstrated that this model
consistently outperforms a benchmark ray-tracing simulator in efficiency, maintaining a …
indoor environment is the estimation of the received signal strength (RSS) given different
access point locations. This paper proposes a deep learning approach, a modification to the
classic Deep Convolutional Generative Adversarial Network (DCGAN), to generate accurate
power maps for a specific indoor geometry. It has been demonstrated that this model
consistently outperforms a benchmark ray-tracing simulator in efficiency, maintaining a …
One of the main challenges in optimally placing indoor Wi-Fi access points in a complex indoor environment is the estimation of the received signal strength (RSS) given different access point locations. This paper proposes a deep learning approach, a modification to the classic Deep Convolutional Generative Adversarial Network (DCGAN), to generate accurate power maps for a specific indoor geometry. It has been demonstrated that this model consistently outperforms a benchmark ray-tracing simulator in efficiency, maintaining a comparable accuracy.
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