Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Stefanos Bakirtzis"'
Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62789869e27a2f0789ad5dbec7dfcf41
https://doi.org/10.36227/techrxiv.17306384
https://doi.org/10.36227/techrxiv.17306384
Deep learning (DL) has been recently leveraged for the inference of characteristics related to wireless communication channels, such as path loss (PL). This paper presents how a deep convolutional encoder-decoder, namely a path loss prediction net (P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5257675a52a04dedd29ea4f4fb8fd66f
Cell densification through the installation of small- cells and femtocells in indoor environments is an emerging solution to enhance the operation of wireless networks. The deployment of new components within the heart of the radio access network cal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c097903d99bd3dde8d5257a0f4a5c12
https://hdl.handle.net/20.500.12761/1612
https://hdl.handle.net/20.500.12761/1612
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 propagation solvers with machine learning (ML) have been made. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e8d8eb9be31e7c8f0814720e86c4fd1
In this letter we present our results on how deep learning can be leveraged for outdoor path loss prediction in the 30GHz band. In particular, we exploit deep learning to boost the performance of outdoor path loss prediction in an end-to-end manner.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3ac0f88c358a63b8f31d5f7b53822df
Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a427930615d28b3adf865973976af385
Publikováno v:
IEEE Transactions on Microwave Theory and Techniques. 69:500-508
Waveguide components at terahertz (THz) frequencies suffer from increased conductor losses. These losses are further exacerbated by surface roughness. In this article, we introduce an expedient approach for modeling surface roughness, which is suitab
Publikováno v:
2020 IEEE/MTT-S International Microwave Symposium (IMS).
With 5G communication systems along with several radar and imaging technologies employing millimeter waves, accurately modeling wave propagation at these frequencies is as important as ever. At these frequencies, surface roughness of waveguide compon
Autor:
Costas D. Sarris, Stefanos Bakirtzis
Publikováno v:
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting.
Ray Tracing (RT) has been widely used to evaluate the characteristics of wireless channels in indoor and outdoor environments. However, for RT to be accurate at mm-wave frequencies, it is essential to account for the effect of diffuse scattering, esp
Publikováno v:
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting.
Ray-tracing (RT) provides an accurate and cost-effective way to perform propagation modeling, assisting in the planning and optimization of wireless communication systems. To exploit its full potential in the challenging task of mm-wave channel model