A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics

doi:doi:10.57702/lr8o0z6s

A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel characteristics data.

BibTex:

@dataset{Xiping_Wang_and_Zhao_Zhang_and_Danping_He_and_Ke_Guan_and_Dongliang_Liu_and_Jianwu_Dou_and_Bo_Sun_2024,
    abstract = {Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel characteristics data.},
    author = {Xiping Wang and Zhao Zhang and Danping He and Ke Guan and Dongliang Liu and Jianwu Dou and Bo Sun},
    doi = {10.57702/lr8o0z6s},
    institution = {No Organization},
    keyword = {'convolutional neural network', 'multi-task learning', 'ray-tracing', 'super resolution', 'wireless channel modeling'},
    month = {dec},
    publisher = {TIB},
    title = {A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics},
    url = {https://service.tib.eu/ldmservice/dataset/a-multi-task-learning-model-for-super-resolution-of-wireless-channel-characteristics},
    year = {2024}
}