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

Author:

Wang Xiping1,Zhang Zhao1,He Danping1,Guan Ke1,Liu Dongliang2,Dou Jianwu2,Mumtaz Shahid3,Al-Rubaye Saba4

Affiliation:

1. Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044

2. State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen,China,518055

3. Instituto de Telecomunicações,Aveiro,Portugal,1049-001

4. School of Aerospace, Transport and Manufacturing, Cranfield University, Central Bedfordshire,Cranfield,UK,2717

Funder

Fundamental Research Funds for the Central Universities

National Science Foundation of China

Beijing Natural Science Foundation

State Key Laboratory of Rail Traffic Control and Safety

Publisher

IEEE

Reference19 articles.

1. An intelligent electromagnetic environment reconstruction method based on super-resolution generative adversarial network

2. Radionet: Transformer based radio map prediction model for dense urban environments;tian;ar Xiv preprint,2021

3. The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial

4. Multi-task learning with dynamic splitting for open-set wireless signal recognition;xu;ZTE Communications,2022

5. RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks

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