Transfer Learning for Tilt-Dependent Radio Map Prediction

@article{Parera2020TransferLF,
  title={Transfer Learning for Tilt-Dependent Radio Map Prediction},
  author={Claudia Parera and Qi Liao and Ilaria Malanchini and Cristian Tatino and A. Redondi and M. Cesana},
  journal={IEEE Transactions on Cognitive Communications and Networking},
  year={2020},
  volume={6},
  pages={829-843}
}
Machine learning will play a major role in handling the complexity of future mobile wireless networks by improving network management and orchestration capabilities. Due to the large number of parameters that can be monitored and configured in the network, collecting and processing high volumes of data is often unfeasible or too expensive at network runtime, which calls for taking resource management and service orchestration decisions with only a partial view of the network status. Motivated… Expand
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