@inproceedings {42574,
	title = {Deep Transfer Learning Based Radio Map Estimation for Indoor Wireless Communications},
	journal = {IEEE International Conference on Signal Processing Advances in Wireless Communications (SPAWC)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK WISECART grant 250910/F20 from the Research Council of Norway.},
	publisher = { IEEE},
	author = {Rahul K. {Jaiswal} and Elnourani, Mohamed and Deshmukh, Siddharth and Beferull-Lozano, Baltasar}
}
@inproceedings {28423,
	title = {Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications},
	journal = {IEEE Wireless Communications and Networking Conference (WCNC)},
	year = {2022},
	note = {This research work was carried out at University of Agder with funding from the FRIPRO TOPPFORSK WISECART grant 250910/F20, and completed after the SIGIPRO Department was created.},
	publisher = {IEEE},
	abstract = {In this paper, we investigate the application of transfer learning to train a DNN model for joint channel and power allocation in underlay Device-to-Device communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect CSI is not computationally demanding, compared to scenarios with imperfect CSI. Thus, a transfer learning-based approach can be exploited to transfer the DNN model trained for the perfect CSI scenarios to the imperfect CSI scenarios. We also consider the issue of defining the similarity between two types of resource allocation tasks. For this, we first determine the value of outage probability for which two resource allocation tasks are same, that is, for which our numerical results illustrate the minimal need of relearning from the transferred DNN model. For other values of outage probability, there is a mismatch between the two tasks and our results illustrate a more efficient relearning of the transferred DNN model. Our results show that the learning dataset required for relearning of the transferred DNN model is significantly smaller than the required training dataset for a DNN model without transfer learning.\&nbsp;\&nbsp;},
	author = {Rahul K. {Jaiswal} and Deshmukh, Siddharth and Elnourani, Mohamed and Beferull-Lozano, Baltasar}
}
