@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}
}
@article {28424,
	title = {Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {73},
	number = {3},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK under Grant WISECART 250910/F20 from the Research Council of Norway.},
	pages = {3075-3091},
	publisher = {IEEE},
	abstract = {Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel state information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the art alternatives.},
	issn = {0018-9545},
	doi = {10.1109/TVT.2022.3145011},
	author = {Elnourani, Mohamed and Deshmukh, Siddharth and Beferull-Lozano, Baltasar and Romero, Daniel}
}
@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}
}
@article {28421,
	title = {Energy Efficient AoI Minimization in Opportunistic NOMA/OMA Broadcast Wireless Networks},
	journal = {IEEE Transactions on Green Communications and Networking},
	year = {2021},
	note = {This research work was carried out at University of Agder and completed after the SIGIPRO Department was created. This work was supported by the Research Council of Norway through FRIPRO TOPPFORSK under Grant WISECART 250910/F20.},
	publisher = {IEEE},
	address = {IEEE Transactions on Green Communications and Networking},
	abstract = {The concept of Age of Information (AoI) minimization in wireless networks has garnered huge interest in recent times. While current literature focuses on scheduling for AoI minimization, there is also a need to efficiently utilize the underlying physical layer resources. In this paper, we consider the problem of energy-efficient scheduling for AoI minimization in an opportunistic NOMA/OMA downlink broadcast wireless network, where the user equipment operate with diverse QoS requirements. We first formulate a resource allocation problem to minimize the average AoI of the network, with energy-efficiency factored in by restricting the long term average transmit power to a predetermined threshold. A heuristic adaptation of the driftplus-penalty approach from the Lyapunov framework is then utilized to solve the original long-term mixed-integer nonlinear problem on a per time-slot basis. The single time-slot problem is further decomposed into multiple sub-problems, solving for power allocation and user scheduling separately. However, the attained power allocation sub-problems being non-convex, we propose an efficient piece-wise linear approximation to obtain a tractable solution. The scheduling sub-problem is solved optimally by using the integrality property of the linear program. Finally, we provide extensive numerical simulations to show that our proposed approach outperforms the state of the art.},
	doi = {10.1109/TGCN.2021.3135351},
	author = {Sharan, Ravi and Deshmukh, Siddharth and Sibi R. B. {Pillai} and Beferull-Lozano, Baltasar}
}
