@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 {42562,
	title = {Resource Allocation for Underlay Interfering D2D Networks with Multi-antenna and Imperfect CSI},
	journal = {IEEE Transactions on Communications},
	volume = {70},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK Grant WISECART 250910/F20 from the Research Council of Norway.},
	month = {09/2022},
	pages = {6066-6082},
	publisher = {IEEE},
	abstract = {Underlay Device-to-Device (D2D) communications improve the spectral efficiency by simultaneously allowing direct communication between D2D-users on the same channels as cellular users (CU). However, most related works consider perfect Channel State Information (CSI) with single-antenna transmissions and usually assign each channel to one D2D pair. In this work, we formulate an optimization problem for maximizing the aggregate rate of all D2D pairs and CUs in single and multiple antenna configurations under imperfect CSI, by optimizing channel and power resources. Our formulation guarantees probability of outage below a specified threshold and fairness in channel allocation across D2D pairs. The resulting problem is a stochastic-mixed-integer-non-convex problem, we solve it approximately by alternating between power-allocation and channel-assignment sub-problems. The stochastic objective and outage constraints are addressed by the concept of order-of-statistics in the single-antenna case and the Bernstein-type inequality in the multiple-antenna configuration. The power-allocation sub-problem is solved by exploiting a quadratic-transformation, while the channel-assignment sub-problem is solved by integer relaxation. Furthermore, two computationally efficient algorithms are proposed to approximately solve the problem in a partially decentralized manner. We also establish convergence guarantees for the different algorithms proposed in this work. Simulation results show that the proposed approach achieves higher throughput compared to the state-of-the-art alternatives.},
	issn = {0090-6778},
	doi = {10.1109/TCOMM.2022.3194193},
	url = {https://ieeexplore.ieee.org/document/9841599},
	author = {Elnourani, Mohamed and Deskmukh, Siddharth and Beferull-Lozano, Baltasar}
}
@inproceedings {42585,
	title = {Risk-Aware Particle-Filtering for State Estimation in Recirculating Aquaculture Systems},
	journal = {IEEE Asilomar Conference on Signals, Systems, and Computers},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by\&nbsp; the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	publisher = { IEEE},
	author = {Juan Diego {Cardenas-Cartagena} and Elnourani, Mohamed and Beferull-Lozano, Baltasar and Romero, Daniel}
}
@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}
}
