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Conference Papers Year : 2022

Unsupervised deep learning to solve power allocation problems in cognitive relay networks

Abstract

In this paper, an unsupervised deep learning approach is proposed to solve the constrained and non-convex Shannon rate maximization problem in a relay-aided cognitive radio network. This network consists of a primary and a secondary user-destination pair and a secondary full-duplex relay performing Decode-and-Forward. The primary communication is protected by a Quality of Service (QoS) constraint in terms of tolerated Shannon rate degradation. The relaying operation leads to non-convex objective and primary QoS constraint, which makes deep learning approaches relevant and promising. For this, we propose a fully-connected neural network architecture coupled with a custom and communication-tailored loss function to be minimized during training in an unsupervised manner. A major interest of our approach is that the required training data contains only system parameters without the corresponding solutions to the non-convex optimization problem, as opposed to supervised approaches. Our numerical experiments show that our proposed approach has a high generalization capability on unseen data without overfitting. Also, the predicted solution performs close to the brute force one, highlighting the high potential of our unsupervised approach.
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Dates and versions

hal-03534545 , version 1 (19-01-2022)
hal-03534545 , version 2 (07-03-2022)
hal-03534545 , version 3 (10-03-2022)
hal-03534545 , version 4 (15-03-2022)

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Cite

Yacine Benatia, Anne Savard, Romain Negrel, Elena Veronica Belmega. Unsupervised deep learning to solve power allocation problems in cognitive relay networks. IEEE International Conference on Communications Workshops, ICC Workshops 2022, May 2022, Séoul, South Korea. ⟨10.1109/ICCWorkshops53468.2022.9814541⟩. ⟨hal-03534545v4⟩
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