Sum rate maximization of D2D networks with energy constrained UAVs through deep unsupervised learning


We consider a system model in which several energy harvesting (EH) unmanned aerial vehicles (UAVs), often known as drones, are deployed with device-to-device (D2D) communication networks. For the considered system model, we formulate an optimization problem that aims to find an optimal transmit power vector which maximizes the sum rate of the D2D network while also meets the minimum energy requirements of the UAVs. Because of the nature of the system model, it is necessary to deliver solutions in real time i.e., within a channel coherence time. As a result, conventional non-data-driven optimization methods are inapplicable, as either their run-time overheads are prohibitively expensive or their solutions are significantly suboptimal. In this paper, we address this problem by proposing a deep unsupervised learning (DUL) based hybrid scheme in which a deep neural network (DNN) is complemented by the full power scheme. It is shown through simulations that our proposed hybrid scheme provides up to 91% higher sum rate than an existing fully non-data driven scheme and our scheme is able to obtain solutions quite efficiently, i.e., within a channel coherence time.

Presented at IEEE UEMCON 2021, New York, USA
Debaditya Shome
Debaditya Shome
Graduate researcher experienced in AI / ML

My research interests include Self-supervised learning, Generative learning, Computer Vision and it’s interplay with different modalities of data such as natural language and audio.