  Floating Content (FC) is a communication paradigm for the local dissemination
of contextualized information through D2D connectivity, in a way which
minimizes the use of resources while achieving some specified performance
target. Existing approaches to FC dimensioning are based on unrealistic system
assumptions that make them, highly inaccurate and overly conservative when
applied in realistic settings. In this paper, we present a first step towards
the development of a cognitive approach to efficient dynamic management of FC.
We propose a deep learning strategy for FC dimensioning, which exploits a
Convolutional Neural Network(CNN) to efficiently modulate over time the
resources employed by FC in a QoS-aware manner. Numerical evaluations show that
our approach achieves a maximum rejection rate of3%, and resource savings of
37.5% with respect to the benchmark strategy
