  Device-to-device (D2D) communication, which enables a direct connection
between users while bypassing the cellular channels to base stations (BSs), is
a promising way to offload the traffic from conventional cellular networks. In
D2D communication, one recurring problem is that, in order to optimally
allocate resources across D2D and cellular users, the knowledge of D2D channel
gains is needed. However, such knowledge is hard to obtain at reasonable
signaling costs. In this paper, we show this problem can be circumvented by
tapping into the information provided by the estimation of the cellular
channels between the users and surrounding BSs as this estimation is done
anyway for a normal operation of the network. While the cellular and D2D
channel gains exhibit independent fast fading behavior, we show that average
gains of the cellular and D2D channels share a non-explicit correlation
structure, which is rooted into the network topology, terrain, and buildings
setup. We propose a machine (deep) learning approach capable of predicting the
D2D channel gains from seemingly independent cellular channels. Our results
show a high degree of convergence between true and predicted D2D channel gains.
The predicted gains allow to reach a near-optimal communication capacity in
many radio resource management algorithms.
