  The basic idea of device-to-device (D2D) communication is that pairs of
suitably selected wireless devices reuse the cellular spectrum to establish
direct communication links, provided that the adverse effects of D2D
communication on cellular users is minimized and cellular users are given a
higher priority in using limited wireless resources. Despite its great
potential in terms of coverage and capacity performance, implementing this new
concept poses some challenges, in particular with respect to radio resource
management. The main challenges arise from a strong need for distributed D2D
solutions that operate in the absence of precise channel and network knowledge.
In order to address this challenge, this paper studies a resource allocation
problem in a single-cell wireless network with multiple D2D users sharing the
available radio frequency channels with cellular users. We consider a realistic
scenario where the base station (BS) is provided with strictly limited channel
knowledge while D2D and cellular users have no information. We prove a
lower-bound for the cellular aggregate utility in the downlink with fixed BS
power, which allows for decoupling the channel allocation and D2D power control
problems. An efficient graph-theoretical approach is proposed to perform the
channel allocation, which offers flexibility with respect to allocation
criterion (aggregate utility maximization, fairness, quality of service
guarantee). We model the power control problem as a multi-agent learning game.
We show that the game is an exact potential game with noisy rewards, defined on
a discrete strategy set, and characterize the set of Nash equilibria.
Q-learning better-reply dynamics is then used to achieve equilibrium.
