  In this paper, a deep learning (DL) framework for the optimization of the
resource allocation in multi-channel cellular systems with device-to-device
(D2D) communication is proposed. Thereby, the channel assignment and discrete
transmit power levels of the D2D users, which are both integer variables, are
optimized to maximize the overall spectral efficiency whilst maintaining the
quality-of-service (QoS) of the cellular users. Depending on the availability
of channel state information (CSI), two different configurations are
considered, namely 1) centralized operation with full CSI and 2) distributed
operation with partial CSI, where in the latter case, the CSI is encoded
according to the capacity of the feedback channel. Instead of solving the
resulting resource allocation problem for each channel realization, a DL
framework is proposed, where the optimal resource allocation strategy for
arbitrary channel conditions is approximated by deep neural network (DNN)
models. Furthermore, we propose a new training strategy that combines
supervised and unsupervised learning methods and a local CSI sharing strategy
to achieve near-optimal performance while enforcing the QoS constraints of the
cellular users and efficiently handling the integer optimization variables
based on a few ground-truth labels. Our simulation results confirm that
near-optimal performance can be attained with low computation time, which
underlines the real-time capability of the proposed scheme. Moreover, our
results show that not only the resource allocation strategy but also the CSI
encoding strategy can be efficiently determined using a DNN. Furthermore, we
show that the proposed DL framework can be easily extended to communications
systems with different design objectives.
