Device-to-device (D2D) communication is developed as a new paradigm to enhance net-
work performance according to LTE and WiMAX advanced standards. On the other hand,
cognitive radio (CR) approach provides efficient spectral usage using intelligent wireless
nodes. In this thesis, a number of optimal resource allocation strategies for D2D communi-
cation networks are investigated using the CR approach.
As a first step, the CR approach in radio access networks is introduced. In the second
step, the taxonomy of the RA process in CRNs is provided. For radio resource allocation
(RRA), the most crucial task is to associate a user with a particular serving base station, to
assign the channel and to allocate the power efficiently. In this thesis, a subcarrier assignment
scheme and a power allocation algorithm using geometric water-filling (GWF) is presented
for orthogonal frequency division multiplexing (OFDM) based CRNs. This algorithm is
proved to maximize the sum rate of secondary users by allocating power more efficiently.
Then, the RA problem is studied to jointly employ CR technology and D2D communication
in cellular networks in terms of spectral efficiency (SE) and energy efficiency (EE). In the
first case, in terms of SE, a two-stage approach is considered to allocate the radio resource
efficiently where a new adaptive subcarrier allocation (ASA) scheme is designed first and
then a novel power allocation (PA) scheme is developed utilizing proven GWF approach
that can compute exact solution with less computation. In the second case, in terms of EE,
the power allocation problem of cellular networks that co-exist with D2D communication
considering both underlay and overlay CR approaches are investigated. A proven power
allocation algorithm based on GWF approach is utilized to solve the EE maximization
problem which results in an “exact" and “low complexity" solution.
Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC
