Centralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback

In this paper, a centralized Power Control (PC)
scheme and an interference channel learning method are jointly
tackled to allow a Cognitive Radio Network (CRN) access to
the frequency band of a Primary User (PU) operating based
on an Adaptive Coding and Modulation (ACM) protocol. The
learning process enabler is a cooperative Modulation and Coding
Classification (MCC) technique which estimates the Modulation
and Coding scheme (MCS) of the PU. Due to the lack of
cooperation between the PU and the CRN, the CRN exploits
this multilevel MCC sensing feedback as implicit channel state
information (CSI) of the PU link in order to constantly monitor
the impact of the aggregated interference it causes. In this paper,
an algorithm is developed for maximizing the CRN throughput
(the PC optimization objective) and simultaneously learning how
to mitigate PU interference (the optimization problem constraint)
by using only the MCC information. Ideal approaches for this
problem setting with high convergence rate are the cutting
plane methods (CPM). Here, we focus on the analytic center
cutting plane method (ACCPM) and the center of gravity cutting
plane method (CGCPM) whose effectiveness in the proposed
simultaneous PC and interference channel learning algorithm is
demonstrated through numerical simulations.