  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.
