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

TL;DR

This paper presents a centralized power control scheme for cognitive radio networks that uses modulation and coding classification feedback to optimize throughput while minimizing interference with primary users, employing cutting plane methods for fast convergence.

Contribution

It introduces a novel algorithm combining power control and interference mitigation using MCC feedback, with a focus on analytic and gravity center cutting plane methods for efficient learning.

Findings

The proposed algorithms effectively maximize CRN throughput.

The MCC feedback enables accurate interference channel estimation.

Cutting plane methods achieve high convergence rates in the learning process.

Abstract

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.