# On Green Multicasting over Cognitive Radio Fading Channels

## TL;DR

This paper proposes an energy-efficient multicast scheme for underlay cognitive radio networks, optimizing power and rate adaptation under interference constraints, with an iterative algorithm demonstrating significant performance improvements.

## Contribution

It introduces a novel energy efficiency maximization framework for cognitive radio multicast networks with a non-quasi-concave optimization problem and an efficient iterative solution.

## Key findings

- Proposed scheme improves energy efficiency in cognitive multicast networks.
- Iterative algorithm effectively handles non-quasi-concave optimization.
- Simulation results show notable performance gains over existing methods.

## Abstract

In this paper, an underlay cognitive radio (CR) multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs' individual interference constraints. The optimization problem formulated for this is proved to be non quasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme.