Multi-Objective Optimization for Many-to-Many Communication in Cognitive Radio Networks

In this paper, we investigate the problem of routing for supporting many-to-many communication and optimizing the Quality of Service (QoS) for the Secondary Users (SUs) in Cognitive Radio Networks (CRN). The goal is to build the routing tree for many-to-many communication that optimizes the delay, rate and the number of transmission links. We model the network using the Multi-Layer Hyper Graph, and we model the optimization problem as an Integer Linear Program (ILP). We also study the problem of many-to-many communication scheduling in CRNs. We employ a modified version of an Ant Colony optimization algorithm (MOACS) to solve our problem of finding the routing tree that optimizes our objectives. Our simulation results show that the performance of MOACS outperforms the Shortest-Path Tree (SPT) in all our objectives.