Cognitive Network Inference through Bayesian Network Analysis

Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios that apply cognition only at the physical layer. Designing a cognitive network is challenging since learning the relationship between network protocol parameters in an automated fashion is very complex. We propose to use Bayesian Network (BN) models for creating a representation of the dependence relationships among network protocol parameters. BN is a unique tool for modeling the network protocol stack as it not only learns the probabilistic dependence of network protocol parameters but also provides an opportunity to tune some of the cognitive network parameters to achieve desired performance. To the best of our knowledge, this is the first work to explore the use of BNs for cognitive networks. Creating a BN model for network parameters involves the following steps: sampling the network protocol parameters ...

• Type: Journal
• Year: 2010
• Where: GLOBECOM
• Authors: Giorgio Quer, Hemanth Meenakshisundaram, Tamma Bheemarjuna Reddy, B. S. Manoj, Ramesh Rao, Michele Zorzi
• Keywords: Cognitive Network, Communications, GLOBECOM 2010, Network Protocol, Network Protocol Parameters