Distributed Data Fusion over Correlated Log-Normal Sensing and Reporting Channels: Application to Cognitive Radio Networks

In this Letter, we propose an advanced framework for performance analysis and design of decentralized data fusion problems. In particular, the performance of a multi-layer system setup for data detection, which includes realistic sensing/reporting channels and correlated Log-Normal shadow-fading in all wireless links of the cooperative network, will be studied. The system setup will be used to analyze the performance of cooperative spectrum sensing problems adopting an Amplify-and-Forward (AF) relay protocol. We will show that, even though often overlooked in typical cooperative spectrum sensing analysis, shadowing correlation on the reporting channel can yield similar performance degradations as shadowing correlation on the sensing channel. All findings will be substantiated via theoretical arguments and Monte Carlo simulations, and, in particular, novel approximation methods to account for correlated Log-Normal shadowing in cooperative spectrum sensing problems will be introduced in this paper.