Performance evaluation of cooperative cognitive radio networks with data/decision fusion

Abstract

In this article, we develop an analytical framework for performance evaluation and comparison between decision and data fusion rules for cooperative relay based cognitive radio networks. Our unified analytical formulas for the data fusion rules are sufficiently general to tackle generalized stochastic channel models with independent but non-identically distributed (i.n.d) link statistics. We also derive a unified expression for computing the effective detection and false alarm probabilities for multi-relay networks with k-out-of-N decision fusion rules (which includes “AND”, “OR”, and the “Majority-Rule” as special cases). While the decision fusion rule yields a slightly better performance than the data fusion rule with a single cooperating relay, we observe that the performance of data fusion schemes lie in between the extremes of “AND” and “OR” rules of decision fusion for multi-relay networks. The maximal-ratio combining (MRC) based data fusion rule outperforms the square-law combining (SLC) counterpart, as anticipated. However, a different value of “k” in the k-out-of-N decision fusion rule outperforms others depending on the system parameters. As such, an adaptive decision fusion rule will more advantageous than a decision fusion rule with a fixed k value.