Location-aided Distributed Primary User Identification in a Cognitive Radio Scenario

TL;DR

This paper introduces two distributed algorithms for primary user identification in cognitive radio networks, leveraging limited location data and detection pre-processing to improve accuracy and efficiency.

Contribution

It proposes novel fully distributed algorithms for primary user identification, including a detection pre-processing step, with analytical and simulation validation.

Findings

Detection pre-processing improves identification accuracy.

Algorithms are effectively implemented via consensus averaging.

Analytical results match simulation outcomes.

Abstract

We address a cognitive radio scenario, where a number of secondary users performs identification of which primary user, if any, is transmitting, in a distributed way and using limited location information. We propose two fully distributed algorithms: the first is a direct identification scheme, and in the other a distributed sub-optimal detection based on a simplified Neyman-Pearson energy detector precedes the identification scheme. Both algorithms are studied analytically in a realistic transmission scenario, and the advantage obtained by detection pre-processing is also verified via simulation. Finally, we give details of their fully distributed implementation via consensus averaging algorithms.