Research Article
Distributed Autocorrelation-Based Sequential Detection of OFDM Signals in Cognitive Radios
@INPROCEEDINGS{10.1109/CROWNCOM.2008.4562525, author={Sachin Chaudhari and Visa Koivunen and Vincent Poor}, title={Distributed Autocorrelation-Based Sequential Detection of OFDM Signals in Cognitive Radios}, proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2008}, month={7}, keywords={Sensing / Interference Radio Design / Physical Layer}, doi={10.1109/CROWNCOM.2008.4562525} }- Sachin Chaudhari
Visa Koivunen
Vincent Poor
Year: 2008
Distributed Autocorrelation-Based Sequential Detection of OFDM Signals in Cognitive Radios
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2008.4562525
Abstract
This paper addresses the problem of collaborative spectrum sensing using sequential detection (SD) in cognitive radios. The goal of sequential processing is to reduce the delay and amount of data needed in identifying underutilized spectrum. Each secondary user (SU) employs a simple and computationally efficient autocorrelation-based detector for Orthogonal Frequency Division Multiplexing (OFDM) signals of the primary user (PU). The decision statistics from individual detectors are combined in a fusion center that may be a separate node or one of the secondary users. The statistical properties of the decision statistics are established. The performance of the scheme is studied by theory and simulations. A comparison of the SD scheme with the Neyman-Pearson Fixed Sample Size (FSS) test for the same false alarm and missed detection probabilities is also carried out.