LMPIT-Inspired Tests for Detecting a Cyclostationary Signal in Noise With Spatio-Temporal Structure Articles uri icon

publication date

  • September 2018

start page

  • 6321

end page

  • 6334

volume

  • 17

International Standard Serial Number (ISSN)

  • 1536-1276

Electronic International Standard Serial Number (EISSN)

  • 1558-2248

abstract

  • In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework.

keywords

  • Cyclostationarity; detection; generalized likelihood ratio test (GLRT); interweave cognitive radio; locally most powerful invariant test (LMPIT); spectrum sensing