Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

TL;DR

This paper introduces a new covariance-based spectrum sensing method for noncircular signals in cognitive radio networks with uncalibrated antennas, leveraging both covariance types for improved detection.

Contribution

The paper proposes the NCC method that exploits both covariance and complementary covariance for robust spectrum sensing of noncircular signals, with theoretical analysis and threshold derivation.

Findings

NCC outperforms existing spectrum sensing methods in simulations.

Theoretical decision threshold derived for the NCC statistic.

Method effectively utilizes noncircular signal properties.

Abstract

In this letter, the problem of spectrum sensing is addressed for noncircular (NC) signal in cognitive radio networks with uncalibrated multiple antennas. Specifically, by taking both the standard covariance and complementary covariance information of the NC signal into account, a new robust spectrum sensing method called NC covariance (NCC) is proposed, which can fully reap the statistical property of the NC signals. Meanwhile, we derive the asymptotic distribution of the NCC statistic under the signal-absence hypothesis and obtain the theoretical decision threshold of the NCC method. Simulation results demonstrate that the proposed method is capable of outperforming state-of-the-art methods.