Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise Ratio

TL;DR

This paper introduces a covariance matrix-based spectrum sensing algorithm for cognitive radio that effectively detects signals at extremely low signal-to-noise ratios, such as below -30 dB, with limited data.

Contribution

It presents a novel detection method leveraging a monotonic property of a matrix function, enabling reliable spectrum sensing under very low SNR conditions.

Findings

Effective detection at SNR below -30 dB

Outperforms existing methods in simulations

Theoretical threshold setting analysis

Abstract

Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it works under extremely low signal-to-noise ratio, like lower than -30 dB with limited sample data. Theoretical analysis of threshold setting for the algorithm is discussed. A performance comparison between the proposed algorithm and other state-of-the-art methods is provided, by the simulation on captured digital television (DTV) signal.