A Covariance Absolute Value Detection Algorithm Exploiting Generalized Stochastic Resonance

Abstract

As an essential aspect of cognitive radio (CR), spectrum sensing has always been a research hotspot. In a multi-antenna scenario, the received signals from each antenna exhibit strong spatial correlation. Therefore, the covariance absolute value (CAV) detection algorithm is commonly employed, although its performance at low signal-to-noise ratio (SNR) needs improvement. This paper proposes an improved CAV detection algorithm that leverages generalized stochastic resonance (GSR) in multi-antenna scenarios. The research demonstrates that the performance of the CAV algorithm can be enhanced by introducing an appropriate direct current signal. By maximizing the probability of detection for a fixed probability of false alarm, the optimal amplitude of the additional direct current signal can be determined. Unlike previous work, this paper derives more exact formulas and considers a more general random signal model and a multi-antenna scenario. Theoretical analysis and simulation results confirm that the proposed method outperforms traditional CAV detection methods, particularly under low SNR conditions.

Jingjing Liu

Associate Professor

My research interests include low-power smart micro-sensor integrated circuit design, image sensors, biomedical sensors, and energy harvesting circuits.