Real-Time Implementation of DSSS Signal Detection Based on Autocorrelation Estimation and Cumulative Peak-to-Average Ratio Using SDR Platforms

The paper introduces a novel method for detecting Direct-Sequence Spread-Spectrum (DSSS) signals through autocorrelation estimation and cumulative peak-to-average ratio (CPAR). DSSS signals pose a challenge for traditional spectrum sensing methods due to their low power and spread-spectrum nature. This paper proposes an implementation of method based on autocorrelation estimation to address these challenges by identifying peaks in the autocorrelation function and applying a CPAR criterion to distinguish DSSS signals from noise and interference. This approach offers several advantages, including robustness at low signal-to-noise ratios (SNR), no requirement for prior information about the signal, and real-time implementation capability. Simulation results demonstrate the effectiveness of the proposed method, making it a promising solution for real-time spectrum sensing in cognitive radio applications.