A Novel Spectrum Sensing Method for Multiple Unknown Signal Sources Using Frequency Domain Energy Detection and DBSCAN

Spectrum sensing is a fundamental aspect of cognitive radio technology, tasked with detecting spectrum holes and unknown signal sources to improve spectrum management and resource utilization. While the energy detection (ED) method is widely used for spectrum sensing, it struggles with noise uncertainty (NU) and in low signal-to-noise ratio (SNR) environments. To address these challenges, we present a novel spectrum sensing approach called FDED-DBSCAN, which combines frequency domain energy detection (FDED), frequency domain noise estimation (FDNE), and the density-based spatial clustering of applications with noise (DBSCAN) algorithm; The DBSCAN algorithm clusters signals based on their time-frequency two-dimensional feature space. The proposed method was implemented using the GNU Radio toolkit for efficient acquisition and processing of in-phase and quadrature (I/Q) data from software-defined radio (SDR) hardware, and transformed this data into power spectral density (PSD) for analysis. The FDED algorithm detects signal occupancy in sub-channels, while FDNE calculates an adaptive threshold based on noise variance estimates. Multiple rounds of FDED detection are compiled into a two-dimensional signal detection matrix, which is subsequently analyzed by the DBSCAN algorithm to identify signal clusters. This integrated approach facilitates accurate detection of multiple unknown signals and their bandwidths over a given frequency range, even under low SNR conditions. Experimental validation across three scenarios—single-signal comparison with ED (Scenario I), Orthogonal Frequency Division Multiplexing (OFDM)/Narrowband signal detection (Scenario II), and real-world multi-signal acquisition including FM bands (Scenario III)—confirm FDED-DBSCAN’s superiority over conventional ED, particularly in unsupervised detection of unknown signals. This method demonstrates robust potential for practical applications in complex radio environments.