SDR implementation of wideband spectrum sensing using machine learning

New cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method-based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach-based ML presents a higher probability of detection than the WBSS approach-based conventional detectors, even at low signal-to-noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios. Emerging cognitive radio (CR) systems require extensive throughput and bandwidth utilization. Consequently, CR users encounter the challenge of detecting wide frequency bands in the radio spectrum to utilize vacant frequency channels. This paper introduces a novel approach to wideband spectrum sensing (WBSS) aimed at enhancing detection performance by employing support vector machine (SVM) classification, along with two key features: energy and goodness of fit. Simulation results and the implementation of software defined radio confirm the effectiveness of the proposed WBSS approach under low signal-to-noise ratio conditions and in real-time detection scenarios. image