| Abstract: |
The escalating demand for wireless spectrum, coupled with the inefficient utilization of licensed frequency bands, has established Cognitive Radio (CR) as a pivotal technology for next-generation wireless communication. Spectrum sensing forms the foundational layer of cognitive radio networks, enabling secondary users to opportunistically access underutilized spectrum without causing harmful interference to licensed primary users. This paper presents an empirical, data-driven evaluation of five widely deployed spectrum sensing techniques, namely Energy Detection (ED), Matched Filter Detection (MFD), Cyclostationary Feature Detection (CFD), Wavelet-based Detection (WD), and Cooperative Sensing (CS), under varying signal-to-noise ratio (SNR), false alarm probability, and network density conditions. Simulation-generated datasets were analyzed using probability of detection (Pd), probability of false alarm (Pfa), probability of missed detection (Pmd), sensing time, throughput, and computational complexity as performance indicators. Five tabulated datasets and five corresponding figures were constructed to quantify and visualize technique-wise performance trends. Results indicate that cooperative sensing achieves the highest detection reliability (Pd up to 0.95 at low SNR) at the cost of increased computational overhead and sensing latency, whereas energy detection remains computationally efficient but exhibits degraded performance below -10 dB SNR. Cyclostationary feature detection offers a favorable trade-off between robustness to noise uncertainty and complexity. The empirical relationships established between detection accuracy, sensing time, and cooperating user count provide quantitative guidance for selecting sensing strategies in practical cognitive radio deployments. The findings validate that hybrid and cooperative approaches substantially outperform standalone detectors, thereby strengthening the case for adaptive, context-aware spectrum sensing frameworks in future 5G/6G-enabled cognitive radio networks. |
