Construction of Energy Detection Model in Cognitive Radio Spectrum Sensing

In cognitive radio spectrum sensing, the construction of an energy detection model is crucial. This model determines the presence of primary signals by detecting energy levels within specific frequency bands. The detected energy signals are further processed to calculate the Signal-to-Noise Ratio (SNR), which serves as a key metric for evaluating signal quality. Based on this framework, two critical parameters can be computed: the probability of false alarm (pf) and probability of detection (pd). These parameters are essential performance indicators that reveal the system's effectiveness in detecting signals across different SNR conditions. From an implementation perspective, the energy detection algorithm typically involves calculating the test statistic by summing squared signal samples over an observation window, comparing it against a predetermined threshold. The detection threshold is often set based on the noise floor estimation and desired false alarm probability. Therefore, constructing an accurate energy detection model is fundamental to cognitive radio spectrum sensing research, enabling reliable spectrum occupancy decisions and dynamic spectrum access capabilities.