Spectrum Sensing: Enhanced Energy Detection Technique Based on Noise Measurement

Spectrum sensing enables cognitive radio systems to detect unused portions of the radio spectrum and then use them while avoiding interferences to the primary users. Energy detection is one of the most used techniques for spectrum sensing because it does not require any prior information about the characteristics of the primary user signal. However, this technique does not distinguish between the signal and the noise. It has a low performance at low SNR, and the selection of the threshold becomes an issue because the noise is uncertain. The detection performance of this technique can be further improved using a dynamic selection of the sensing threshold. In this work, we investigate a dynamic selection of this threshold by measuring the power of noise present in the received signal using a blind technique. The proposed model was implemented and tested using GNU Radio software and USRP units. Our results show that the dynamic selection of the threshold based on measuring the noise level present in the received signal during the detection process increases the probability of detection and decreases the probability of false alarm compared to the ones of energy detection with a static threshold.

💡 Research Summary

Spectrum sensing is a cornerstone of cognitive radio (CR) systems, enabling secondary users to opportunistically exploit underutilized spectrum while protecting primary users from interference. Among the various sensing techniques, energy detection (ED) is the most widely adopted because it requires no a priori knowledge of the primary signal’s modulation, bandwidth, or coding. However, the simplicity of ED comes at a cost: it cannot differentiate between signal energy and noise energy, leading to poor detection performance in low‑signal‑to‑noise‑ratio (SNR) regimes and making the choice of a detection threshold problematic when the noise floor is uncertain.

The present paper addresses this fundamental limitation by introducing a dynamic threshold selection method that relies on blind measurement of the noise power present in the received waveform. The authors propose a two‑stage algorithm. In the first stage, the received complex samples are segmented into frames of N samples. For each frame the average squared magnitude (i.e., the frame energy) is computed. By constructing a histogram of these frame energies and applying a median‑filter‑based outlier rejection, the algorithm isolates frames that are most likely to contain only noise. The mean of the selected frames provides an estimate of the noise variance σ²_n without requiring any knowledge of the primary signal.

In the second stage, the estimated noise variance is inserted into the classic Neyman‑Pearson threshold formula for energy detection:

 λ = σ²_n ·