Performance Evaluation of Improved Energy Detection under Signal and Noise Uncertainties in Cognitive Radio Networks
Bansi Gaiera, Dhaval K. Patel, Briiesh Soni, Miguel López‐Benítez · 2019
Energy detection has proved to be a promising technique for spectrum sensing owning to its simplicity and low implementation and computational costs. However, the signals present in the sensed band are ambiguous and are not fully known beforehand which is referred to as the signal uncertainty. Signal and noise uncertainties are prone to degrade the detection performance. This paper presents an analysis of the impact of signal and noise uncertainties under an Improved Energy Detection (IED) algorithm for spectrum sensing. Step by step derivation and analysis of signal detection with signal and noise uncertainties under the considered IED algorithm are carried out in detail. The obtained analytical results are compared with experimental results obtained with a spectrum measurement platform, which not only demonstrate the validity of the mathematical analysis presented in this work but also show that IED outperforms the classical energy detection algorithm even in presence of both signal and noise uncertainties, an important fact of practical relevance that had not been demonstrated to the date in the existing literature.