Citation: Li, S.; Han, Y.; Gaber, J.; Yang, S.; Yang, Q. A Multi-Antenna Spectrum Sensing Method Based on CEEMDAN Decomposition Combined with Wavelet Packet Analysis. Electronics 2023, 12, 3823. https://doi.org/10.3390/ electronics12183823 Academic Editor: Matteo Bruno Lodi Received: 6 August 2023 Revised: 6 September 2023 Accepted: 7 September 2023 Published: 9 September 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article A Multi-Antenna Spectrum Sensing Method Based on CEEMDAN Decomposition Combined with Wavelet Packet Analysis Suoping Li 1, * , Yuzhou Han 1, *, Jaafar Gaber 2 , Sa Yang 1 and Qian Yang 1 1 School of Electrical & Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; ys_sayang@126.com (S.Y.); ystrong4@163.com (Q.Y.) 2 Department of Computer Science and Computer Engineering, Universite de Technologie Belfort-Montbeliard, 90010 Belfort, France; gaber@utbm.fr * Correspondence: lsuop@lut.edu.cn (S.L.); 19119376252@163.com (Y.H.) Abstract: In many practical communication environments, the presence of uncertain and hard-to- estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distribution. This paper introduces a cutting- edge multi-antenna spectrum sensing methodology that synergistically integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet packet analysis, and differential entropy. Signal feature extraction commences by employing CEEMDAN decomposition and wavelet packet analysis to denoise signals collected by secondary antenna users. Subsequently, the differential entropy of the preprocessed signal observations serves as the feature vector for spectrum sensing. The spectrum sensing module utilizes the SVM classification algorithm for training, while incorporating elite opposition-based learning and the sparrow search algorithm with genetic variation to determine optimal kernel function parameters. Following successful training, a decision function is derived, which can obviate the need for threshold derivation present in conventional spectrum sensing methods. Experimental validation of the proposed methodology is conducted and comprehensively analyzed, conclusively demonstrating its remarkable efficacy in enhancing spectrum sensing performance. Keywords: spectrum sensing; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); wavelet packet analysis; improvement sparrow search algorithm; SVM classification; machine learning 1. Introduction In recent years, static spectrum allocation strategies struggled to meet the growing demand for spectrum resources. Within dedicated frequency bands, substantial portions of spectrum resources remain unused, both in terms of time and frequency domains [1]. While static allocation strategies effectively manage interference among communication systems operating in separate frequency bands, they exhibit inherent inflexibility. When primary users either refrain from utilizing their allocated spectrum for extended periods or use it intermittently, these idle frequency bands become inaccessible to other radio users. To address this contradiction, Dr. Mitola proposed the concept of cognitive radio (CR) [2]. As a new wireless communication technology, CR became an ideal solution to improve spectrum utilization, and spectrum sensing plays a crucial role in CR. Several common spectrum sensing methods were proposed in references [3–5]. The energy detection algorithm compares the received signal energy with a threshold to deter- mine the presence of a signal [3]. The matched filter detection correlates the received signal with a known transmitted signal to detect the existence of a primary user signal [4]. The cyclostationary feature detection adjusts the number of samples involved in real time based Electronics 2023, 12, 3823. https://doi.org/10.3390/electronics12183823 https://www.mdpi.com/journal/electronics