## 1 Introduction  
The need to make better use of the radio spectrum is leading to the development of new spectrum access strategies. Among these strategies, the opportunistic spectrum access based on the cognitive radio concepts allows the sharing of a spectral bandwidth between two categories of users: Primary User \"PU\" and Secondary User \"SU\". The PU holds the license to exploit the bandwidth, and the SU is an opportunistic user willing to use the channel when the PU is idle. One of the most crucial challenges for the SU is the identification of a free bandwidth by conducting a spectrum sensing [1,2]. Many reliable spectrum sensing methods have been developed to help the SU limit their interference to the PU's transmission [3, 4, 5, 6, 7, 8, 9, 10, 11]. Among the spectrum sensing approaches, we can mention Waveform Detection (WFD) [12], Cyclostationary-Features-based Detection (CFD) [13], and Energy-based Detection (ED) [14, 15]. One of the most reliable methods, WFD, requires prior knowledge of the PU's signal characteristics. Based on the cyclic spectrum estimation, the CFD requires a relatively high computational cost for a high-frequency resolution. ED is the simplest detection method, but it is unable to distinguish a communication signal from an energetic noise when the noise is not a weak-sense stationary stochastic process or the signal-to-noise ratio (SNR) is very low. Recently, spectrum sensing algorithms, based on the promising concept of machine or deep learning, have been proposed [16, 17, 18, 19]. However, these algorithms do not perform well in a noncooperative context or at a low SNR (SNR \(\leq-3\) dB) and require a huge database to be optimized. To overcome these issues, we developed a blind strategy based on the Recurrence Quantification Analysis(RQA) of the received signal [20]. RQA is a nonlinear data analysis technique applied to various fields to study the dynamics of complex systems. It is particularly useful for analyzing time series data that exhibit nonlinear, nonstationary, or chaotic behavior. It is widely introduced in the financial sector, particularly in the analysis of cryptocurrencies like Bitcoin [21]. In the field of cardiology, RQA has been also employed to understand the nonlinear dynamics of heart rate variability after acutely induced myocardial ischemia by percutaneous transluminal coronary angioplasty [22]. RQA is a versatile tool that has been applied to a wide range of applications, from financial markets and cardiology to atmospheric science, urban management, and neurocognitive research. Its ability to reveal important properties of time series data, such as determinism, laminarity, stability, randomness, regularity, and complexity, makes it a valuable technique for analyzing the dynamics of complex systems across various domains [21; 22; 23; 24; 25]. In this paper, we propose to use RQA in digital communications, because it is able to reveal some intrinsic features of digital communication signals, such as hidden periodicities, stationarity features, or linearity properties. Indeed, due to modulation standards, transmitted signals may contain hidden periodicities. Using this fact, we use Recurrence Quantification Analysis (RQA) tools to detect if the bandwidth allocated to the PU is available or not. The main RQA tool used to quantify the recurrence level is the Recurrence Rate (RR), which is considered the probability of having recurring states in a signal. In a recent work, we proposed an RR-based Detector (RRD) [20]. However, the RRD is very sensitive to SNRs and depends on the choice of a recurrence threshold. To overcome the RRD's limits, in this paper, we propose an efficient algorithm called the Recurrence-Analysis-based Detector (RAD). The RAD exploits the similitude of distances among various states of the signal in a multidimensional space. This similitude of distances is evaluated by a square symmetrical matrix named the distance matrix.