###### Abstract
In the context of energy detection, establishing a threshold that ensures a consistent false alarm rate and missed detection rate requires either having explicit information about the noise variance and SNR, or estimating these parameters from the available data. Extensive research has focused on the development of adaptive threshold determination methods, driven by the recognition that the noise floor commonly exhibits non-stationary behavior. The objective of these studies is to devise techniques that can dynamically adjust the threshold to accommodate the temporal fluctuations in the noise floor, enabling more robust and accurate detection performance. For example, [PERSON]. [25] introduces an adaptive thresholding method by leveraging the binarization concept used in image processing. The proposed approach represents the threshold as a linear function of the mean and standard deviation of the received signal samples. [PERSON]. [26] proposes a heuristic method modeled as a linear function of the signal-to-interference-plus-noise-ratio. They highlight the simplicity and practicality of this approach for engineering applications. The authors also highlight that although the proposed method necessitates complex offline optimization, it offers straightforward online threshold control. [PERSON]. [27] addresses the problem of minimizing the error decision probability by formulating it as a function of the primary user's spectrum utilization ratio (ranging from 0 to 1) and the threshold. The authors demonstrate that when the spectrum utilization ratio is fixed, the error decision probability function exhibits convex behavior, allowing for the derivation of solutions. A novel three-event energy detection algorithm is presented in [28]. The authors employ [PERSON]'s method with forced convergence in a single iteration to accurately approximate the optimal decision threshold, effectively minimizing the error decision probability. In [29], the authors present a novel approach centered on deep reinforcement learning. They employ a custom-designed reward function within the deep Q-network algorithm to intelligently adjust the energy detection threshold. Furthermore, they seamlessly integrate this method with a clustered cooperative spectrum sensing architecture, harnessing the collective power of these advanced techniques for enhanced spectrum management. In [30], the authors present a method for adaptive threshold determination, employing noise power estimates obtained at each sensing interval. The noise power estimation relies on the spectral minima tracking (SMT) technique. Furthermore, the authors perform a correlation analysis to assess the relationship between the parameters of the SMT technique and the resulting noise power estimates. Leveraging the insights from this analysis, they fine-tune the parameters of the SMT method to enhance the accuracy of their noise power estimations. In their study, Ref. [31] enhance the fixed double-threshold using a conventional mean energy detection algorithm with a novel approach by determining the intermediate threshold adaptively. They formulate the intermediate threshold as a weighted combination of high- and low-value thresholds and employ a decision error probability metric to optimize its value. With the primary objective of enhancing the utilization of the limited electromagnetic spectrum in real-world applications, this study introduces a novel multi-stage DL approach to spectrum sensing. It employs energy detection with adaptive thresholding, utilizing multiple stages of DL techniques applied to the time domain representation of the narrow-band channel readings to estimate the threshold. The key contributions of the proposed approach are:  
* The introduction of a multi-stage DL approach that effectively mitigates channel impairments and jointly conducts spectrum sensing while maintaining superior performance, resulting in low false alarm (3.85%) and missed detection (3.06%) rates.