###### Abstract  
The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system's capability to adaptively determine a decision threshold for energy detection. s 20231241831622023124183  
aims to address various user needs in different application scenarios [2; 8]. Even though there are multiple facets to consider, such as spectrum sharing and management, spectrum-aware networking, sensing scheduling, joint software, and hardware design [9; 10], to equip the radio with cognitive abilities, it can be claimed that sensing the spectrum with the purpose of identifying the opportunities, e.g., available time-frequency slots, in the spectrum space [11] is the initiator of the CR's capabilities. Thus, being an essential component of CR, spectrum sensing has attracted the interest of the research community. It appears that this interest will persist. Based on the findings of [12], it has been observed that while 5G networks generally outperform 4G networks in terms of service quality and speed, their performance has diminished over time due to the increased number of users accessing 5G networks without a corresponding increase in network capacity. The primary limiting factor in addressing this capacity constraint is the scarcity of available EM spectrum resources. Considering the envisioned goals of 6G [13; 14], which strive to establish ubiquitous connectivity across diverse vertical application domains, it can be inferred that the ongoing significance of efficient EM spectrum utilization persists. Consequently, the research topic of spectrum sensing retains its importance, as it directly addresses the need to effectively detect and allocate available spectrum resources in support of 6G's objectives. Spectrum sensing research is often classified into two primary categories: wideband sensing and narrowband sensing. Wideband sensing can be further characterized by two distinct methodologies: Nyquist-based and sub-Nyquist-based methods. Similarly, narrowband sensing is investigated under two discrete classifications: coherent methods and non-coherent methods [15; 16]. Nyquist-based wideband sensing methods often suffer challenges due to their reliance on high sampling rates, necessitating specialized and expensive equipment. In contrast, sub-Nyquist wideband sensing methods alleviate the aforementioned requirements. However, a substantial portion of these methods rely on assumptions of sparsity and the estimation of sparsity levels, thereby introducing high computational complexity [17]. An increase in computational complexity is frequently associated with increased energy consumption, which is particularly undesirable for radios operating in resource-constrained environments, such as mobile phones or military radios that are deployed in field conditions.