## 2 Literature Review  
Several schemes have recently been developed to efficiently detect the BD signals in BC systems. For instance, [PERSON] et al. [30] implemented practical BDs and proposed an energy detector that uses a differential coding scheme to decode BD signals, which is a step towards achieving BC systems. [PERSON] et al. [31] created an improved energy detection method that requires perfect channel state information (CSI) knowledge. To address this issue, [PERSON] et al. [32] designed a semi-coherent detection method that needs only a few pilots and unknown data symbols, substantially reducing signaling overhead and decoding complexity. To avoid channel estimation, [PERSON] et al. [33] applied a differential encoding scheme to BD signals and proposed a minimum BER detector with an optimal detection threshold. [PERSON] et al. in [34] conducted fundamental studies of BER performance for non-coherent detectors on top of [33]. However, these methods either explicitly require channel estimation or lead to unsatisfactory detection performance. As a solution, ML-based methods have been developed recently to directly recover BD signals using only a few training pilots, without explicitly estimating relevant channel parameters. For example, [PERSON] et al. [35] proposed a clustering method to extract constellation symbol features, and developed two constellation learning-based signal detection methods that obtain better BER performance. Nevertheless, these schemes were designed for systems where the signal from the RF source is a constellation-modulated signal. [PERSON] et al. [36] changed the task of BD signal detection to a classification problem, and developed the support vector machine (SVM)-based energy detection scheme to enhance the BER performance. However, this ML technique requires a large number of training samples, and there is a significant gap between the proposed scheme and the optimal one. On the other hand, DL techniques, which utilize a neural network to explore features optimally in a data-driven manner, have demonstrated better performance in various areas [37; 38; 39; 40; 41; 42]. However, wireless communication channels in real-world scenarios undergo significant fluctuations over time, and the range of these changes can be very large. Additionally, wireless transmission is often intermittent and can have a variable transmission pattern. The authors of [29] proposed a method based on deep transfer learning (DTL), which utilizes a DNN to extract time-varying features using a small amount of online training data. They showed that their proposed method achieves a BER performance that is quite similar to that of the optimal detection method when perfect CSI is available. Today, in much of the research, meta-heuristic algorithms are used for learning deep learning architectures [17; 18; 43; 44; 45; 46]. [PERSON] et al. proposed an intelligent identification approach using variational mode decomposition (VMD), composite multi-scale dispersion entropy (CMDE), and a particle swarm optimization deep belief network (PSO-DBN)--namely, VMD-CMDE-PSO-DBN--for bearing faults. Since the accuracy of identifying faults cannot be maximized by manually setting the parameters of DBN nodes, PSO is utilized to optimize the parameters of the DBN model. Based on the experimental comparison and analysis, the VMD-CMDE-PSO-DBN approach has practical application in intelligent fault diagnosis [45]. Training DL architectures is considered to be one of the most difficult problems in ML. Gradient-based methods have significant drawbacks, such as being stuck in local minima in multi-objective cost functions, requiring expensive execution times, and relying on continuous cost functions. Training DLs is an NP-hard problem, and optimizing their parameters using meta-heuristics has become increasingly popular. In recent decades, many meta-heuristic algorithms have been introduced as potential solutions to various engineering problems.