## 1 Introduction
### Related Works  
There has been a lot of study into many different aspects of SC, such as the categorization of modulators [24; 25; 26], error-control encoders [27; 28], multi-carrier transmissions [11; 12; 13], and space-time block coding (STBC) [29; 30; 31; 32; 33] broadcasts. Since STBC categorization is the primary emphasis of this research, the accompanying discussion provides a context on recent advances in this field. In single-carrier broadcast scenarios, the STBC signals Alamouti (AL) and spatial multiplexing (SM) across Nakagami channels were recognized using a collection of techniques that depend on higher-order statistics [29]. These techniques utilized a receiver equipped with a single antenna and depended on the fourth-order moment as the distinguishing characteristic. Signals from two antennas may be analyzed for their cyclostationarity, which allowed for discrimination between several STBC signals under conditions of varied transmission defects [30; 31]. It was attainable to distinguish between AL and SM STBC waveforms by making use of the dispersion attributes of multipath wireless channels [32]. It was shown that AL generated peaks at a certain set of time delays in the cross-correlation function of two separate intercepted signals, but SM does not. The maximum likelihood notion and the false alarm rate criterion formed the basis for two suggested identification algorithms. Assuming perfect synchronization on the receiving end, three maximum likelihood procedures, the optimum identifier, the second-order statistic recognizer, and the code parameter estimator were developed for blind identification of STBC signals [34]. It was demonstrated in [35] that with ideal timing alignment and under appropriate conditions of a full rank channel and a number of receive antennas greater than or equal to the number of sent antennas, the Frobenius norms of these statistics possessed non-null qualities whose locations only depend on the STBC used at the sender end. This was utilized to categorize five STBC signals. Intelligent signal categorization of STBC was reported in [36], utilizing a convolutional neural network (CNN) with multi-delay features fusing approach. Two fusion approaches were presented to integrate the characteristics of various time delays, and a residual block was utilized to provide more discriminating characteristics. The challenge of STBC identification for orthogonal frequency division multiplexing (OFDM) systems operating over frequency-selective channels was introduced in [37; 38]. A binary hypothesis test for decision-making was designed by taking advantage of the space-time redundancy. Space frequency block code (SFBC) signals are distinguished from one another by analyzing the time-domain correlation functions of signals acquired by a pair of antennas [33]. The reported method consisted of two distinct phases. In the first stage, the cross-correlation function of pairs of signals received from various antennas was estimated, and in the second step, a false-alarm dependent assessment was used for effective decision-making. The challenge of STBC recognition and channel estimation for multi-user asynchronous uplink transmissions in single carrier frequency division multiple access (SC-FDMA) systems was investigated in [39]. Mathematical investigation proved that the space alternating generalized expectation maximization method could be used to provide an iterative solution to the maximum-likelihood problem of STBC identification, channel estimation, and synchronization. An iterative expectation maximization approach was proposed in [40] to create a maximum likelihood classifier that could differentiate between SFBC-OFDM signals utilizing the soft outputs of a channel decoder over unknown wireless channels. Modulation types and STBC formats were simultaneously identified in [41] over undetermined multipath links while waiving the need to have more antenna elements at the recipient than at the sender.