## 1 Introduction
### Related Works  
Researchers have recently shown a lot of enthusiasm for the challenge of identifying modulation in MIMO broadcasts. Related efforts have been accomplished for both single-carrier and multi-carrier systems. A decision theoretic algorithm for use in spatially multiplexed MIMO systems is offered for determining the number of transmit antennas and identifying modulation simultaneously [22]. Based on the correlation functions of the received signals, a binary-tree algorithm is developed to identify the type of modulation used by MIMO systems operating over unexplored wireless multipath fading channels [23]. The properties of the fourth-order cumulants of the predicted broadcast signal streams and a likelihood assessment are employed to implement a modulation identification algorithm for MIMO systems [24]. Combining the qualities of the correlation functions and cyclic cumulant results in the development of a modulation identifier used for MIMO systems over frequency-selective channels [25]. A modulation identifier is designed using Gibbs sampling and mean field variational inference for MIMO-OFDM systems operating on wireless channels with unknown characteristics [14]. A semi-supervised deep learning technique for MIMO modulation identification was developed in [26] by framing multi-class classification as an anomaly inference issue with unlabeled data. A three-dimensional convolutional neural network with cuboidal filtering was proposed in [27] for modulation identification in MIMO and orthogonal frequency division multiplexing (OFDM) transmissions. A tree-based blind modulation classification algorithm for asynchronous MIMO-OFDM systems was created in [28] by computing higher-order cumulants of the collected signal in the frequency domain. Three distribution tests were used in [29] to compare the possible modulation options, and their results were combined by a multi-layer classifier to boost the eventual classification accuracy. The authors of [30] proposed a modulation identifier for MIMO systems using a convolutional neural network with zero-forcing (ZF) equalization and investigated the effect of incomplete channel state information on identification accuracy.  
In the meanwhile, a large body of literature has been devoted to STBC categorization for both single-carrier and multicarrier MIMO transmissions. A set of techniques dependent on the fourth-order moment was created in [31; 32] to discriminate between Alamouti (AL) and spatial multiplexing (SM) STBC transmissions across Nakagami single-path wireless channels. The utilization of the second-order cyclostationarity of two distinct received signals was assessed in [33; 34] in order to categorize among various STBC signals. The authors of [35] employed the scattering capabilities of frequency-selective wireless channels in order to differentiate between AL and SM STBC transmissions. A maximum-likelihood (ML) strategy [36] and a Frobenius norm approach [37] were evolved to make distinctions between STBC broadcasts. The researchers of [38; 39; 40; 41] utilized the cross-correlation that exists between multiple collected signals in order to identify AL and SM broadcasts within the context of OFDM systems. Training symbols are adopted to categorize amongst STBCs for OFDM systems [42].