## 1 Introduction  
Two-thirds of the Earth's surface corresponds to the sea, demanding connectivity in the new paradigm of the Fourth Industrial Revolution, namely, to interconnect Internet of Things (IoT) devices. Underwater acoustic (UWA) communications is the solution to solve this gap. However, due to the high level of multipath [1], impulsive noise, and low carrier frequencies, the bit rates available in UWA communications are low [2]. This limitation can be mitigated by employing techniques such as block transmission techniques, such as orthogonal frequency division multiplexing (OFDM) or single carrier with frequency-domain equalization (SC-FDE), as well as multiple input-multiple output (MIMO) systems, as adopted for the Fifth Generation of Cellular Communications (5G) [3]. MIMO systems bring added value in terms of capacity and diversity gains. However, such gains are conditioned on the existence of spatially uncorrelated channels. Due to the lower carrier frequencies employed in UWA scenarios, such condition is rarely fulfilled [4]. Since a certain level of channel correlation between signals of different antennas exists in real scenarios, such configuration is here considered. The complexity of the UWA scenarios is defined by their non-homogeneity, being dependent on the following factors: pressure, temperature, and salinity. The propagation speed of the sound is affected by each of these factors [5]. Several expressions have been developed empirically to calculate sound propagation speed in UWA scenarios. [PERSON] defined in [5] the following expression widely utilized:  
\[c=1449+4.6T_{\text{C}}+0.055T_{\text{C}}{}^{2}+0.003T_{\text{C}}{}^{3}+(1.39- 0.012T_{\text{C}})(S-35)+0.17d_{P} \tag{1}\]  
where \(c\) stands for the propagation speed, \(T_{\text{C}}\) the temperature in degrees Celsius, \(S\) the salinity in parts per thousands, and \(d_{P}\) the depth in meters. Although there is complexity of propagation of sound in underwater scenarios, it is known that the increase of the number of antenna elements that comprise MIMO systems corresponds to a technique widely used to increase the capacity gains [6; 7; 8]. However, this demands higher signal processing levels associated with linear receivers, such as the zero forcing (ZF). Therefore, a solution to simplify the complexity relies on the use of sub-optimal receivers, such as equal gain combining (EGC), maximum ratio combining (MRC) [6; 7], and iterative block--decision feedback equalization (IB-DFE), being studied in this article. Low-density parity-check (LDPC) has been adopted by 3 GPP as an encoding scheme for the 5G standard [9; 10]. Before, LDPC codes were adopted for the long-term evolution (LTE), as well as for the World Interoperability for Microwave Access (WiMAX) standard. Due to its outstanding performance, LDPC codes have been utilized in other scenarios, such as in UWA communications. Impulsive noise is typically experienced in low frequencies, consisting of short-duration noisy pulses (from a few microseconds up to milliseconds) [11; 12; 13]. This is caused by sea life activity in the underwater environment, which can be substantially above the additive white Gaussian noise (AWGN). Previous works considered using the block transmission technique OFDM, in UWA communications, such as [14]. The work published in [11] focuses on impulsive noise superimposed on OFDM signals, including mitigation techniques. Furthermore, previous works have also considered the combination of OFDM, and MIMO systems applied to UWA communications [15; 16]. Moreover, as compared to [17], the research published in this article considers LDPC codes and impulsive noise, which is typical of UWA communications. Moreover, channel estimation, using training sequences, is also adopted to make the results of this complex system more realistic.