## 1 Introduction  
The field of advanced wireless MIMO communication technology is rapidly developing and has attracted significant research interest. Current MIMO technology has greatly improved in terms of reliability, effectively decoding ultra-high transmission bit rates with minimal performance loss and decoding heavy information based on high-order QAM constellations with high BER performance [1, 2, 3]. These systems' performance and features are achieved thanks to mechanisms that enable the creation of spatial-multiplexing and uncorrelated MIMO channels with high diversity order, high spatial selectivity, and diversity gain. Other significant advantages of modern wireless MIMO systems include their effectiveness in dealing with various interferences on the wireless MIMO channel, such as Rayleigh deep-fading [4, 5, 6], multi-path effects [3, 7], and scattering phenomena [8, 9], the fast scale of MIMO fading [10], and dealing with multiple interference phenomena and jamming attacks [11] with several methods of interference cancellation [2, 10, 12, 13]. These capabilities were possible thanks to the intelligent utilization of the spatial domain [14]. The requirements for the application of advanced engineering in wireless MIMO networks, such as Dynamic and Shared Spectrum Access (DSA) technology [15], adaptive equalizers, and modern estimation techniques, provide stability in the decoding process and an accurate estimation of high-order constellations under bad propagation conditions, and make up the basic construction of traditional networks [1] (e.g., 5 th Generation New Radio (5G-NR) Heterogeneous Network (HetNet) [15; 16], 6 th-mobile-generation [17], and Wi-Fi-6 802.11.ax). These requirements, in addition to achieving Ultra-Reliable Low-Latency (URLL) in the decoding process, including the capability of digital Successive Interference Cancellation (SIC) and the optimization of the spectrum access, are also needed, especially in Unmanned Aerial Vehicle (UAV) communications [10], point-to-point Stand-Alone (SA) wireless MIMO network systems [10], communication between autonomous vehicles [18], tactical MIMO communication systems in the military field, communication medical equipment, and many more. Strengthening the cognitive intelligence of the wireless MIMO communication system's backbone is a critical contribution to developing modern wireless MIMO systems [19; 20; 21]. One of the key features of intelligent mechanisms is the use of parallel spatial decoding algorithms that are based on real-time hard decisions [22; 23]. Spatial decoding algorithms that include features, such as high reliability, robustness, and flexibility, are creating conditions that allow a paradigm in which we are freed from constraints as much as possible (e.g., without the need for physical feedback between the receiver and the transmitter, enabling us to share the CSIT [7; 10; 24]). Our research is focused on developing a spatial decoding algorithm and studying destructive phenomena in the wireless channel. The wireless channel plays a crucial role in the transmission process, and it is important for researchers to develop effective methods for quickly and accurately processing MIMO wireless channel matrices, which represent the \"propagation characteristics\". These methods and studies are essential for the development of advanced communication systems. A good communication channel enables optimal throughput, spectral efficiency, and network capacity and minimizes power consumption. In advanced MIMO wireless communication systems, adjusting the channel parameters to support next-generation technologies can result in favorable propagation characteristics, especially in higher frequency bands [25]. Current approaches such as massive MIMO, Coordinated Multipoint Operation (CoMP), Millimeter Wave, and Industrial Internet of Things (IIoT) rely on higher frequency bands and contribute to advancements beyond 5G-NR or 6 th-mobile-generation requirements, also known as next-generation technologies [26].