## 1 Introduction
Our research aligns with the trends and goals of deploying these advanced technologies, particularly in urban environments, indoor applications, large and dense cities, and areas with many end users. This aims to significantly improve real-time connectivity and drastically increase information rates. The communication bottleneck depends on the wireless medium [27]. All these areas that we mentioned are characterized by Rayleigh deep-fading phenomena, which damage the channel significantly [28]. Therefore, it is crucial to integrate spatial SIC decoding algorithms at the front end of the receiver component of advanced base stations, Access Points (APs), relays, and advanced modems. For example, in deploying small-cell HetNet in 5G-NR networks, these components play a critical role in increasing the number of users and the channel's capacity in a given area. However, the presence of Rayleigh deep-fading phenomena between the relay and the end users may cause the scheduler to lower the order of the transmitted constellations. Thus, integrating spatial SIC decoding algorithms such as AP-HSIC or LM-HSIC in the Radio Intelligence Controlling (RIC) of the small-cell HetNet is significant. These algorithms can counteract destructive phenomena and determine high-order constellation orders. Few spatial SIC decoding algorithms constitute exemplary contributions, such as the possibility of an extension approach in the modulation order and the generality of time-frequency-space scenario solutions via the decoding core mechanisms. These approaches apply to all modern communication systems' high-quality operation. The meaning of these concepts pertains to creating a situation where the algorithmic complexity overheads and the computational complexity overheads of the decoding algorithms are preserved low, even though the order of the QAM constellation increases significantly. The existence of the convergence capability of the decoding algorithm and maintaining the generalization principle must be taken into account, even though the order of the constellation increases significantly [1; 22; 29]. For example, a decoding algorithm developed for 16-QAM must be able to decode constellations of significantly higher orders of QAM while maintaining properties of generalization, fast convergence, and reasonable computational complexity [1; 5; 22; 29]. In addition, the utilization of any number of multiple antenna arrays in the transmitter as well as in the receiver side without any computational limitation, such as power domain multiplexing management, for example, is essential for advanced MIMO schemes such as massive-MIMO systems [30]. Another example of the essential contribution of a few advanced spatial SIC decoding algorithms is to allow, in the development process of the algorithms, the approach of the accurate decoding of any value of constellation order or any arbitrary mapping constellations of bits to the M-QAM symbol, especially in a high order of QAM [5], as we mentioned, but under challenging interference scenarios. In addition, the intelligent development of advanced spatial SIC decoding algorithms allows integration with any version of COD scheme approaches such as OSTBC [7] or Orthogonal Space-Frequency Block Codes (OSFBC) [7]. As we mentioned, we focus on the ability to overcome different types of interference and deal with a diversity of interference phenomena, such as wireless destruction channels, small/large scale fading, deep-fading, and interference jamming [31; 32; 33; 34; 35; 36], with Channel-State Information Receivers (CSIRs) only.