###### Abstract
Here, the authors have looked into the possibilities of utilising the approximate message passing (AMP) method, LR-ZF, and LR-SIC, as well as LR-ZF to enhance the symbol error rate (SER) efficacy for the mMIMO network while reducing complexity. In mMIMO, network complexity remains a significant dilemma. For building low-complexity HPs, artificial neural network [31; 32], deep learning [33; 34]-reliant strategies have been presented and thoroughly examined. An upsurge of complexity within HP is a serious dilemma as it severely impacts the implementation cost for mMIMO systems, and this is because of the complexity brought on by the matrix inversion procedure. To make the precoders less complex, certain efforts have been made. Regarding this matter, there have been proposals as well as thorough studies of truncated polynomial expansion (TPE) [35] as well as Neumann series (NS) [36] precodings. With a few iterations, the NS precoder is advantageous. However, when there are many iterations, they display a comparable level of complexity that is on par with that of ZF. Diverse precoding strategies, notably the Jacobi iteration-reliant [37] precoder, Gauss-Seidel (GS) [38] precoding, successive over-relaxation (SOR) [39] precoding, symmetric SOR (SSOR) [40] precoding, weighted SSOR (WSSOR) [41] precoding, and modified SOR (MSOR) [42] precoding have also been put forward and assessed in order to further reduce computational complexity. These techniques can deliver close to ideal ZF performance by successfully iteratively converting the inversion of a matrix onto an equation of linear form. However, the efficacy of the system can be increased to an even greater extent by employing a more advanced HP architecture as well as a mechanism for grouping users. The aforementioned motivated the authors to streamline the CRZF precoder using the SSOR approach in order to improve the functioning associated with the mmWave-mMIMONOMA-SWIPT mechanism. An SSOR-CRZF precoder that is sub-connected (SC) has been proposed by the authors of this work. The MRT, ZF, RZF, Kalman, SSOR, and CRZF precoders are compared to the recommended SSOR-CRZF precoder in this work. Computational complexity, SE, and EE have all been assessed in relation to the performances. The presented SSOR-SRZF precoder outperforms with less computational complexity than the MRT, ZF, RZF, Kalman, and SSOR precoders. This work's primary contribution may be summarized succinctly as follows:  
* We examine the optimisation of both HP and PS techniques to enrich SWIPT-enabled mmWave-mMIMO-NOMA systems. Based on the channel correlation, a modified version of the K-Means (MKM) user grouping algorithm was used. Here, we examine the proposed HP and optimization of the PA and PS factors to improve the sum rate of the proposed system. * We have constructed a hybrid/joint mmWave MIMO-NOMA precoding technique/scheme. Depending on the user groupings, a scheme for analog precoding (AP) has been designed to guarantee that every beam receives equivalent channel gain at its maximum. We introduce the SSOR-CRZF digital precoder (DP) for eradicating inter-user interference. We define the problem as a combined optimization of power distribution and PS variables in order to make the overall power as well as the minimal rate limits for every UE more straightforward. * In comparison to traditional precoders, the presented SSOR-CRZF precoder is evaluated in this study for its ability to improve both SS as well as EE within the mmWave-mMIMO-NOMA-SWIPT network while having less complexity. In terms of SE, EE, and computational complexity, its performance is contrasted with that of MRT, ZF, RZF, Kalman, SSOR, and CRZF-based precoders. It is shown that the recommended strategy outperforms the system significantly more than the current state-of-the-art algorithms. * The performance of the proposed system is evaluated with respect to variations in the signal-to-noise ratio, number of users, number of BS antennas, number of phase quantization bits, fading parameters, and under imperfect CSI conditions.