## 1 Introduction  
There has been an unprecedented surge in the number of wireless communication systems in recent years owing to the increasing demand for efficiently managing the extensive data traffic originating from numerous users and devices [1]. In response to this demand, massive MIMO technology has emerged as a promising solution for fifth-generation (5G) wireless communication [2]. The essence of massive MIMO lies in deploying a substantial array of antennas, often numbering in the hundreds, at the base station (BS) to serve several single-antenna user terminals simultaneously. In contrast to traditional MIMO, massive MIMO offers a multitude of advantages, such as enhancements in capacity, reliability, throughput, and increased spectral efficiency [3, 4]. These compelling advantages are achieved by leveraging spatial diversity and spatial multiplexing techniques [5] within a multi-user MIMO system. Therefore, the widespread deployment of massive MIMO is anticipated to persist as a pivotal aspect of future wireless technologies, including beyond 5G (B5G) and sixth-generation (6G) networks [6; 7]. Its inherent advantages, leveraging multiple antennas at base stations for concurrent user service, align with the growing demands of diverse applications in the Internet of Things (IoT), Virtual Reality (VR), Augmented Reality (AR), autonomous systems, and high-definition multimedia streaming. In essence, the adaptability and potential of massive MIMO make it a crucial technology, which is expected to meet the growing demand for enhanced data throughput, ubiquitous connectivity, and reliable performance across evolving wireless communication standards. Despite the manifold benefits, the presence of hundreds of antennas presents several challenges. One such challenge involves uplink signal detection, a critical element within modern digital frameworks responsible for extracting or estimating transmitted data from observations vulnerable to noise and channel fading [8]. In the context of massive MIMO communication systems, the presence of multiple antennas leads to signal interference during transmission, significantly complicating the task of estimating the desired signal amidst the interference. Consequently, at the receiver end, constructing a detector suitable for massive MIMO configurations becomes imperative. This detector must ensure good performance while maintaining low complexity to effectively manage the challenges posed by the high number of antennas in the system. Channel estimation is also essential for optimizing detection and mitigating interference [9]. Theoretically, optimal or suboptimal nonlinear detection techniques like maximum likelihood (ML) [10] and sphere decoding [11] exhibit a high computational burden that increases exponentially as the number of antennas rises. In scenarios involving high-order modulation and several hundred antennas, this complexity escalates to levels deemed unacceptably high. Therefore, several detectors have emerged for signal detection in massive MIMO systems, including linear detectors like minimum mean square error (MMSE), which is a relatively computationally efficient solution. Massive MIMO has been shown to achieve good performance, particularly under favorable propagation conditions and a large loading ratio (\(N/K\)) [12], where \(K\) represents the number of users and \(N\) is the number of receive antennas. Nonetheless, the computational complexity associated with the MMSE linear detector remains notably high. This is primarily due to the computation involved in matrix inversion, particularly when dealing with high-dimensional channel matrices in massive MIMO. Direct inverse solvers, such as zero-forcing and linear MMSE solvers, can provide efficient and exact solutions for small matrix sizes. However, in the context of massive MIMO systems, even matrix sizes that appear small in comparison to other large systems can present significant computational challenges, especially as the number of antennas and users grows [12].