# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 1 Introduction  
Artificial intelligence (AI) aided in multiple-input output (MIMO) in becoming a stepping stone toward the development of sixth-generation (6G) wireless communication networks. With a large number of transmitting and receiving antennas, MIMO technology enables the B5G technology to meet user demands of high data rates [1]. However, with multiple transmitting and receiving antennas, signal detection becomes a very complicated task at the receiver end. To overcome detection problems in MIMO technology, several research studies have been carried out based on both conventional and AI-driven MIMO detection techniques [2]. The conventional MIMO detectors, including minimum mean square error (MMSE) and zero-forcing (ZF), have a lower computational complexity but struggle to maintain performance [1]. To overcome performance issues in MMSE andZF, the maximum-likelihood detector (MLD) offers the best performance; however, it has an extremely high computational complexity [3]. Although linear detection techniques offer reduced computational complexity compared to optimal detection schemes, they still require matrix inversion operations, which become computationally intensive and impractical in systems with a large number of antennas, a hallmark of massive MIMO (maximum) deployments [2]. To further enhance convergence speed and detection accuracy, more advanced iterative solvers such as the Gauss-Seidel (GS) [4] and Conjugate Gradient (CG) [5] algorithms have been investigated for MIMO signal detection. These methods exploit the structure of the Gram matrix to achieve more efficient iterative updates and are generally better suited for large-scale systems. Despite these improvements, GS and CG methods still fall short of achieving optimal detection performance, particularly in scenarios involving high-order modulation, strong inter-antenna interference, or ill-conditioned channel matrices [6; 7]. Thus, while these linear and iterative detection approaches offer promising avenues for complexity reduction, their suboptimal performance in practical MIMO environments underscores the need for alternative strategies, particularly those leveraging data-driven and AI-assisted techniques that can adapt to the dynamic nature of next-generation wireless systems. Now, to find a balance between complexity and detection performance, researchers are trying to integrate AI, more specifically machine learning (ML) and deep learning (DL), into next-generation wireless communication networks [1]. DL is playing a vital part in developing promising solutions for next-generation wireless network problems, including signal detection [3]. Among all DL applications for wireless communication systems, signal detection is one of the primary topics. A recent study shows that by increasing channel awareness and allowing adaptive feedback mechanisms, the research study shown in [8] demonstrates how AI-driven techniques can improve overall system performance. Over the past decade, numerous research studies have been carried out that use DL techniques to overcome the signal detection issue in MIMO systems [3; 9; 10; 11; 12; 13; 14; 15]. Despite demonstrating decent performance, these DL-aided signal detection techniques still have a considerable gap between detection accuracy and computational complexity, which demands further investigation. Based on this motivation, this research study presents a DL-driven signal detection method known as the MIMONet detector for MIMO systems. The newly developed model employs a feed-forward neural network (FFNN) model to improve detection performance and lower computational complexity. The proposed MIMONet is an extension of our previous work [3] that utilized a deep neural network (DNN) model for signal detection in the MIMO system. The primary contribution of this paper is highlighted as follows:  
* We developed a DL-driven signal detection method known as the MIMONet detector for signal detection in a MIMO system and explored the study under diverse channel conditions.