## 3 Literature Review  
This section provides a comprehensive review of data detection techniques employed in wireless communication systems. The focus is on both conventional methods and the emerging field of machine-learning-based approaches. Several well-established data detection techniques, including MLD, MMSE, and ZF, are explored. These techniques offer a solid foundation for signal detection but may face limitations in complex communication scenarios. In the previous work, the authors proposed a method for improving the performance of ZF and MMSE MIMO detectors using a heuristic 1-Opt local search algorithm. This approach achieved better BER performance compared to both the original ZF/MMSE detectors and complex non-linear methods like V-BLAST and Sphere Decoder while maintaining significantly lower computational complexity [6]. Reference [7] presents a comparative analysis of pilot-based channel estimation schemes for massive MIMO systems, evaluating them based on normalized mean square error (NMSE), SNR, number of antennas, and computational complexity. The work done in [8] investigated the impact of varying numbers of transmitting and receiving antennas on channel capacity using ZF and MMSE precoding techniques for MIMO downlink transmissions. The study revealed that these precoding methods have distinct effects on capacity, with the optimal choice depending on the specific antenna configuration. The findings are further substantiated through simulation results that demonstrate the influence of antenna combinations on channel capacity [7]. The role of MIMO systems in future communication networks is a key theme addressed in [9]. As communication migrates to higher frequency bands, increased path losses due to atmospheric effects become a significant challenge. This paper explores the application of MIMO systems to improve communication quality through channel modeling. The simulation results illustrate the effectiveness of MIMO systems in enhancing user signal quality. A comprehensive categorization of MIMO detection algorithms is presented in [10]. This work aims to provide wireless communication specialists with insights into various detection algorithms and their suitability for diverse applications. The focus lies on optimal and near-optimal detection concepts specifically designed for massive MIMO systems. The impact of activation functions in DNNs on performance is investigated in [11]. This research offers valuable insights into optimizing the effectiveness of the proposed Deep-Learning-Based Multiple-Antenna Detection (DM-DETECT) algorithm for Massive MIMO (Ma-MIMO) technology. The analysis focuses on the behavior of activation functions across different SNR ranges. Building upon the concept of leveraging deep learning for MIMO detection, ref. [12] proposed AIDETECT, a lightweight, AI-based detection system. This work delved into the optimal number of hidden layers in the DNN architecture for various SNR conditions. The findings offer valuable guidance on maximizing the performance of lightweight AI models for MIMO and Ma-MIMO systems. The research not only shed light on the promise of deep learning techniques in addressing signal detection challenges but also paved the way for further exploration in this domain. For clarity and completeness, a brief overview of the transmitter-receiver chain in a wireless communication system is included in [13]. This highlights the critical role of data detection methods, both traditional and DNN-based, in ensuring reliable and performant wireless communication. This research builds upon the foundation established by the reviewed literature on data detection techniques for wireless communication systems. While previous works (e.g., [7, 8]) have explored conventional methods like MMSE, MLD, and ZF and investigated their performance under various conditions, the focus has primarily been on optimization within these established frameworks. This study, however, ventures beyond these limitations by introducing a DNN-based detector specifically designed for MIMO systems.