## 1 Introduction  
This research investigates data detection techniques for future communication systems beyond 5G, focusing on overcoming challenges associated with increased data rates, spectral efficiency, and reliability. The growing complexity of 5G necessitates the use of more antennas in terms of multiple inputs, multiple outputs (MIMO), which introduces new detection hurdles such as higher computational demands, inter-antenna interference, and the need for accurate channel information. The classical detection methods like maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF) exhibit limitations in MIMO systems due to their increased complexity with a growing number of antennas [1, 2]. This performance gap between SISO and MIMO systems motivates further exploration of deep neural network (DNN) architectures for MIMO data detection. Therefore, this study proposes a DNN-based MIMO detector to address these limitations and achieve reliable, high-quality wireless networks with large antenna arrays. The primary objective is to develop a MIMO detector leveraging a deep learning model for superior signal detection and error minimization compared to conventional approaches such as MLD, MMSE, and ZF. The proposed DNN detector is hypothesized to effectively handle the complexity of large-scale antenna systems, as in MIMO, and deliver much better performance in simulations, as well as real-life implementations. The proposed DNN-based detector's efficacy is carefully assessed and compared with the traditional techniques (MLD, MMSE, and ZF). This covers performance measures including computing time, the signal-to-noise ratio (SNR), and the symbol error rate (SER). The analysis presented in this work is based on the design and implementation of a DNN-based MIMO detector to enhance signal identification and achieve an SER reduction in MIMO systems. This work also explores the trade-off between computational efficiency and detection accuracy, considering the feasibility of deploying DNN-based detectors in beyond-5G systems. The performance of the DNN detector is evaluated and compared with traditional methods (MLD, MMSE, and ZF) to assess its efficacy relative to these established techniques. In addition to the MIMO systems, this work employs DNN to provide a method of signal detection in SISO systems as a base case. This DNN-based model, in contrast to conventional approaches, is engineered to efficiently manage the complexities of MIMO systems, offering improved computational efficiency and SER performance. This work not only assesses the DNN model's efficacy but also looks into ways to increase system reliability and signal detection accuracy going forward. To optimize the performance of DNN-based models, suitable training algorithms and techniques, such as stochastic gradient descent, have been implemented. A comprehensive analysis and interpretation of the simulation results have also been presented, highlighting the implications for improved detection accuracy, enhanced system reliability, and potential gains in spectral efficiency. This investigation aims to provide valuable insights and recommendations for future research directions to further refine the effectiveness and efficiency of not only the DNN-based data detection methods but also other machine learning methods. The comparative analysis presented in this work offers a valuable understanding of the strengths and limitations of DNN-based approaches, paving the way for future advancements in 6G and beyond. The study strives to provide insightful perspectives and contribute to the development of communication systems beyond the 5G era by acknowledging and addressing these limitations.