## 6 Conclusions  
This paper has presented a novel DNN-based signal detection model that attempts to address the unique problems with MIMO systems, such as inter-antenna interference and high computing demands. This study investigated the use of a DNN-based detection model for MIMO communication systems with limited resources. The DNN model's performance was measured according to its SER and computational complexity and compared to traditional methods like MLD, MMSE, and ZF. The results showed that, while the DNN model has potential for MIMO signal detection, it did not outperform the traditional methods. This underscores the important balance between improving performance and maintaining computational efficiency for practical use in wireless communication systems with limited resources.  
This work demonstrates the great potential of DNN-based signal detection for MIMO systems, outperforming traditional methods in terms of efficiency and accuracy. This innovative approach has the potential to revolutionize wireless communication technologies and open up new research directions. The developed DNN model raises the bar for performance in intricate MIMO situations by overcoming the drawbacks of traditional methods.  
The study's conclusions also highlight the need for research into the computational difficulties involved in DNN-based MIMO detection. There are several directions to pursue more research. Examining AI-based methods to improve DNN compute efficiency--possibly via hardware acceleration or architectural optimization--is one interesting avenue to pursue. It may also be helpful to investigate alternate activation functions created especially for low-power wireless communication systems. Moreover, performance and efficiency improvements may be obtained by modifying current DNN architectures or creating new ones that are specifically designed to meet the demands of MIMO signal processing task.  
Future research will consider a hybrid strategy that integrates other AI-based methods and traditional signal-processing methods in a synergistic way. A hybrid system like this could be able to get around each method's drawbacks and improve wireless network performance significantly by combining the best features of both. By outlining the benefits and drawbacks of employing AI-based signal detection methods, this work paves the way for additional research into the adaptability and limitations of AI-based signal detection methods in MIMO framework. A useful framework for future research on digital signal processing and the development of wireless communication infrastructure is provided via the study's conclusions and recommendations. In this field, bridging the theoretical promise with real-world application is still a major issue, but further research into DNNs and other developing technologies holds great promise for improvements in the future.  
Figure 16: Hidden layers vs. number of neurons per layer.  
**Author Contributions:** Conceptualization, M.U.H.; methodology, M.U.H. and K.S.; software, H.S. and A.A.K.; validation, H.S., A.A.K. and M.Y.D.; formal Analysis, H.S. and A.A.K.; investigation, H.S. and A.A.K.; resources, M.Y.D.; data curation, M.Y.D.; writing--original draft preparation, H.S. and A.A.K.; writing--review and editing, M.U.H. and K.S.; visualization, M.U.H. and K.S.; supervision, M.U.H. and K.S.; project administration, M.U.H. and K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.