Exploring Deep Learning for Improved MIMO Performance: A Design Research Methodology Approach

Utilizing a design methodology approach, deep learning is harnessed to enhance Multiple-Input Multiple-Output (MIMO) system performance. Leveraging artificial neural networks, deep learning effectively addresses MIMO signal complexities, yielding optimized solutions and identifying future research directions. Notably, deep learning aids in predicting channel traits, refining MIMO systems, and end-to-end communication design. Recent focus includes channel estimation, beamforming, and MIMO detection. Distinguishing design thinking from design methodology, the former grasps user needs, while the latter offers a structured, systematic issue-solving avenue. Through this approach, deficiencies are recognized, leading to superior MIMO solutions via conceptual understanding, fit analysis, and apt design methodology. The study's aim is to enhance MIMO performance effectively.