Advancing MIMO adaptation with transfer learning: pioneering approaches and emerging perspectives

Transfer learning has emerged as a promising approach to address the challenges of adapting multiple-input multiple-output (MIMO) systems across diverse scenarios, environments, and frequency bands. By leveraging knowledge from pre-trained models, transfer learning aims to enhance the performance of MIMO systems in new settings, thereby reducing the need for extensive labeled data and training time. Despite the growing interest in this field, limited efforts have been made in the existing literature to present a comprehensive analysis and identify challenges associated with the application of transfer learning for MIMO system adaptation. Addressing this gap is essential for advancing the adoption and further development of these techniques by the research community. This review article provides an in-depth analysis of transfer learning techniques applied to MIMO system adaptation, encompassing various aspects such as model architectures, transfer strategies, and practical implementations. By synthesizing the latest advancements, offering insights into their strengths and limitations, and identifying emerging trends, this review aims to serve as a valuable resource for researchers, engineers, and practitioners interested in leveraging transfer learning to enhance the adaptability and performance of MIMO systems in real-world scenarios.