Comprehensive Investigation of Channel Estimation Techniques in mmWave MIMO Systems: Unlocking the Potential of Future Connectivity

Millimeter wave (mmWave) MIMO systems are critical for next-generation wireless communication, yet accurate channel estimation remains a key challenge due to high pilot overhead and dynamic channel conditions. This paper explores advanced channel estimation techniques, focusing on deep learning-based methods such as denoising autoencoders and supervised learning approaches. These techniques leverage spatial and temporal correlations to reduce pilot overhead while improving estimation accuracy. The study synthesizes existing literature on deep learning applications for channel estimation and highlights key trends, methodologies, and challenges. Furthermore, this review identifies research gaps, such as computational complexity in real-time applications and limitations in handling high-speed channel variations. By critically analyzing recent advancements and their limitations, this work provides insights into future research directions for enhancing mmWave MIMO channel estimation using hybrid deep learning models.