Deep Learning Based Channel Estimation

Deep learning is revolutionizing channel estimation in wireless communication systems, aiming to improve accuracy and efficiency compared to traditional methods. Current research focuses on adapting deep learning architectures, such as convolutional and recurrent neural networks, and extreme learning machines, to various challenging scenarios including massive MIMO, distributed MIMO with limited quantization, and integrated sensing and communication systems with intelligent reflecting surfaces. These advancements address limitations of pilot-based methods by leveraging the inherent structure and correlations within channel data, leading to improved spectral efficiency and robustness in diverse wireless environments. The resulting improvements in channel estimation accuracy have significant implications for enhancing the performance and reliability of next-generation wireless networks.