The rapid evolution of wireless communication systems has led to the increasing deployment of Massive Multiple-Input Multiple-Output (Massive MIMO) technology to meet the growing demand for higher data rates, improved spectral efficiency, and enhanced system capacity. Massive MIMO systems employ a large number of antennas at the base station to simultaneously serve multiple users, thereby significantly improving network throughput. However, obtaining accurate channel state information (CSI) remains a major challenge due to the high overhead associated with pilot signaling and feedback mechanisms. This paper investigates the use of deep learning techniques for channel-to-channel mapping in Massive MIMO systems. The proposed approach aims to predict downlink channel information directly from uplink channels, thereby reducing the need for extensive channel training and feedback. A deep neural network model is trained using datasets generated through the DeepMIMO framework based on accurate ray-tracing simulations. The model learns the spatial and frequency relationships between antenna channels and predicts channel responses efficiently. Performance evaluation using achievable rate and spectral efficiency metrics demonstrates that the proposed deep learning-based channel mapping approach can closely approach the performance of systems with perfect channel knowledge while significantly reducing training overhead. The results highlight the potential of artificial intelligence in improving the scalability and efficiency of future wireless communication systems, particularly in 5G and emerging 6G networks.
Vanrammawia Vanrammawia (Wed,) studied this question.