Abstract

Researcher proposes presented a deep learning-based channel estimation scheme for cooperative MIMO systems in 5G networks. The proposed method displayed an extremely high degree of accuracy, with a mean average MSE of 0.00165, showing the strong correlation between estimated and actual channel values. By presenting an average Bit Error Rate (BER) of 0.0052, the low figures emphasise a convincing error-correcting ability against many real-world challenges, portraying a reliable and functional system for transmitting information. It is shown to be computationally feasible, employing an average of 2.2 million FLOPS, and thus demonstrating the applicability of the scheme for a variety of realistic deployment scenarios. This work lays the foundation for communication systems in the 5G era and proves to be a potential method for deep learning based cooperative MIMO channel estimation.