Robust MIMO-OFDM Channel Estimation for 5G Networks Using Conditional Self-Attention GANs

Due to the rapid increase in Mobile networks data traffic, there is a need to improve on channel estimation methods for Multiple Input Multiple Output (MIMO) system that combined with the Orthogonal Frequency Division Multiplexing (OFDM) relevant to 5G and future generations. The papers introduced in this research present a novel method that is based on the use of Conditional Self-Attention Generative Adversarial Networks (CSAGANs) to improve the channel estimation. CSAGANs combine the generative adversarial network (GAN) that has the function of generating high quality synthetic image with long-range dependencies and self-attention mechanisms that enable Identify the dependencies of MIMO-OFDM channels. The conditioned aspect maintains that the estimated sources satisfy realistic conditions in the channels, which enhances the solutions' accuracy and robustness. Experimental outcomes also prove that in terms of MSE and BER, proposed methodology exhibits better performance as compared to conventional techniques like LS and MMSE. This means that the proposed method provides higher efficiency in cases where there are mobile nodes and constantly changing SNR as in the case of 5G technology. Moreover, it is revealed that the CSAGAN framework can effectively decrease computational costs which is why it can be applicable for real-time tasks. This work opens up avenues for developing more robust and accurate channel estimation schemes, which are required for the evolution of the forth coming wireless communication systems.