Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks.

Wireless networks need channel estimation (CE) to function well. Deep learning (DL) has improved 5G and future-generation network communication reliability and computational complexity. Due to its lack of statistical channel information, the least squares (LS) estimation approach has significant estimate mistakes despite its cost-effectiveness and widespread usage. The MIMO-OFDM-5G-CS-AGAN architecture improves CE for 5G and beyond networks. The suggested CE architecture uses DL to outperform LS. The suggested architecture has been specifically developed using a multipath channel profile and a MIMO system, to effectively address situations characterized by mobility-induced Doppler effects in 5G and future networks. Because of its adaptability and scalability, the suggested architecture can accommodate almost unlimited transceiver antennas. Conditional Self-Attention Generative Adversarial Networks (CS-AGAN) are at the heart of the proposed method because they reliably provide high-quality CE at low error rates. Python is used for the implementation of the suggested approach. In this case, we have the performance metrics symbol error rate (SER), bit error rate (BER), uplink sum rate, downlink sum rate, Normalized CE Error, and mean square error. When compared to current methods like MIMO-OFDM-5G-Bi-LSTM, MIMO-OFDM-5G-DNN, and MIMO-OFDM-5G-CGAN, the suggested approach achieves 13.4%, 13.02%, 12.21% lower BER and 11.23%, 6.3%, 9.08% lower SER. [ABSTRACT FROM AUTHOR]