Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb AOA-COA-MIMO-OFDM) is proposed in this manuscript. In OFDM, there is a non-stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA-COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN-Hyb AOA-COA-MIMO-OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO-OFDM system using deep neural network and MN-based improved AMO model (DNN-IAMO-MIMO-OFDM), MIMO-OFDM systems using the deep learning and optimization (RBFNN-PSO-MIMO-OFDM), and MIMO-OFDM systems using hybrid neural network (HNN-CSI-MIMO-OFDM) respectively. Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb-AOA-COA-MIMO-OFDM) is proposed in this manuscript. During channel estimation in OFDM, there is a non-stationary channel physical appearance, problems arise to overcome this issue, and DiWGAN is used. Therefore in this work, hybrid Archimedes optimization algorithm and chimp optimization algorithm (Hyb AOA-COA) are employed to enhance the DiWGAN weight parameters. The proposed is compared with existing methods, like DNN-IAMO-MIMO-OFDM, MIMO OFDM RBFNN-PSO-MIMO-OFDM, and HNN-CSI-MIMO-OFDM, respectively. image