Wavelet Neural Network-Enhanced Meteorological Data Recovery for Airport Energy Optimization

Wireless transmission technology is poised to become the standard for data exchange in airport meteorological detection equipment. Yet, the intricate electromagnetic environment prevalent at airports can substantially diminish the precision of wireless transmission. Consequently, the capacity for noise reduction in wireless signals is pivotal to the viability of wireless transmission technologies. This study employs experimental simulations to elucidate the data transmission process within MIMO-OFDM systems, examining the influence of data noise errors on channel capacity. Additionally, a wavelet neural network is utilized for data recovery, thereby substantiating the practicality of neural networks in the context of MIMO-OFDM system restoration. The findings from our experiments indicate that noise significantly affects the channel capacity of wireless communication networks. Furthermore, the application of neural networks is demonstrated to be an efficacious method for data recovery within MIMO-OFDM systems.