Wireless Channel Optimization of Internet of Things

Abstract

Research on the optimization of Internet of Things wireless channel has received widespread attention. First, this paper analyzed the Internet of Things wireless channel, including multichannel analysis, Doppler effect analysis, MIMO channel model analysis, and channel correlation characteristics simulation. On this basis, a three-path channel neural network curve approximation model was established, and the CDF and PDF curves of Rayleigh and Rice fading channels were approximated and analyzed. The results showed that the neural network model basically conformed to the reality. According to the 3GPP2 standard, the time domain delay model of the FIR filter is optimized, and the design model has the characteristics of fast calculation speed and high precision. In addition, this paper also used the neural network to approximate the amplitude-frequency characteristics of the 64-order fading shaping filter and the 120-order interpolation filter. The simulation results met the requirements. Finally, based on the neural network MIMO channel model, a neural network model of additive white Gaussian noise channel under certain conditions was established. Based on the simulation data, the polarization antenna was optimized for path loss and different tilt angles, and the optimization could be performed at high speed while achieving the minimum error.