Simulation of Large-Scale 5G Communication Optimization Model Based on Fuzzy Neural Network Optimization Algorithm

With the rapid development of Internet of Things (IoT) technology, the demand for machine communication (MTC) is increasing. The traditional cellular mobile communication system designed and built for human communication (HTC) will not meet the communication needs of MTC. At the same time, due to the huge number of users and the expanded network scale, the energy consumption of wireless communication network system is on the rise. The environmental pollution caused by greenhouse gas emissions generated by wireless communication systems and the accompanying huge energy consumption have attracted wide attention of operators and the whole society. This article briefly introduces the development of 5G wireless communication systems, comprehensively summarizes the spectrum allocation of millimeter waves, channel propagation characteristics, channel measurement methods, channel detectors, channel parameter estimation algorithms, channel modeling methods, and standardized channel models. A large number of multi band and multi scene millimeter wave channel measurements have been conducted. Experiments were conducted using UCI datasets and engineering examples, and compared with traditional cloud models, fuzzy neural networks, and BP neural networks to analyze the operational efficiency, accuracy, and number of rules of the algorithm. In addition, computer simulation further verifies that the proposed method can achieve better performance compared to several existing codebook design methods.