The Analysis of Node Planning and Control Logic Optimization of 5G Wireless Networks under Deep Mapping Learning Algorithms

In order to meet the needs of blowout growth of data flow and 10100 times increase of user experience rate, the next generation mobile communication(5G) heterogeneous network deployment will use ultra-dense network. Accurate fault location is the basis of slice management. In virtualized network model, the function of network elements is software, the underlying layer is highly abstract, the structure changes dynamically, and common cause faults occur frequently, which brings difficulties to fault location. Deep Learning is a machine Learning method that USES Deep Neural Network (DNN) with multiple hidden layers to complete Learning tasks. The essence is that by building a neural network model with multiple hidden layers and using a large amount of training data to learn more useful features, the accuracy of model prediction or classification can be improved. Based on the theory of deep mapping learning algorithm, a scheduling algorithm based on event-driven access point, improved time-driven access point and idle network is designed for centralized wireless network control system. Aiming at the centralized wireless network control system, this paper designs a scheduling algorithm based on event-driven access point, improved time-driven access point and network idleness. The designed sensor network node platform uses the programmable logic controller as the main control chip, contains the data interface module that realizes the communication function between the node and the upper computer, and the acquisition and conditioning module responsible for the collection and conditioning and conversion of the analog signal. At the same time, the TCP and UDP are compared and studied. The accuracy of the model is verified, and the interference distribution of the model and the influence of the D2D inter-pair distance on the system performance are simulated. The distance between the D2D pairs under the given simulation parameters has a certain influence on the system performance. As the distance increases, the system performance will deteriorate, but the impact is not a big conclusion.