Wireless ad hoc network performance prediction method based on improved BP neural network

Abstract

The invention discloses a wireless ad hoc network performance prediction method based on an improved BP neural network regression algorithm. The method simultaneously relates to the field of wirelesscommunication networks and machine learning. A traditional BP neural network regression algorithm is improved, three network performance indexes, namely throughput, time delay and packet loss rate, ofthe wireless ad hoc network in the time-varying environment are predicted respectively through the improved algorithm, and the convergence rate of network parameters is effectively increased on the premise that the prediction performance of an original algorithm is guaranteed. According to the method, an empirical data set is constructed by combining an actual task scene and three MAC protocols (CSMA / CA, DTDMA and ESTDMA), and each piece of data can represent one task scene; and the traditional BP neural network is improved, so that the convergence rate of network parameters is improved. Thebasic idea of the method is that features are extracted by analyzing actual task information to construct an empirical data set; an amplification function is introduced into a BP neural network parameter offset calculation formula to improve the parameter convergence rate, and an improved algorithm is used to learn an empirical data set to obtain a learning model; and calling the learning model topredict the network performance for the new task.

Description

technical field [0001] The invention belongs to the field of wireless communication networks, in particular to a wireless ad hoc network performance prediction method based on an improved BP neural network. Background technique [0002] This method involves the fields of wireless communication network and machine learning at the same time. It mainly uses the BP neural network regression algorithm to predict the current network performance, including throughput, delay, and packet loss rate, based on the networking situation among the participating nodes in the battlefield. In addition, it also improves the traditional BP neural network, and realizes the improvement of the convergence speed in the BP network training process. [0003] In a complex battlefield environment, in order to realize real-time data sharing between participating nodes, it is necessary to build an ad hoc network for information transmission. In a wireless ad hoc network, the MAC protocol is mainly respon...

AI Technical Summary

Problems solved by technology

[0011] (2) Latency

[0013] (3) Packet loss rate

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