A ZigBee network channel interference type identification method based on BP neural network

Abstract

ZigBee network communication in the LAN is susceptible to interference from WiFi, Bluetooth, adjacent network obstacles, and other factors. ZigBee network itself cannot identify the type of interference, resulting in network reliability decline, and in severe cases, it will cause network paralysis. In response to this problem, this paper proposes a ZigBee network channel interference type identification method based on BP neural network, which accurately identifies the interference type by constructing neural networks in the ZigBee chip. If the decision output is WIFI or adjacent network interference on the same frequency, the ZigBee network communication channel is configured through the MAC layer of the ZigBee protocol stack to avoid interference. If the decision output is obstacle interference, the physical network can be reconstructed to avoid obstacles. Through simulation verification, the ZigBee network channel interference type identification method can significantly improve ZigBee network communication quality and anti-interference performance, which has application value.