基于改进Q学习算法的低压电力线通信组网及维护方法

In order to improve the stability of the network, it is crucial to select a suitable low-voltage power line communication(LVPLC) topology control method. To solve the existing problems of low network stability caused by the relatively delayed topological response to dynamic changes in the existing networking method without self-learning ability, this paper proposes an improved Q learning algorithm for LVPLC local area network with multiple constraints. Based on the bind carrier sense multiple access (CSMA) protocol, the asymmetric channel network system is modeled as a discrete Markov decision process. Network stability can be improved through the continuous interaction with the unknown environment, the association with registered node information, the generation of routing tables as well as the periodical online training for optimizing the cluster tree rooted at the gateway. But network stability can be improved through rotating the stations periodically, maintaining and updating the logical topology of the backbone cluster tree, as well as extending the network life cycle. Simulation results verify its effectiveness and generalization ability.