Research And Application Of Broadband Micro-power Routing Algorithm

The user electricity information acquisition system in the current mostly adopts lowvoltage power communication,communication signals are transmitted through power lines.This communication mode has many disadvantages,such as limited by transmission medium and wiring,low network rate,signal being easily affected by power frequency noise and unable to pass through the transformer,and so on.As a kind of wireless broadband communication mode,wideband micro-power avoids the disadvantages brought by power line communication and has been applied to electricity information system.However,the network layer management node of the current wideband micropower network protocol stack has low efficiency and poor flexibility,which can not cope with the information network with the increasing volume of traffic and data recently.In this thesis,the network structure and routing algorithm are optimized based on the realization of broadband micro-power network.Firstly,an optimization scheme of broadband micropower network structure is proposed,which adopts a hybrid network structure of tree and mesh.Tree network is used to centralize node management,improve service efficiency,control transmission power of child nodes,improve channel multiplexing ability,mesh network is used to optimize path planning in the process of multi-hop transmission,reduce the time delay in the process of message transmission to improve service success rate.In the existing dense band micro-power network,each node needs to send network maintenance frames periodically to maintain the stability and reliability of the network.However,in the region with dense nodes,network maintenance occupies a large amount of channel resources,which lowers the upper limit of network performance.However,simply reducing the transmission frequency of network maintenance will lead to a sharp decline in network quality.Therefore,this thesis proposes a distributed reinforcement learning routing algorithm applied to broadband micro-power networks.Through the iterative exploration of the transmission path and the data merging of the service message,the quantity of network maintenance is greatly reduced and the upper limit of network performance is exerted while ensuring the stability of network quality.Finally,the hybrid network model and reinforcement learning routing algorithm are implemented and compared on a broadband micro-power experimental platform in this passage.It is verified that the hybrid network has better service performance under the condition of large attenuation,and the reinforcement learning routing algorithm increases the upper limit of network capacity service.