A Study Of Low Power Lo Ra Decoding Method Based On Node Resampling

With the development of Internet of things,wireless communication technology has gradually become the focus of research in industry and academia.The emergence of LPWAN provides a new solution for the long-distance transmission of the Internet of things.Among them,LoRa(Long Range,LoRa)Network has attracted much attention due to its Network protocol and unauthorized use of frequency band.The LoRa node mainly relies on low duty cycle and low transmission power to achieve the advantage of low power consumption.However,the research results show that in order to meet the requirements of communication and improve the duty cycle,to ensure the transmission rate and increase the transmission power,the need to implement special network protocols such as time synchronization LoRa is accompanied by rising energy consumption of the nodes,even carefully adjusted LoRa network's various parameters,node of the life cycle is the highest for three years or so,it's unable live ten years.In order to reduce the energy consumption and prolong the life cycle of LoRa nodes,the D-S LoRa(Downsample LoRa)decoding system is proposed in this paper,which can decode LoRa packets under the condition of greatly reducing the sampling rate of LoRa nodes,so as to effectively reduce the energy consumption of LoRa nodes.The innovative research content of this paper mainly includes:(1)Aiming at the problem of low energy consumption utilization rate when decoding LoRa nodes,a low energy consumption LoRa decoding system based on node downsampling(D-S LoRa)is proposed and designed.A method combining sliding window and correlation analysis is designed to obtain the time frequency domain characteristics of the LoRa signal and accurately identify the detection of the start position of the LoRa packet during downsampling.Based on the frequency domain characteristics,the bp neural network is used to classify the resampled signals to achieve the decoding of the resampled LoRa data packets.Due to the resampling strategy,effective signal detection and decoding methods,the D-S LoRa system can tolerate a lower signal-to-noise ratio,which can greatly improve the node transmission success rate and reduce node energy consumption.(2)Aiming at the problem of increased energy consumption of the resampling LoRa decoding system in a dynamic signal-to-noise ratio environment,we design energy estimation model and a dynamic parameter adjustment model with node sampling rate adjusted with the environmental signal-to-noise ratio.The designed energy estimation model can support the A,B,and C access modes in the LoRa WAN protocol.According to the energy estimation model,the D-S LoRa system can reduce the node energy consumption by about 80% in the A access mode,and the B access mode node energy consumption can be reduced by about 75%,and the class C access mode can reduce the node energy consumption by about 98%.The proposed dynamic parameter adjustment model can adaptively adjust the sampling rate of the node,and improve the energy utilization of the D-S LoRa system by nearly 60% by selecting the optimal downsampling multiple.