探索性数据分析在LoRa通信SNR性能的研究

Research on exploratory data analysis for SNR performance in LoRa communication

为了降低数据传输信噪比,提高传输质量,在实际部署过程中需合理配置LoRa物理层参数。为此,基于探索性数据分析,提出一种优化配置参数选择的预测模型。首先,设置物理层参数配置,收集数据;其次,利用探索性数据分析剔除数据中的异常值,并计算各配置参数和信噪比的相关性;最后,通过ID3决策树算法构建预测模型,建立配置参数和SNR之间的关系,以此提高传输信噪比,改善数据传输质量。实验结果表明,预测模型可以有效地对LoRa通信技术中信噪比性能进行优化。

LoRa physical layer parameters need to be properly configured during the actual deployment to reduce the signal⁃to⁃noise ratio(SNR)in data transmission and improve the data transmission quality.Therefore,a prediction model for optimal configuration parameter selection is proposed based on exploratory data analysis(EDA).The physical layer parameter configuration is set to collect data,and then the outliers are removed from the data by means of the EDA and the correlation between configuration parameters and SNR is calculated.The prediction model is constructed by means of the ID3 decision tree algorithm,and the relationship between configuration parameter sums and SNR is established,so as to improve the transmission SNR and the data transmission quality.The experimental results show that the prediction model can be used to effectively optimize the SNR performance in the LoRa communication technology.