Machine learning (ML) theories and methods are mainly based on probability theory and statistics. It is a very powerful tool for data modelling. On the other hand, energy harvesting has been regarded as a viable solution to extending battery lifetime of wireless sensor network. Motivated by these, modelling of the radio frequency (RF) energy available to the wireless nodes is required for efficient operation of wireless networks. In this work, we will use different ML algorithms to model the RF energy data for efficient operation of energy harvesting communication systems. Four ML algorithms are studied and compared in terms of the accuracy for RF energy modelling using the energy data in the band between 1805 and 1880 MHz. The results show that linear regression (LR) has the highest accuracy and the most stable performance, while decision tree is the worst model. Also, in terms of the operation efficiency of the system, LR has the best performance, followed by support vector machine and random forest algorithm.

中文翻译：

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使用机器学习对能量收集通信系统进行RF能量建模

机器学习（ML）的理论和方法主要基于概率论和统计学。它是用于数据建模的非常强大的工具。另一方面，能量收集已被视为延长无线传感器网络电池寿命的可行解决方案。由于这些原因，为了使无线网络有效运行，需要对可用于无线节点的射频（RF）能量进行建模。在这项工作中，我们将使用不同的ML算法为RF能量数据建模，以使能量收集通信系统高效运行。研究了四种ML算法，并使用1805和1880 MHz之间的能量数据在RF能量建模的准确性方面进行了比较。结果表明，线性回归（LR）具有最高的准确性和最稳定的性能，而决策树是最差的模型。此外，就系统的运行效率而言，LR具有最佳性能，其次是支持向量机和随机森林算法。