Performance analysis of energy harvesting–enabled lora networks under hardware impirments with diversity and learning techniques

Abstract

This paper investigates the performance of energy harvesting (EH)–enabled long range (LoRa) networks with diversity techniques under the impact of hardware impairments (HI). In particular, we analyze the coverage probability (Pcov) of a network where LoRa end devices (EDs) rely solely on harvested energy supplied by power beacons (PBs). Both the gateway and PBs are equipped with multiple antennas and employ maximal ratio combining (MRC) and maximal ratio transmission (MRT) techniques, respectively, to enhance system performance. Due to the complexity of the considered network, conventional mathematical analysis becomes intractable. To overcome this challenge, we leverage machine learning and deep learning approaches including support vector machines (SVMs), random forest (RF), gradient boosting (GB), and neural networks (NNs) with different normalization strategies to estimate the coverage probability of the system. Extensive simulation results show that neural networks provide the most accurate performance predictions, followed by SVMs and RF, while GB exhibits the weakest performance. Nonetheless, the performance gaps among these models remain moderate to minor, indicating that all are suitable for most Internet of Things (IoT) applications. The accompanying parameter sensitivity analysis highlights the critical roles of the path-loss exponent and PB transmit power, offering valuable design and optimization insights for EHenabled LoRa networks under practical hardware constraints.