In this article, we analyze the performance of the long-term evolution random access procedure with the Third Generation Partnership Project's access class barring (ACB) mechanism in an energy harvesting (EH) machine-to-machine (M2M) scenario. To circumvent the state-space explosion in the conventional Markov-chain-based analysis due to time-dependent traffic pattern and data and energy buffer status, we develop an analytical model that combines mean-value analysis with the Markov-based analysis. Based on the analytical model, the random access success probability, the access delay of the network, and the average time duration between two successive successful transmissions are derived. Our analysis suggests that in the EH scenario, despite the lower number of the contending nodes in comparison with the non-EH scenario, the ACB parameters must be chosen in a more conservative way to avoid excessive collisions. The ACB parameters include access barring rate and mean barring duration. We also study an energy threshold-based activation policy and investigate the joint effects of this policy and the ACB mechanism on the random access success probability. The extensive simulations were conducted to evaluate the accuracy of the analytical model.

Energy harvesting (EH), Internet of Things (IoT), long-term evolution (LTE), machine-to-machine (M2M), network performance analysis
IEEE Internet of Things Journal
Department of Systems and Computer Engineering

Khoshabi Nobar, S. (Sina), Ahmed, M.H. (Mohamed Hossam), Morgan, Y. (Yasser), & Mahmoud, S.A. (2020). Performance Analysis of LTE Random Access Protocol with an Energy Harvesting M2M Scenario. IEEE Internet of Things Journal, 7(2), 893–905. doi:10.1109/JIOT.2019.2946295