Analytical Markov model for slotted ALOHA with opportunistic RF energy harvesting

In this paper, we investigate the performance of an ALOHA random access wireless network consisting of nodes with and without RF energy harvesting capability. We develop and analyze a Markov model for the system when nodes with RF energy harvesting capability are infinitely backlogged. Our results indicate that the network throughput is improved when the conventional nodes are underloaded. On the contrary, when all types of nodes have finite backlogs, we numerically demonstrate that the network throughput and delay are improved when the overall system is overloaded. We show that there exists a trade-off between energy efficiency and delay, and we determine the optimal number of energy harvesting nodes in a network maximizing the energy efficiency while satisfying a given delay requirement. © 2015 IEEE.