Improving LoRa Network Capacity Using Multiple Spreading Factor Configurations

LoRa networks enable long range communications for Internet of Things (IoT) applications. The current LoRa technology provides a wide range of communication settings whereas many combination settings are orthogonal and, thus, they can be successfully decoded at the gateway when the signals are transmitted simultaneously. Previous simulation results showed that the LoRa network capacity can be improved when multiple communication parameters are applied. In this paper, we model a LoRa network consisting of nodes with different communication settings in terms of bandwidth and spreading factor. We compute the average success probability per configuration as a function of density taking into account both intra and inter-spreading factor collisions. We, also, formulate and solve an optimization problem to maximize the node capacity for a given deployment area and frequency by optimizing the number of nodes having different spreading factor configurations. We present numerical results and we show that solutions close to the optimal can increase the maximum number of nodes by more than 700% compared to case where equal number of users per spreading factor are considered.