Energy-Aware Adaptive IRSA with Optimal Two-Degree Distributions for LoRaWAN IoT Networks

Abstract

Irregular Repetition Slotted ALOHA (IRSA) has emerged as a promising random access scheme for massive IoT deployments, achieving near-channel-capacity throughput via successive interference cancellation (SIC). However, existing IRSA schemes employ a uniform degree distribution across all devices, disregarding heterogeneous energy constraints inherent to battery-powered LoRaWAN networks. In this paper, we propose Energy-Aware Adaptive IRSA (EA-IRSA), a scheme where each device selects its repetition degree based on its residual energy level while maintaining the network-aggregate distribution close to the analytical optimum. Leveraging the observation that two-consecutive-degree distributions $(d, d+1)$ offer near-optimal throughput with a single tunable parameter, we design a per-class adaptation rule with four energy classes that preserves the aggregate throughput threshold $G^{*}$ while reducing the energy expenditure of depleted devices. Density evolution analysis confirms that the EA-IRSA aggregate distribution preserves the throughput threshold of the nominal optimum. Monte Carlo simulations over 5,000 frames with N = 500 heterogeneous devices demonstrate that EA-IRSA extends the network lifetime by up to 18% compared to standard IRSA, while maintaining comparable cumulative throughput.