In this paper, we explore the problem of minimizing the uplink and downlink age of information (AoI) in non-orthogonal multiple access (NOMA)-based low Earth orbit (LEO) networks for Internet of Things (loT) devices. The objective is to effectively manage the freshness of information across loT devices while maintaining a fair power allocation over time. We propose two novel AoI models that accurately capture the freshness of information for devices during both uplink and downlink transmissions, accounting for factors such as propagation delay, satellite handover delay, and inter-satellite link transmission time. To minimize the time-average AoI, we propose an energy-aware AoI (EA-AoI) algorithm that combines a deep reinforcement learning (DRL)-based scheduling approach with a low-complexity power allocation scheme. Simulation results demonstrate that the proposed EA-AoI scheduling algorithm outperforms successive interference cancellation (SIC)-based scheduling, AoI-greedy scheduling, and random scheduling approaches in terms of lower AoI values in the uplink and downlink directions through both simulated and real-world satellite trajectories. Furthermore, the algorithm ensures that the long-term average allocated power remains within a predefined threshold, thus striking an optimal balance between AoI and power consumption for NOMA-based LEO satellite networks.
Energy-aware Age of Information (AoI) Minimization for Internet of Things in NOMA-based LEO Satellite Networks
2024-06-24
1301974 byte
Conference paper
Electronic Resource
English