Packet Drop Rate Minimization with AoI Constraint in an Energy-Harvesting Cognitive Radio Network

Abstract

Integrating cognitive radio technology with energy harvesting capabilities to the Internet of Things networks offers a promising avenue for tackling spectrum scarcity and battery limitation issues. This study explores a single-channel cognitive radio network including two secondary users who generate two distinct traffic types: packets with deadlines and status update information. Packets are dropped only when failing to reach their destination within the deadline. Leveraging the drift-plus-penalty approach, we develop a scheduling policy to minimize the average drop rate of these packets subject to a data freshness constraint. Simulation results depict how variations in main system parameters, such as the primary user’s transmission power and energy arrival rate, impact the average drop rate. Additionally, experimental simulations confirm that our proposed policy outperforms the performance of a suggested baseline policy with a notable gap.