Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

Abstract

Exchanging time-critical information is prevalent in various industrial applications where low latency and timely delivery are paramount. Through this work, we consider a cognitive radio network comprised of multiple secondary users with time-sensitive traffic, and they can access the licensed channel under the hybrid interweave/underlay scheme to enhance spectrum utilization. Traffic in the secondary system is divided into two distinct categories: deadline-constrained data and status updates. Quality of service of data with expiration time, such as multimedia streams, is assessed through the timely throughput metric. However, the age of information metric is used to characterize the freshness of the status update packets, which is vital in several emerging applications. Within an interference constraint imposed by the primary user, a dynamic scheduling policy is proposed to optimize the weighted sum of the average age of information of the status update users under a strict timely throughput requirement for each user with deadline-constrained traffic. We formulate the optimization problem as a constrained Markov decision process. Then, through the drift-plus-penalty method, the problem is reduced into a series of unconstrained Markov decision problems. Finally, each subproblem is tackled using the backward dynamic programming technique. Simulation results illustrate the effect of the main system parameters, such as the PU transmitted power and transmission rate level, on the performance of the secondary system. Moreover, the model feasibility regarding the fulfillment of the constraints against PU activity is experimentally investigated under the proposed hybrid mode and classical interweave mode. The performance of the proposed policy is compared to two other low-complexity scheduling schemes, which ensure the satisfaction of the constraints; results show the performance superiority of our proposed policy.