Minimizing Age of Information in Downlink Wireless Networks With Time-Varying Channels and Peak Power Constraint

The rapid development of real-time applications such as the Internet of Things (IoT) puts forward high requirements for the data freshness of wireless networks. The metric Age of Information (AoI) is adopted to measure the data freshness from the user side (Kaul et al., 2012). In this paper, we investigate the AoI-minimization problem of wireless networks with time-varying channels and the peak power constrained base station (BS). Firstly we model the time-varying channels as a multi-state ergodic Markov chain and formulate the multi-user scheduling problem. Next, we relax the original strict power constraint and formulate the relaxed problem as a constrained Markov decision process (CMDP). To reduce the computational complexity, we exploit the threshold structure of the optimal policy for the CMDP and search for the optimal policy via Linear programming (LP). To satisfy the original strict power constraint, we propose an truncated multi-user scheduling policy, which is proven asymptotic optimal. Finally, we simulate our proposed scheduling policy and compare the AoI performance with other policies. Simulation results verify the theoretical analysis and indicate that our policy can significantly reduce the AoI of users with bad channel quality, thus outperforming other policies.