Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State

Abstract

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.

Whittle Index Based Age-of-Information Aware Scheduling for Markovian Channels

“…As discussed above, there is a rich body of work that uses the AoI metric for various applications [2][3][4][5][6][7][8], [10][11][12][13][14][15][16][17], [22][23][24][25][26][27][28][29]. Refer to [30] for a comprehensive survey of results in this area.…”

“…The scheduling problem is modeled as an MDP. In [24], the authors consider the setting where a base-station receives time-sensitive data from multiple sensors via errorprone Markovian channels. The data freshness is modeled as a monotonically increasing function of AoI.…”

“…The goal is to design a scheduling policy to maximize data freshness in the presence of bandwidth and power constraints. Unlike [24], we consider AoI as the metric of interest and do not have any power constraints. In [25], the authors study a multi-source multi-destination system where requests for the data generated by the sources comes from the destination.…”