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

Yuchao Chen; Haoyue Tang; Jintao Wang; Jian Song

doi:10.3390/e23010091

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

Entropy (Basel). 2021 Jan 10;23(1):91. doi: 10.3390/e23010091.

Authors

Yuchao Chen^{1

2}, Haoyue Tang^{1

2}, Jintao Wang^{1

2

3}, Jian Song^{1

2

3}

Affiliations

¹ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
² Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China.
³ Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China.

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.

Keywords: age of information; constrained Markov decision process; cross-layer design.

Abstract

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