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

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

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 (A...

Full description

Bibliographic Details
Main Authors: Yuchao Chen, Haoyue Tang, Jintao Wang, Jian Song
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Entropy
Subjects:
age of information
cross-layer design
constrained Markov decision process
Online Access:https://www.mdpi.com/1099-4300/23/1/91
id doaj-dbfa523bc91444fe8fce876fe09d7f75
record_format Article
spelling doaj-dbfa523bc91444fe8fce876fe09d7f752021-01-11T00:02:00ZengMDPI AGEntropy1099-43002021-01-0123919110.3390/e23010091Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel StateYuchao Chen0Haoyue Tang1Jintao Wang2Jian Song3Department of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaIn 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.https://www.mdpi.com/1099-4300/23/1/91age of informationcross-layer designconstrained Markov decision process
collection DOAJ
language English
format Article
sources DOAJ
author Yuchao Chen
Haoyue Tang
Jintao Wang
Jian Song
spellingShingle Yuchao Chen
Haoyue Tang
Jintao Wang
Jian Song
Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
Entropy
age of information
cross-layer design
constrained Markov decision process
author_facet Yuchao Chen
Haoyue Tang
Jintao Wang
Jian Song
author_sort Yuchao Chen
title Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
title_short Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
title_full Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
title_fullStr Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
title_full_unstemmed Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State
title_sort optimizing age penalty in time-varying networks with markovian and error-prone channel state
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2021-01-01
description 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.
topic age of information
cross-layer design
constrained Markov decision process
url https://www.mdpi.com/1099-4300/23/1/91
work_keys_str_mv AT yuchaochen optimizingagepenaltyintimevaryingnetworkswithmarkoviananderrorpronechannelstate
AT haoyuetang optimizingagepenaltyintimevaryingnetworkswithmarkoviananderrorpronechannelstate
AT jintaowang optimizingagepenaltyintimevaryingnetworkswithmarkoviananderrorpronechannelstate
AT jiansong optimizingagepenaltyintimevaryingnetworkswithmarkoviananderrorpronechannelstate
_version_ 1724341659614838784

Similar Items