A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

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Abd-Elmagid, Mohamed A.Virginia Tech, VA 24061 USA (författare)

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

Artikel/kapitelEngelska2020

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Institute of Electrical and Electronics Engineers (IEEE),2020
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LIBRIS-ID:oai:DiVA.org:liu-169252
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169252URI
https://doi.org/10.1109/TCOMM.2020.2991992DOI

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Språk:engelska
Sammanfattning på:engelska

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Funding Agencies|U.S. NSFNational Science Foundation (NSF) [CPS-1739642]
In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age of Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI. To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP) with finite state and action spaces. Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem even for reasonable-scale settings. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy. Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
Batteries; Energy harvesting; Reinforcement learning; System analysis and design; Real-time systems; Wireless communication; Age of Information; RF energy harvesting; Markov Decision Process; Reinforcement learning

Biuppslag (personer, institutioner, konferenser, titlar ...)

Dhillon, Harpreet S.Virginia Tech, VA 24061 USA (författare)
Pappas, NikolaosLinköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten(Swepub:liu)nikpa02 (författare)
Virginia Tech, VA 24061 USAKommunikations- och transportsystem (creator_code:org_t)

Sammanhörande titlar

Ingår i:IEEE Transactions on Communications: Institute of Electrical and Electronics Engineers (IEEE)68:8, s. 4747-47600090-67781558-0857

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Av författaren/redakt...: Abd-Elmagid, Moh ...; Dhillon, Harpree ...; Pappas, Nikolaos

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Signalbehandling

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