Optimal transmission for energy harvesting nodes under battery size and usage constraints

Jing Yang, Jingxian Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

In this paper, we study the optimal energy management policy of an energy harvesting transmitter by taking both battery degradation and finite battery constraints into consideration. We consider a scenario where the sensor is able to harvest energy from the ambient environment and use it to power its transmission. The harvested energy can be used for transmission immediately without entering the equipped battery, or charged into the battery and discharged later for transmission. When the battery is charged or discharged, a cost will be incurred to account for its impact on battery degradation. We impose a long-term average cost constraint on the battery, which is translated to the average number of charge/discharge operations per unit time. At the same time, we assume the capacity of the battery is finite, and the total amount of energy stored in the battery cannot exceed its capacity. Our objective is to develop an online energy management policy to maximize the long-term average throughput of the transmitter under both the battery usage constraint and finite battery constraint. We propose an energy-aware adaptive transmission policy, which is a modified version of the optimal policy for the infinite battery case. Our analysis indicates that the energy-aware adaptive transmission policy is asymptotically optimal when the battery size is sufficiently large. Simulation results corroborate the theoretical analysis.

Original languageEnglish (US)
Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages819-823
Number of pages5
ISBN (Electronic)9781509040964
DOIs
StatePublished - Aug 9 2017
Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
Duration: Jun 25 2017Jun 30 2017

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other2017 IEEE International Symposium on Information Theory, ISIT 2017
Country/TerritoryGermany
CityAachen
Period6/25/176/30/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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