Millimeter Wave Energy Harvesting

Talha Ahmed Khan, Ahmed Alkhateeb, Robert W. Heath

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions.

Original languageEnglish (US)
Article number7491259
Pages (from-to)6048-6062
Number of pages15
JournalIEEE Transactions on Wireless Communications
Issue number9
StatePublished - Sep 2016
Externally publishedYes


  • Millimeter wave
  • energy coverage
  • energy harvesting
  • simultaneous wireless information and energy transfer
  • stochastic geometry
  • wireless power transfer

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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