Energy efficiency of wireless cooperation

Aman Jain, Sanjeev R. Kulkarni, Sergio Verdu

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

1 Scopus citations

Abstract

We study the energy efficiency of wireless transmissions for a class of networks where a large number of proximal nodes co-operate over the wireless medium to reliably decode the message from a distant transmitter (the source). The objective is to minimize the sum total of energy expenditure (per bit) over all transmissions in the network. The wireless medium is assumed to be affected by Gaussian noise and symmetric fading with the channel state information known at the receivers but not at the transmitters. Furthermore, we assume the possibility of wideband communication and no delay constraints. In a network with k proximal nodes, where the links amongst the nodes are much better than the link between the source and the nodes, the lower bound on the energy per bit could reduce by as much as k-1 (in k) up to a certain point. This suggests a large potential benefit in co-operating with proximal nodes. We first propose and analyze an estimate-and-forward transmission scheme inspired by the Gaussian CEO problem but show that it does not have such an asymptotic behavior (in k) under most conditions. Next, we propose a simple, uncoded aggregate-and-forward scheme and show that it has an almost optimal asymptotic behavior.

Original languageEnglish (US)
Title of host publication54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages664-671
Number of pages8
ISBN (Electronic)9781509045495
DOIs
StatePublished - Feb 10 2017
Event54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 - Monticello, United States
Duration: Sep 27 2016Sep 30 2016

Publication series

Name54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

Other

Other54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
CountryUnited States
CityMonticello
Period9/27/169/30/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Optimization

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