Network beamforming based on second order statistics of the channel state information

Veria Havary-Nassab, Shahram Shahbazpanahi, Ali Grami, Zhi Quan Luo

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

13 Scopus citations

Abstract

The problem of distributed beamforming is considered for a network which consists of a transmitter, a receiver, and r relay nodes. Assuming that the second order statistics of the channel coefficients are available, we design a distributed beamforming technique via maximization of the receiver signal-to-noise ratio (SNR) subject to individual relay power constraints. We show that using semi-definite relaxation, this SNR maximization can be turned into a convex feasibility semi-definite programming problem, and therefore, it can be efficiently solved using interior point methods. We also obtain a performance bound for the semi-definite relaxation and show that the semi-definite relaxation approach provides a c-approximation to the (nonconvex) SNR maximization problem, where c = O((log r)-1) and r is the number of relays.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages2605-2608
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Convex feasibility problem
  • Distributed beamforming
  • Distributed signal processing
  • Relay networks
  • Semi-definite programming

Fingerprint Dive into the research topics of 'Network beamforming based on second order statistics of the channel state information'. Together they form a unique fingerprint.

Cite this