Blind channel estimation in OFDM systems by relying on the Gaussian assumption of the input

T. Y. Al-Naffouri, A. A. Quadeer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In an OFDM system, the receiver requires an estimate of the channel to recover the transmitted data. Most channel estimation methods rely on some form of training which reduces the useful data rate. In this paper, we introduce an algorithm that blindly estimates the channel by maximizing the log likelihood ofthe channel given the output data. Finding the likelihood function of a linear system can be very difficult. However, in the OFDM case, central limit arguments can be used to argue that the time-domain input is Gaussian. This together with the Gaussian assumption on the noise makes the output data Gaussian. The output likelihood function can then be maximized to obtain the maximum likelihood (ML) estimate ofthe channel. Unfortunately, this optimization problem is not convex and thus finding the global maximum is challenging. In this paper, we propose two methods to find the global maximum of the ML objective function. One is the blind Genetic algorithm and the other is the semi-blind Steepest descent method. The performance ofthe proposed algorithms is demonstrated by computer simulations.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2009
Pages201-206
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event9th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2009 - Ajman, United Arab Emirates
Duration: Dec 14 2009Dec 16 2009

Other

Other9th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2009
CountryUnited Arab Emirates
CityAjman
Period12/14/0912/16/09

Keywords

  • Blind channel estimation
  • Gaussian assumption on data
  • Maximum likelihood estimation
  • Semi-blind channel estimation

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this