A New Approach to Blind Channel Identification for OFDM Systems

Abstract

Orthogonal frequency division multiplexing (OFDM) is, with substantial progress in digital signal processing, becoming an important part of the telecommunications arena. The most appealing feature of OFDM is the simplicity of the receiver design due to the efficiency with which OFDM can cope with the effects of frequency-selective multipath channels. Here, we address the problem of channel estimation for OFDM systems. Exploiting receive antenna diversity, a second-order statistics-based (SOS) blind technique is proposed. Our method differs from the existing SOS-based techniques in that channel estimation is carried out using the frequency domain (i.e., post-FFT) signals whereas existing methods use the time domain signals (i.e., pre-FFT). In the proposed method, channel identifiability is guaranteed regardless of the channels zeros locations, so long as any roots common to all the diversity channels are on the unit circle. Only short data records are required to achieve good performance. For PSK transmission, this method enables channel estimation even from a single OFDM symbol at high SNR. Further, when only a small number of subcarriers can be used for channel estimation because of computational complexity, we determine the optimal set of subcarriers in terms of estimation accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2003
Accession Number
ADA419876

Entities

People

  • Mounir Ghogho

Organizations

  • University of Leeds

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Amplitude Modulation
  • Bandwidth
  • Channel Estimation
  • Computational Complexity
  • Cross Correlation
  • Demodulation
  • Frequency
  • Frequency Domain
  • Frequency Response
  • Identification
  • Modulation
  • Modulators
  • Order Statistics
  • Orthogonal Frequency Division Multiplexing
  • Statistics
  • Time Domain

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.