Open Access

Intelligent Modified Channel and Frequency Offset Estimation Schemes in Future Generation OFDM-Based Packet Communication Systems

  • Jaemin Kwak1Email author,
  • Sungeon Cho2,
  • Kitaeg Lim3,
  • Pusik Park3,
  • Daekyo Shin3 and
  • Jongchan Choi3
EURASIP Journal on Wireless Communications and Networking20082008:735732

https://doi.org/10.1155/2008/735732

Received: 30 January 2008

Accepted: 5 June 2008

Published: 16 June 2008

Abstract

The channel estimation and frequency offset estimation scheme for future generation orthogonal frequency division multiplexing (OFDM-) based intelligent packet communication systems are proposed. In the channel estimation scheme, we use additional 8 short training symbols besides 2 long training symbols for intelligently improving estimation performance. In the proposed frequency offset estimation scheme, we allocate intelligently different powers to the short and long training symbols while maintaining average power of overall preamble sequence. The preamble structure considered is based on the preamble specified in standardization group of IEEE802.11a for wireless local area network (WLAN) and IEEE802.11p for intelligent transportation systems (ITSs). From the simulation results, it is shown that the proposed intelligent estimation schemes can achieve better mean squared error (MSE) performance for channel and frequency offset estimation error than the conventional scheme. The proposed schemes can be used in designing for enhancing the performance of OFDM-based future generation intelligent communication network systems.

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Authors’ Affiliations

(1)
Division of Marine Electronics and Communication Engineering, Mokpo National Maritime University
(2)
Division of Computer & Communications Engineering, Sunchon National University
(3)
SoC Research Center, Korea Electronics Technology Institute

Copyright

© Jaemin Kwak et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.