Blind Modulation Classification Algorithm for Adaptive OFDM Systems(Wireless Communication Technologies)

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概要

• 論文の詳細を見る

• Adaptive modulation is an efficient method to increase the spectral efficiency of OFDM based high-speed wireless data transmission systems in multipath channel. Blind modulation classification schemes play an important role in adaptive modulation systems, eliminating the need for transmitting modulation information, thereby increasing spectral efficiency. In this paper, a novel blind modulation classification algorithm is derived from the finite alphabet property of information symbols and the equivalent parallel model of OFDM systems. The performances of the proposed algorithm and M2M4P algorithm are tested and compared using Monte-Carlo simulations. It is found that, the novel algorithm yields performance better than that of M2M4P algorithm and with much less complexity.

• 社団法人電子情報通信学会の論文
• 2007-02-01

著者

• Huang Qi‐shan

  Univ. Electronic Sci. And Technol. Of China (uestc) Sichuan Province Chn

• Huang Qi-shan

  Uestc-ti Dsp Center University Of Electronic Science And Technology Of China (uestc)

• Peng Qi‐cong

  Univ. Electronic Sci. And Technol. Of China (uestc) Chengdu Chn

• PENG Qi-Cong

  UESTC-TI DSP Center, University of Electronic Science and Technology of China (UESTC)

• SHAO Huai-Zong

  UESTC-TI DSP Center, University of Electronic Science and Technology of China (UESTC)

• Peng Qi-cong

  Uestc-ti Dsp Center University Of Electronic Science And Technology Of China (uestc)

• Shao Huai‐zong

  Univ. Electronic Sci. And Technol. Of China (uestc) Chengdu Chn

• Shao Huai-zong

  Uestc-ti Dsp Center University Of Electronic Science And Technology Of China (uestc)

関連論文

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• Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches(Digital Signal Processing)
• Blind Modulation Classification Algorithm for Adaptive OFDM Systems(Wireless Communication Technologies)
• A Robust Sound Source Localization Approach for Microphone Array with Model Errors

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