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Channel Estimation for OFDM Systems Using Superimposed Training

Daofeng Xu, Luxi Yang

Open publisher page 4 citations

Abstract

Superimposed training has raised lots of attentions due to its great spectral efficiency and its relatively fast channel estimation algorithms. In this paper, we present channel estimation methods for OFDM system using periodic superimposed training added in frequency domain. At the receiver, channel estimation is done both in time domain (pre-FFT) and frequency domain (post-FFT). In addition, we prove that the estimation is MMSE solution. Simulations show that those methods are effective especially when SNR is lower.

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What this paper is about

Superimposed training has raised lots of attentions due to its great spectral efficiency and its relatively fast channel estimation algorithms. In this paper, we present channel estimation methods for OFDM system using periodic superimposed training added in frequency domain. At the receiver, channel estimation is done both in time domain (pre-FFT) and frequency domain (post-FFT). In addition, we prove that the estimation is MMSE solution. Simulations show that those methods are effective especially when SNR is lower.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Superimposed training has raised lots of attentions due to its great spectral efficiency and its relatively fast channel estimation algorithms. In this paper, we present channel estimation methods for OFDM system using periodic superimposed training added in frequency domain. At the receiver, channel estimation is done both in time domain (pre-FFT) and frequency domain (post-FFT). In addition, we prove that the estimation is MMSE solution. Simulations show that those methods are effective especially when SNR is lower.

Key concepts: Orthogonal frequency-division multiplexing, Fast Fourier transform, Frequency domain, Channel (broadcasting), Computer science, Estimation, Time domain, Algorithm

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