Shaoping Chen

Intercarrier interference suppression and channel estimation for OFDM systems in time-varying frequency-selective fading channels

In orthogonal frequency division multiplexing (OFDM) systems, channel variations within an OFDM block destroy orthogonality between subcarriers, resulting in intercarrier interference (ICl), which increases an error floor in proportional to normalized Doppler frequency. To mitigate the effects of channel variations, many schemes have been proposed, but they are computationally complex or at the price of sacrificing spectral efficiency. In this paper, we first provide an ICI analysis in both time- and frequency-domain while existing literatures analyze the ICI effects mainly in the frequency domain. Based on this analysis, we then propose a novel ICI suppression technique, which uses iterative ICI cancellation in time domain and has a computational complexity that is linear in the OFDM symbol length. Channel estimation in rapidly time-varying scenarios is critical for ICl suppression and coherent demodulation. We also propose a scheme for estimating the channel parameters varying within an OFDM block. Along with the channel estimation scheme, we investigate the issue of optimum pilot tone placement. Theoretical analysis and simulation results show that the proposed ICl suppression method and channel estimation scheme have low computational complexity and good performance for most Doppler frequency of practical importance.

Low complexity ICI cancellation for OFDM systems in doubly-selective fading channels

In orthogonal frequency division multiplexing (OFDM) systems, rapid variations of doubly-selective fading channels lead to a loss of subcarrier orthogonality, resulting in inter-carrier interference (ICI) and system performance degradation. Although some methods have been proposed to suppress ICI, they are computationally complex or at the price of sacrificing spectral efficiency. In this paper, we provide an ICI analysis in both time- and frequency-domains while existing literatures analyze the ICI effects mainly in frequency domain. Based on this analysis, we propose a novel ICI mitigation scheme whose complexity is linear in the OFDM symbol length. The method uses iterative ICI cancellation in the time domain. We provide theoretical analysis and simulation results to show that the proposed method can effectively mitigate ICI caused by channel variations with a low computation complexity and high spectral efficiency.

Zero-forcing equalization for OFDM systems over doubly-selective fading channels using frequency domain redundancy

Rapid variations of doubly-selective fading channels lead to the loss of orthogonality among subcarriers, resulting in inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. In this scenario, the one-tap frequency-domain equalizer is not applicable any more. Based on an ICI analysis in both time- and frequency-domain, we propose a novel zero-forcing equalizer (ZFE) for OFDM systems over doubly-selective channels using the existing frequency domain redundancy in many OFDM standards. To deal with the zero-forcing equalizers disadvantage of noise amplification, which causes system performance degradation when there exist deep nulls in the channels frequency response, we also provide the MMSE-based optimization of ZFE coefficients in the presence of channel noise. We show that ZFE matrix has a sparse structure, and thus a low computational complexity. Simulation results indicate that the proposed equalizer is capable of suppressing ICI effectively with a low complexity.

An eigenanalysis-based method for blind channel identification and equalisation

Subspace (SS) methods are effective approaches for blind channel identification, for they achieve a good performance with a relatively short data lengths and work well at low signal-to-noise ratio (SNR). However, they require accurate channel order estimation, which is difficult in a noisy environment. Although linear prediction (LP) methods can handle the problem of channel order overestimation, their performance degrades dramatically when SNR is low. In this letter, we proposed a blind channel identification and equalisation algorithm, based on the eigenanalysis of shifted correlation matrices of the received data and their associated properties. The algorithm is robust to channel order overestimation and not sensitive to noise as well. Furthermore, the algorithm does not require the computation of the correlation matrix pseudo-inverse, as with linear prediction algorithms, nor are the whole noise or signal eigen vectors necessary to achieve identification as with the subspace algorithm, so it is computationally efficient. Copyright

A BEM for Estimation of Time-varying Channels in OFDM

A Basis Expansion Model (BEM) for time-varying channels is proposed. It overcomes the effects of Gibbs phenomenon of the widely used Complex-Exponential BEM (CE-BEM) by using baseline tilting and therefore significantly reduces the modeling error. Theoretical analysis and simulation results show that the modeling error of the proposed BEM is obviously lower than those of the Complex-Exponential BEM (CE-BEM) and the General Complex-Exponential BEM (GCE-BEM).

A Low Complexity MMSE Equalizer for OFDM Systems over Time-Varying Channels

A low complexity minimum mean squared error (MMSE) equalizer for orthogonal frequency division multiplexing (OFDM) systems over time-varying channels is presented. It uses a small matrix of dominant partial channel information and recursive calculation of matrix inverse to significantly reduce the complexity. Theoretical analysis and simulations results are provided to validate its significant performance or complexity advantages over the previously published MMSE equalizers.