Iterative estimation of doubly selective channels with ICI suppression for OFDM using KL-BEM

来源 :The Journal of China Universities of Posts and Telecommunica | 被引量 : 0次 | 上传用户:zhanglangsdkd
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Channel estimation for orthogonal frequency division multiplexing (OFDM) systems operating over doubly selective channels is complicated by the existence of intercarrier interference (ICI).This paper extends the idea of estimating time-variant channel impulse response (CIR) via time-averaged CIR proposed by Hijazi,and proposes a low-complexity channel estimation scheme with ICI suppression based on Karhunen-Loeve basis expansion model (KL-BEM),which is in a more general form.In the proposed scheme,the KL-BEM coefficients are obtained through their numerical relationship with the time-averaged CIR.The estimation performance is improved by adopting an expectation-maximization (EM)-based iterative structure combined with a low-complexity ICI equalizer and canceller.Theoretical analysis and simulation results show that the proposed scheme achieves superior performance over the original one with lower computational complexity. Channel estimation for orthogonal frequency division multiplexing (OFDM) systems operating over doubly selective channels is complicated by the existence of intercarrier interference (ICI). This paper extends the idea of ​​estimating time-variant channel impulse response (CIR) via time-averaged CIR proposed by Hijazi, and proposes a low-complexity channel estimation scheme with ICI suppression based on Karhunen-Loeve basis expansion model (KL-BEM), which is in a more general form. In the proposed scheme, the KL-BEM coefficients are obtained through their numerical relationship with the time-averaged CIR. estimation performance is improved by adopting an expectation-maximization (EM) -based iterative structure combined with a low-complexity ICI equalizer and canceller.Theoretical analysis and simulation results show that the proposed scheme achieves superior performance over the original one with lower computational complexity.
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