By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division ...By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.展开更多
基金supported by the National Science and Technology Major Project (2012ZX03001039-002)
文摘By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.
