Due to the high mobility of communication, channel times can vary rapidly and system performance can be decreased. In this study, pseudo-random noise is used as the guard interval and the training sequence in the time domain in order to estimate the channel-based compressive sensing scheme. This scheme reduces the number of pilots in the frequency domain and improves spectrum efficiency. By adequately exploiting the sparse characteristics and temporal correlation of the wireless channel, a low complexity compressive channel estimation scheme is proposed. Firstly, the authors average the successive symbols of the channel impulse response in the coherence time to improve the accuracy of the coarse channel estimation. Secondly, a low complexity partial priori information CoSaMP (PPI-CoSaMP) algorithm is proposed to accurately estimate the channel state information. Finally, based on the precise time delay, the accurate gains are estimated based on the least-squares algorithm. The simulation results show that compared with the conventional algorithms, the number of observation points required by the PPI-CoSaMP algorithm is reduced by at least 25%. Moreover, the proposed scheme is more robust at larger multipath channel delays. The complexity of the proposed scheme is reduced by 51.21% compared with the conventional CoSaMP algorithm.

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Compressive channel estimation for universal filtered multi-carrier system in high-speed scenarios

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