A compressive channel sensing method with optimal thresholding for OFDM systems under fast fading channels

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the high-speed movement between the transmitter and receiver would cause large Doppler shifts, which will degrade the channel estimation severely due to the channel power dispersion. Moreover, the channel structure may vary fast such that the channel structure-based estimation methods cannot work properly. Therefore, one key challenge is to design an efficient and reliable estimation method to adapt to the fast time-varying channels in high-speed movement scenarios. In this paper, a basis expansion model (BEM) based compressive channel sensing algorithm is proposed to estimate the channel state information (CSI) for OFDM systems under fast fading channels. In order to mitigate the channel power leakage, Discrete Prolate Spheroidal Sequences (DPSS) are employed as the basis of the channel model. By fully exploiting the sparse characteristics of the channel, an Orthogonal Matching Pursuit (OMP) algorithm with optimal thresholding is developed, which can flexibly support the varying channel structure without channel side information. Numerical simulation results verify the efficiency of the proposed method for the fast time-varying fading channels.