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Compressive sensing based channel estimation for scattered pilot OFDM Systems over doubly-selective Rician channel

In this paper, a typical high-speed train (HST) communication system based on orthogonal frequency division multiplexing (OFDM) is considered and the wireless channel is estimated in utilizing the sparsity of the channel. For the complex-exponential basis expansion model (CE-BEM) based channel model, the time-frequency selectivity channel can be estimated more accurately which base the compressive sensing (CS) theory than conventional channel estimation methods. In this paper, the sparsity of Rician channel at each subcarrier in OFDM system is proved. And compressive sensing based channel estimation algorithm for scattered pilot OFDM Systems over doubly-selective Rician channel is proposed. Simulation results show that the proposed channel estimation algorithm is insensitivity to mobile speed and can achieve better channel estimation performance in terms of mean square error and bit error rate compared with the existing algorithms.