Structured Compressive Sensing-Based Channel Estimation for Time Frequency Training OFDM Systems Over Doubly Selective Channel

This letter proposes a novel doubly selective channel estimation scheme based on structured compressive sensing (SCS) for time-frequency training orthogonal frequency division multiplexing systems. By exploiting the proposed pilot pattern, an SCS model is built by utilizing the jointly sparse property of the coefficient vectors corresponding to the channel expansion bases. The doubly selective channel can be recovered through the proposed adaptive support-aware block orthogonal matching pursuit algorithm. Simulation results demonstrate that the proposed scheme has superior performance than conventional counterparts over doubly selective channel with lower computational complexity.