Two compressive sensing-based estimation schemes designed for rapidly time-varying channels in orthogonal frequency division multiplexing systems

The problem of estimating rapidly time-varying channels is considered to be one of the key challenges in high mobility orthogonal frequency division multiplexing (OFDM) systems. In such scenarios, fast time variation within OFDM symbol duration requires overloaded measurements for estimation. By exploiting the inherent sparsity of wireless channels, the authors cast the channel estimation as a compressive sensing (CS) problem to reduce the required pilot symbols. Different from the existing CS-based estimators which mainly focus on the diagonal matrix model and treat the inter-carrier interference as additive noise, the proposed methods are designed for the non-diagonal matrix, a more precise representation of fast fading channels. To handle this more complex channel model, an iterative estimation scheme is presented, which adopts the recently introduced modified-CS algorithm. In addition, a more simplified scheme is also designed by utilising a reasonable approximation of the system model. Compared to the former method, it has a reduced computational complexity with limited performance degradation. The simulation results demonstrate that the two proposed CS-based methods are robust to large Doppler spreading and have better performance than conventional CS-based estimators for fast time-varying channels in OFDM systems.